From f36449ae6811ae590a52a7680c92da72d5536a82 Mon Sep 17 00:00:00 2001 From: Devin Ha Date: Mon, 6 Oct 2025 14:12:02 +0200 Subject: [PATCH 01/13] [SPARKNLP-1288] AutoGGUF close model --- .../embeddings/auto_gguf_embeddings.py | 5 + .../annotator/seq2seq/auto_gguf_model.py | 5 + .../annotator/seq2seq/auto_gguf_reranker.py | 5 + .../seq2seq/auto_gguf_vision_model.py | 5 + .../embeddings/auto_gguf_embeddings_test.py | 47 ++++++-- .../annotator/seq2seq/auto_gguf_model_test.py | 42 +++++++- .../seq2seq/auto_gguf_reranker_test.py | 37 ++++++- .../seq2seq/auto_gguf_vision_model_test.py | 100 +++++++++++------- python/test/util.py | 19 +++- .../johnsnowlabs/ml/gguf/GGUFWrapper.scala | 56 ++++++++-- .../ml/gguf/GGUFWrapperMultiModal.scala | 54 +++++++++- .../annotators/seq2seq/AutoGGUFModel.scala | 4 + .../annotators/seq2seq/AutoGGUFReranker.scala | 4 + .../seq2seq/AutoGGUFVisionModel.scala | 4 + .../nlp/embeddings/AutoGGUFEmbeddings.scala | 4 + .../seq2seq/AutoGGUFModelTest.scala | 17 +++ .../seq2seq/AutoGGUFRerankerTest.scala | 20 +++- .../seq2seq/AutoGGUFVisionModelTestSpec.scala | 16 +++ .../AutoGGUFEmbeddingsTestSpec.scala | 16 +++ .../com/johnsnowlabs/util/TestUtils.scala | 23 ++++ 20 files changed, 416 insertions(+), 67 deletions(-) diff --git a/python/sparknlp/annotator/embeddings/auto_gguf_embeddings.py b/python/sparknlp/annotator/embeddings/auto_gguf_embeddings.py index cb31b0b1f4c1e5..ef0d8aabd78601 100755 --- a/python/sparknlp/annotator/embeddings/auto_gguf_embeddings.py +++ b/python/sparknlp/annotator/embeddings/auto_gguf_embeddings.py @@ -532,3 +532,8 @@ def pretrained(name="Qwen3_Embedding_0.6B_Q8_0_gguf", lang="en", remote_loc=None return ResourceDownloader.downloadModel( AutoGGUFEmbeddings, name, lang, remote_loc ) + + def close(self): + """Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + """ + self._java_obj.close() diff --git a/python/sparknlp/annotator/seq2seq/auto_gguf_model.py b/python/sparknlp/annotator/seq2seq/auto_gguf_model.py index 760cfbcfa5d361..eab1fc1901cdf2 100755 --- a/python/sparknlp/annotator/seq2seq/auto_gguf_model.py +++ b/python/sparknlp/annotator/seq2seq/auto_gguf_model.py @@ -300,3 +300,8 @@ def pretrained(name="Phi_4_mini_instruct_Q4_K_M_gguf", lang="en", remote_loc=Non """ from sparknlp.pretrained import ResourceDownloader return ResourceDownloader.downloadModel(AutoGGUFModel, name, lang, remote_loc) + + def close(self): + """Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + """ + self._java_obj.close() diff --git a/python/sparknlp/annotator/seq2seq/auto_gguf_reranker.py b/python/sparknlp/annotator/seq2seq/auto_gguf_reranker.py index 5d4c082dd26f89..075e45c67f5493 100755 --- a/python/sparknlp/annotator/seq2seq/auto_gguf_reranker.py +++ b/python/sparknlp/annotator/seq2seq/auto_gguf_reranker.py @@ -327,3 +327,8 @@ def pretrained(name="bge_reranker_v2_m3_Q4_K_M", lang="en", remote_loc=None): """ from sparknlp.pretrained import ResourceDownloader return ResourceDownloader.downloadModel(AutoGGUFReranker, name, lang, remote_loc) + + def close(self): + """Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + """ + self._java_obj.close() diff --git a/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py b/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py index 4408e054f90a4f..9308e1bc34a183 100755 --- a/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py +++ b/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py @@ -329,3 +329,8 @@ def pretrained(name="Qwen2.5_VL_3B_Instruct_Q4_K_M_gguf", lang="en", remote_loc= """ from sparknlp.pretrained import ResourceDownloader return ResourceDownloader.downloadModel(AutoGGUFVisionModel, name, lang, remote_loc) + + def close(self): + """Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + """ + self._java_obj.close() diff --git a/python/test/annotator/embeddings/auto_gguf_embeddings_test.py b/python/test/annotator/embeddings/auto_gguf_embeddings_test.py index 228feb652f63e9..25b60c9834aa1d 100644 --- a/python/test/annotator/embeddings/auto_gguf_embeddings_test.py +++ b/python/test/annotator/embeddings/auto_gguf_embeddings_test.py @@ -17,7 +17,7 @@ from sparknlp.annotator import * from sparknlp.base import * -from test.util import SparkContextForTest +from test.util import * @pytest.mark.slow @@ -58,7 +58,7 @@ def runTest(self): embds = row["embeddings"][0] assert embds is not None assert ( - sum(embds) > 0 + sum(embds) > 0 ), "Embeddings should not be zero. Was there an error on llama.cpp side?" @@ -100,7 +100,7 @@ def runTest(self): embds = row["embeddings"][0] assert embds is not None assert ( - sum(embds) > 0 + sum(embds) > 0 ), "Embeddings should not be zero. Was there an error on llama.cpp side?" @@ -114,7 +114,7 @@ def setUp(self): self.long_data_copies = 16 self.long_text = "All work and no play makes Jack a dull boy" * 100 self.long_data = self.spark.createDataFrame( - [self.long_text] * self.long_data_copies, schema="string" + [self.long_text] * self.long_data_copies, schema="string" ).toDF("text").repartition(4) def runTest(self): @@ -144,7 +144,7 @@ def setUp(self): self.long_data_copies = 16 self.long_text = "All work and no play makes Jack a dull boy" * 100 self.long_data = self.spark.createDataFrame( - [self.long_text] * self.long_data_copies, schema="string" + [self.long_text] * self.long_data_copies, schema="string" ).toDF("text").repartition(4) def runTest(self): @@ -165,7 +165,7 @@ def runTest(self): embds = row[0][0]["embeddings"] assert embds is not None assert ( - sum(embds) > 0 + sum(embds) > 0 ), "Embeddings should not be zero. Was there an error on llama.cpp side?" @@ -185,6 +185,37 @@ def runTest(self): ) model_writer.save(model_path) AutoGGUFEmbeddings.load(model_path) - + model_path = "file:///tmp/autoggufembeddings_spark_nlp" - AutoGGUFEmbeddings.load(model_path) \ No newline at end of file + AutoGGUFEmbeddings.load(model_path) + + +@pytest.mark.slow +class AutoGGUFEmbeddingsCloseTest(unittest.TestCase): + def setUp(self): + self.spark = SparkSessionForTest.spark + + self.data = self.spark.createDataFrame( + [ + ["The moons of Jupiter are "], + ] + ).toDF("text") + + self.document_assembler = ( + DocumentAssembler().setInputCol("text").setOutputCol("document") + ) + + def runTest(self): + model = ( + AutoGGUFEmbeddings.pretrained() + .setInputCols("document") + .setOutputCol("embeddings") + ) + + pipeline = Pipeline().setStages([self.document_assembler, model]) + pipeline.fit(self.data).transform(self.data).show() + + ramChange = measureRAMChange(lambda: model.close()) + + print(f"Freed RAM after closing the model: {ramChange} MB") + assert (ramChange < -100, "Freed RAM should be greater than 100 MB") diff --git a/python/test/annotator/seq2seq/auto_gguf_model_test.py b/python/test/annotator/seq2seq/auto_gguf_model_test.py index 8b47dc76bb9844..bac3e9d373ffe0 100644 --- a/python/test/annotator/seq2seq/auto_gguf_model_test.py +++ b/python/test/annotator/seq2seq/auto_gguf_model_test.py @@ -17,7 +17,7 @@ from sparknlp.annotator import * from sparknlp.base import * -from test.util import SparkContextForTest +from test.util import * @pytest.mark.slow @@ -232,9 +232,7 @@ def runTest(self): ) for row in collected: annotation = row[0][0] - self.assertEqual( - annotation["result"], "", "Completions should be empty" - ) + self.assertEqual(annotation["result"], "", "Completions should be empty") self.assertIn( "llamacpp_exception", annotation["metadata"], @@ -258,6 +256,40 @@ def runTest(self): ) model_writer.save(model_path) AutoGGUFModel.load(model_path) - + model_path = "file:///tmp/autogguf_spark_nlp" AutoGGUFModel.load(model_path) + + +@pytest.mark.slow +class AutoGGUFModelCloseTest(unittest.TestCase): + def setUp(self): + self.spark = SparkSessionForTest.spark + + self.data = self.spark.createDataFrame( + [ + ["The moons of Jupiter are "], + ["Earth is "], + ["The moon is "], + ["The sun is "], + ] + ).toDF("text") + + self.document_assembler = ( + DocumentAssembler().setInputCol("text").setOutputCol("document") + ) + + def runTest(self): + model = ( + AutoGGUFModel.pretrained() + .setInputCols("document") + .setOutputCol("completions") + ) + + pipeline = Pipeline().setStages([self.document_assembler, model]) + pipeline.fit(self.data).transform(self.data).show() + + ramChange = measureRAMChange(lambda: model.close()) + + print(f"Freed RAM after closing the model: {ramChange} MB") + assert (ramChange < -100, "Freed RAM should be greater than 100 MB") diff --git a/python/test/annotator/seq2seq/auto_gguf_reranker_test.py b/python/test/annotator/seq2seq/auto_gguf_reranker_test.py index 996221dd115138..601c904b910199 100644 --- a/python/test/annotator/seq2seq/auto_gguf_reranker_test.py +++ b/python/test/annotator/seq2seq/auto_gguf_reranker_test.py @@ -17,7 +17,7 @@ from sparknlp.annotator import * from sparknlp.base import * -from test.util import SparkContextForTest +from test.util import * @pytest.mark.slow @@ -419,3 +419,38 @@ def runTest(self): print("Combined filters test completed successfully") results.select("ranked_documents").show(truncate=False) + + +@pytest.mark.slow +class AutoGGUFRerankerCloseTest(unittest.TestCase): + def setUp(self): + self.spark = SparkSessionForTest.spark + + self.data = ( + self.spark.createDataFrame( + [ + ["The moons of Jupiter are "], + ] + ) + .toDF("text") + ) + + self.document_assembler = ( + DocumentAssembler().setInputCol("text").setOutputCol("document") + ) + + def runTest(self): + model = ( + AutoGGUFReranker.pretrained() + .setInputCols("document") + .setOutputCol("reranked_documents") + .setBatchSize(4) + .setQuery("A query.") + ) + pipeline = Pipeline().setStages([self.document_assembler, model]) + pipeline.fit(self.data).transform(self.data).show() + + ramChange = measureRAMChange(lambda: model.close()) + + print(f"Freed RAM after closing the model: {ramChange} MB") + assert (ramChange < -100, "Freed RAM should be greater than 100 MB") diff --git a/python/test/annotator/seq2seq/auto_gguf_vision_model_test.py b/python/test/annotator/seq2seq/auto_gguf_vision_model_test.py index 2799dc2a54b60a..12113945e75e36 100644 --- a/python/test/annotator/seq2seq/auto_gguf_vision_model_test.py +++ b/python/test/annotator/seq2seq/auto_gguf_vision_model_test.py @@ -18,7 +18,43 @@ from sparknlp.annotator import * from sparknlp.base import * -from test.util import SparkSessionForTest +from test.util import SparkSessionForTest, measureRAMChange + + +def setup_context(spark): + documentAssembler = ( + DocumentAssembler().setInputCol("caption").setOutputCol("caption_document") + ) + imageAssembler = ( + ImageAssembler().setInputCol("image").setOutputCol("image_assembler") + ) + imagesPath = "../src/test/resources/image/" + data = ImageAssembler.loadImagesAsBytes(spark, imagesPath).withColumn( + "caption", + lit( + "Describe in a short and easy to understand sentence what you see in the image." + ), + ) # Add a caption to each image. + nPredict = 40 + model: AutoGGUFVisionModel = ( + AutoGGUFVisionModel.pretrained() + .setInputCols(["caption_document", "image_assembler"]) + .setOutputCol("completions") + .setBatchSize(2) + .setNGpuLayers(99) + .setNCtx(4096) + .setMinKeep(0) + .setMinP(0.05) + .setNPredict(nPredict) + .setPenalizeNl(True) + .setRepeatPenalty(1.18) + .setTemperature(0.05) + .setTopK(40) + .setTopP(0.95) + ) + pipeline = Pipeline().setStages([documentAssembler, imageAssembler, model]) + + return pipeline, data, model @pytest.mark.slow @@ -27,40 +63,7 @@ def setUp(self): self.spark = SparkSessionForTest.spark def runTest(self): - documentAssembler = ( - DocumentAssembler().setInputCol("caption").setOutputCol("caption_document") - ) - imageAssembler = ( - ImageAssembler().setInputCol("image").setOutputCol("image_assembler") - ) - imagesPath = "../src/test/resources/image/" - data = ImageAssembler.loadImagesAsBytes(self.spark, imagesPath).withColumn( - "caption", - lit( - "Describe in a short and easy to understand sentence what you see in the image." - ), - ) # Add a caption to each image. - nPredict = 40 - model: AutoGGUFVisionModel = ( - AutoGGUFVisionModel.pretrained() - .setInputCols(["caption_document", "image_assembler"]) - .setOutputCol("completions") - .setBatchSize(2) - .setNGpuLayers(99) - .setNCtx(4096) - .setMinKeep(0) - .setMinP(0.05) - .setNPredict(nPredict) - .setPenalizeNl(True) - .setRepeatPenalty(1.18) - .setTemperature(0.05) - .setTopK(40) - .setTopP(0.95) - ) - pipeline = Pipeline().setStages([documentAssembler, imageAssembler, model]) - # pipeline.fit(data).transform(data).selectExpr( - # "reverse(split(image.origin, '/'))[0] as image_name", "completions.result" - # ).show(truncate=False) + pipeline, data, _ = setup_context(self.spark) results = pipeline.fit(data).transform(data).collect() @@ -83,7 +86,7 @@ def runTest(self): print(f"Image: {image_name}, Completion: {completion}") assert ( - expectedWords[image_name] in completion.lower() + expectedWords[image_name] in completion.lower() ), f"Expected '{expectedWords[image_name]}' in '{completion.lower()}'" @@ -103,6 +106,27 @@ def runTest(self): ) model_writer.save(model_path) AutoGGUFVisionModel.load(model_path) - + model_path = "file:///tmp/autoggufvisionmodel_spark_nlp" - AutoGGUFVisionModel.load(model_path) \ No newline at end of file + AutoGGUFVisionModel.load(model_path) + + +@pytest.mark.slow +class AutoGGUFVisionModelCloseTest(unittest.TestCase): + def setUp(self): + self.spark = SparkSessionForTest.spark + + def runTest(self): + pipeline, data, model = setup_context(self.spark) + + ( + pipeline.fit(data) + .transform(data.limit(1)) + .select("completions.result") + .show(truncate=False) + ) + + ramChange = measureRAMChange(lambda: model.close()) + + print(f"Freed RAM after closing the model: {ramChange} MB") + assert (ramChange < -100, "Freed RAM should be greater than 100 MB") diff --git a/python/test/util.py b/python/test/util.py index 7d8f4b80fa59e9..42a13f19180212 100644 --- a/python/test/util.py +++ b/python/test/util.py @@ -12,9 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -from pyspark.sql import SparkSession import os +from pyspark.sql import SparkSession +import time + class SparkSessionForTest: jars_path = "lib/sparknlp.jar" @@ -38,4 +40,17 @@ class SparkContextForTest: .limit(100) data.cache() data.count() - \ No newline at end of file + + +def getFreeRAM(): + """Returns the amount of free system RAM in Megabytes (MB).""" + import psutil # Not available no github runners + return psutil.virtual_memory().free / (1024 * 1024) + + +def measureRAMChange(func, *args, **kwargs): + ramBefore = getFreeRAM() + func(*args, **kwargs) + time.sleep(0.5) + ramAfter = getFreeRAM() + return ramAfter - ramBefore diff --git a/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapper.scala b/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapper.scala index 25ef9fd2f50a6d..2b41614c03e0a9 100644 --- a/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapper.scala +++ b/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapper.scala @@ -17,16 +17,14 @@ package com.johnsnowlabs.ml.gguf import com.johnsnowlabs.nlp.util.io.ResourceHelper import de.kherud.llama.{LlamaModel, ModelParameters} -import org.apache.commons.io.FileUtils import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.spark.SparkFiles +import org.apache.spark.broadcast.Broadcast import org.apache.spark.sql.SparkSession import org.slf4j.{Logger, LoggerFactory} import java.io.File -import java.net.URI -import java.nio.file.{Files, Paths} -import java.util.UUID +import java.nio.file.Paths class GGUFWrapper(var modelFileName: String, var modelFolder: String) extends Serializable { @@ -63,9 +61,16 @@ class GGUFWrapper(var modelFileName: String, var modelFolder: String) extends Se } // Destructor to free the model when this object is garbage collected - override def finalize(): Unit = { - if (llamaModel != null) { - llamaModel.close() + override def finalize(): Unit = close() + + /** Closes the underlying LlamaModel and frees resources. */ + def close(): Unit = { + this.synchronized { + if (llamaModel != null) { + println("Closing llama.cpp model.") + llamaModel.close() + llamaModel = null + } } } @@ -134,4 +139,41 @@ object GGUFWrapper { val modelFile = findGGUFModelInFolder(localFolder) read(spark, modelFile) } + + /** Closes the broadcasted GGUFWrapper model on all Spark workers and the driver, freeing up + * resources. + * + * We use a foreachPartition on a dummy RDD to ensure that the close method is called on each + * executor. + * + * @param broadcastedModel + * An optional Broadcast[GGUFWrapper] instance to be closed. If None, no action is taken. + */ + def closeBroadcastModel(broadcastedModel: Option[Broadcast[GGUFWrapper]]): Unit = { + def closeOnWorkers(): Unit = { + val spark = SparkSession.getActiveSession.get + // Get the number of executors to ensure we run a task on each one. + val numExecutors = spark.sparkContext.getExecutorMemoryStatus.size + + // Create a dummy RDD with one partition per executor + val dummyRdd = spark.sparkContext.parallelize(1 to numExecutors, numExecutors) + + // Run a job whose only purpose is to trigger the shutdown method on each worker + dummyRdd.foreachPartition { _ => + broadcastedModel match { + case Some(broadcastModel) => + broadcastModel.value.close() + case None => // No model to close + } + } + } + + closeOnWorkers() + broadcastedModel match { + case Some(broadcastModel) => + broadcastModel.value.close() // Close the model on the driver as well + broadcastModel.unpersist() + case None => // No model to close + } + } } diff --git a/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapperMultiModal.scala b/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapperMultiModal.scala index 1e5a4aedfca8a2..4e792139ee18cd 100644 --- a/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapperMultiModal.scala +++ b/src/main/scala/com/johnsnowlabs/ml/gguf/GGUFWrapperMultiModal.scala @@ -20,10 +20,11 @@ import com.johnsnowlabs.nlp.util.io.ResourceHelper import de.kherud.llama.{LlamaModel, ModelParameters} import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.spark.SparkFiles +import org.apache.spark.broadcast.Broadcast import org.apache.spark.sql.SparkSession import java.io.File -import java.nio.file.{Files, Paths} +import java.nio.file.Paths class GGUFWrapperMultiModal(var modelFileName: String, var mmprojFileName: String) extends Serializable { @@ -66,12 +67,18 @@ class GGUFWrapperMultiModal(var modelFileName: String, var mmprojFileName: Strin } // Destructor to free the model when this object is garbage collected - override def finalize(): Unit = { - if (llamaModel != null) { - llamaModel.close() + override def finalize(): Unit = close() + + /** Closes the underlying LlamaModel and frees resources. */ + def close(): Unit = { + this.synchronized { + if (llamaModel != null) { + println("Closing llama.cpp model.") + llamaModel.close() + llamaModel = null + } } } - } /** Companion object */ @@ -147,4 +154,41 @@ object GGUFWrapperMultiModal { val (ggufMainPath, ggufMmprojPath) = findGGUFModelsInFolder(localFolder) read(spark, ggufMainPath, ggufMmprojPath) } + + /** Closes the broadcasted GGUFWrapper model on all Spark workers and the driver, freeing up + * resources. + * + * We use a foreachPartition on a dummy RDD to ensure that the close method is called on each + * executor. + * + * @param broadcastedModel + * An optional Broadcast[GGUFWrapper] instance to be closed. If None, no action is taken. + */ + def closeBroadcastModel(broadcastedModel: Option[Broadcast[GGUFWrapperMultiModal]]): Unit = { + def closeOnWorkers(): Unit = { + val spark = SparkSession.getActiveSession.get + // Get the number of executors to ensure we run a task on each one. + val numExecutors = spark.sparkContext.getExecutorMemoryStatus.size + + // Create a dummy RDD with one partition per executor + val dummyRdd = spark.sparkContext.parallelize(1 to numExecutors, numExecutors) + + // Run a job whose only purpose is to trigger the shutdown method on each worker + dummyRdd.foreachPartition { _ => + broadcastedModel match { + case Some(broadcastModel) => + broadcastModel.value.close() + case None => // No model to close + } + } + } + + closeOnWorkers() + broadcastedModel match { + case Some(broadcastModel) => + broadcastModel.value.close() // Close the model on the driver as well + broadcastModel.unpersist() + case None => // No model to close + } + } } diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala index 62001f5acedf0d..9132cacc9aa400 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala @@ -147,6 +147,10 @@ class AutoGGUFModel(override val uid: String) this } + /** Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + */ + def close(): Unit = GGUFWrapper.closeBroadcastModel(_model) + private[johnsnowlabs] def setEngine(engineName: String): this.type = set(engine, engineName) setDefault( diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFReranker.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFReranker.scala index 457d5e81d32737..c22cb099fffb23 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFReranker.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFReranker.scala @@ -155,6 +155,10 @@ class AutoGGUFReranker(override val uid: String) this } + /** Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + */ + def close(): Unit = GGUFWrapper.closeBroadcastModel(_model) + val query = new Param[String]( this, "query", diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala index ec2e9989c5b3b6..34152ecb9a9764 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala @@ -181,6 +181,10 @@ class AutoGGUFVisionModel(override val uid: String) this } + /** Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + */ + def close(): Unit = GGUFWrapperMultiModal.closeBroadcastModel(_model) + private[johnsnowlabs] def setEngine(engineName: String): this.type = set(engine, engineName) /** Sets the number of parallel processes for decoding. This is an alias for `setBatchSize`. diff --git a/src/main/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddings.scala b/src/main/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddings.scala index 093f2f338bb440..76f2ffa864edde 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddings.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddings.scala @@ -138,6 +138,10 @@ class AutoGGUFEmbeddings(override val uid: String) this } + /** Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. + */ + def close(): Unit = GGUFWrapper.closeBroadcastModel(_model) + private[johnsnowlabs] def setEngine(engineName: String): this.type = set(engine, engineName) /** Set the pooling type for embeddings, use model default if unspecified diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala index 1690411a2abee5..70c0d2f3f93f3b 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala @@ -4,6 +4,7 @@ import com.johnsnowlabs.nlp.Annotation import com.johnsnowlabs.nlp.base.DocumentAssembler import com.johnsnowlabs.nlp.util.io.ResourceHelper import com.johnsnowlabs.tags.SlowTest +import com.johnsnowlabs.util.TestUtils.measureRAMChange import org.apache.spark.ml.Pipeline import org.apache.spark.sql.{DataFrame, Dataset, Row} import org.scalatest.flatspec.AnyFlatSpec @@ -235,6 +236,22 @@ class AutoGGUFModelTest extends AnyFlatSpec { AutoGGUFModel.load(savePath) } + // This test requires cpu + it should "be closeable" taggedAs SlowTest in { + val model = AutoGGUFModel + .pretrained() + .setInputCols("document") + .setOutputCol("completions") + + val data = Seq("Hello, I am a").toDF("text") + val pipeline = new Pipeline().setStages(Array(documentAssembler, model)) + pipeline.fit(data).transform(data).show() + + val ramChange = measureRAMChange { model.close() } + println("Freed RAM after closing the model: " + ramChange + " MB") + assert(ramChange < -100, "Freed RAM should be greater than 100 MB") + } + // it should "benchmark" taggedAs SlowTest in { // val model = AutoGGUFModel // .loadSavedModel("models/gemma-3-4b-it-qat-Q4_K_M.gguf", ResourceHelper.spark) diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFRerankerTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFRerankerTest.scala index a1bef38ed9767a..e749ea1797d65f 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFRerankerTest.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFRerankerTest.scala @@ -5,6 +5,7 @@ import com.johnsnowlabs.nlp.base.DocumentAssembler import com.johnsnowlabs.nlp.finisher.GGUFRankingFinisher import com.johnsnowlabs.nlp.util.io.ResourceHelper import com.johnsnowlabs.tags.SlowTest +import com.johnsnowlabs.util.TestUtils.measureRAMChange import org.apache.spark.ml.Pipeline import org.apache.spark.sql.{DataFrame, Dataset, Row} import org.scalatest.flatspec.AnyFlatSpec @@ -22,7 +23,7 @@ class AutoGGUFRerankerTest extends AnyFlatSpec { lazy val query: String = "A man is eating pasta." lazy val modelPath = "/tmp/bge_reranker_v2_m3_Q4_K_M.gguf" lazy val model: AutoGGUFReranker = AutoGGUFReranker - .loadSavedModel(modelPath, ResourceHelper.spark) + .pretrained() .setInputCols("document") .setOutputCol("reranked_documents") .setBatchSize(4) @@ -140,4 +141,21 @@ class AutoGGUFRerankerTest extends AnyFlatSpec { // testing only, should be able to load AutoGGUFReranker.pretrained("Nomic_Embed_Text_v1.5.Q8_0.gguf") } + + // This test requires cpu + it should "be closeable" taggedAs SlowTest ignore { + // TODO: This needs investigation on llama.cpp side + val model: AutoGGUFReranker = AutoGGUFReranker + .pretrained() + .setInputCols("document") + .setOutputCol("reranked_documents") + .setBatchSize(4) + .setQuery(query) + + val pipeline = new Pipeline().setStages(Array(documentAssembler, model)) + pipeline.fit(data).transform(data).show() + + val ramChange = measureRAMChange { model.close() } + assert(ramChange < -50, "Freed RAM should be greater than 100 MB") + } } diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala index 24d0146cde9a57..8683731b17253f 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala @@ -4,6 +4,7 @@ import com.johnsnowlabs.nlp.base.DocumentAssembler import com.johnsnowlabs.nlp.util.io.ResourceHelper import com.johnsnowlabs.nlp.{Annotation, AnnotationImage, ImageAssembler} import com.johnsnowlabs.tags.SlowTest +import com.johnsnowlabs.util.TestUtils.measureRAMChange import org.apache.spark.ml.Pipeline import org.apache.spark.sql.functions.lit import org.apache.spark.sql.{DataFrame, Row} @@ -140,4 +141,19 @@ class AutoGGUFVisionModelTestSpec extends AnyFlatSpec { it should "load models with deprecated parameters" taggedAs SlowTest in { AutoGGUFVisionModel.pretrained("llava_v1.5_7b_Q4_0_gguf") } + + // This test requires cpu + it should "be closeable" taggedAs SlowTest in { + lazy val model: AutoGGUFVisionModel = AutoGGUFVisionModel + .pretrained() + .setInputCols("caption_document", "image_assembler") + .setOutputCol("completions") + .setNPredict(5) + + pipeline.fit(data).transform(data.limit(1)).show() + + val ramChange = measureRAMChange { model.close() } + println("Freed RAM after closing the model: " + ramChange + " MB") + assert(ramChange < -100, "Freed RAM should be greater than 100 MB") + } } diff --git a/src/test/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddingsTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddingsTestSpec.scala index 9331fb23ba9bc6..272d1c3f0d1273 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddingsTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddingsTestSpec.scala @@ -4,6 +4,7 @@ import com.johnsnowlabs.nlp.Annotation import com.johnsnowlabs.nlp.base.DocumentAssembler import com.johnsnowlabs.nlp.util.io.ResourceHelper import com.johnsnowlabs.tags.SlowTest +import com.johnsnowlabs.util.TestUtils.measureRAMChange import org.apache.spark.ml.Pipeline import org.apache.spark.sql.{Dataset, Row} import org.scalatest.flatspec.AnyFlatSpec @@ -141,4 +142,19 @@ class AutoGGUFEmbeddingsTestSpec extends AnyFlatSpec { it should "load models with deprecated parameters" taggedAs SlowTest in { AutoGGUFEmbeddings.pretrained("Nomic_Embed_Text_v1.5.Q8_0.gguf") } + + // This test requires cpu + it should "be closeable" taggedAs SlowTest in { + val model = AutoGGUFEmbeddings + .pretrained() + .setInputCols("document") + .setOutputCol("embeddings") + + val data = Seq("Hello, I am a").toDF("text") + pipeline(model).fit(data).transform(data).show() + + val ramChange = measureRAMChange { model.close() } + println("Freed RAM after closing the model: " + ramChange + " MB") + assert(ramChange < -100, "Freed RAM should be greater than 100 MB") + } } diff --git a/src/test/scala/com/johnsnowlabs/util/TestUtils.scala b/src/test/scala/com/johnsnowlabs/util/TestUtils.scala index 65772f3d401cdb..f3990cabf00b2d 100644 --- a/src/test/scala/com/johnsnowlabs/util/TestUtils.scala +++ b/src/test/scala/com/johnsnowlabs/util/TestUtils.scala @@ -1,8 +1,11 @@ package com.johnsnowlabs.util import com.johnsnowlabs.nlp.util.io.ResourceHelper +import com.sun.management.OperatingSystemMXBean import org.scalactic.{Equality, TolerantNumerics} +import java.lang.Thread.sleep +import java.lang.management.ManagementFactory import scala.io.Source private[johnsnowlabs] object TestUtils { @@ -57,4 +60,24 @@ private[johnsnowlabs] object TestUtils { implicit val tolerantFloatEq: Equality[Float] = TolerantNumerics.tolerantFloatEquality(1e-4f) implicit val tolerantDoubleEq: Equality[Double] = TolerantNumerics.tolerantDoubleEquality(1e-5f) + /** Measures the change in available RAM (in MB) before and after executing the provided block + * of code. + * + * @param block + * The block of code to execute. + * @return + * The change in available RAM in MB (positive if RAM increased, negative if decreased). + */ + def measureRAMChange(block: => Any): Long = { + def getFreeRAM: Long = { + val osBean = ManagementFactory.getOperatingSystemMXBean.asInstanceOf[OperatingSystemMXBean] + osBean.getFreePhysicalMemorySize / (1024 * 1024) // Convert to MB + } + + val ramBefore = getFreeRAM + block + sleep(500) // Wait a bit to let the system update memory stats + val ramAfter = getFreeRAM + ramBefore - ramAfter + } } From f49cc87ebf7f6d08bb5e3bbce09fb3f8e5164169 Mon Sep 17 00:00:00 2001 From: Devin Ha Date: Tue, 14 Oct 2025 12:13:49 +0200 Subject: [PATCH 02/13] [SPARKNLP-1283] Add remove thinking flag --- .../annotator/seq2seq/auto_gguf_model.py | 5 +-- .../seq2seq/auto_gguf_vision_model.py | 2 +- python/sparknlp/common/__init__.py | 1 + .../common/completion_post_processing.py | 37 ++++++++++++++++++ .../annotator/seq2seq/auto_gguf_model_test.py | 38 +++++++++++++++++++ .../annotators/seq2seq/AutoGGUFModel.scala | 6 ++- .../seq2seq/AutoGGUFVisionModel.scala | 19 +++++----- .../seq2seq/CompletionPostProcessing.scala | 31 +++++++++++++++ .../seq2seq/AutoGGUFModelTest.scala | 21 ++++++++++ .../seq2seq/AutoGGUFVisionModelTestSpec.scala | 30 +++++++++++++++ 10 files changed, 174 insertions(+), 16 deletions(-) create mode 100644 python/sparknlp/common/completion_post_processing.py create mode 100644 src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/CompletionPostProcessing.scala diff --git a/python/sparknlp/annotator/seq2seq/auto_gguf_model.py b/python/sparknlp/annotator/seq2seq/auto_gguf_model.py index eab1fc1901cdf2..2495afdced304a 100755 --- a/python/sparknlp/annotator/seq2seq/auto_gguf_model.py +++ b/python/sparknlp/annotator/seq2seq/auto_gguf_model.py @@ -12,12 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. """Contains classes for the AutoGGUFModel.""" -from typing import List, Dict - from sparknlp.common import * -class AutoGGUFModel(AnnotatorModel, HasBatchedAnnotate, HasLlamaCppProperties): +class AutoGGUFModel(AnnotatorModel, HasBatchedAnnotate, HasLlamaCppProperties, CompletionPostProcessing): """ Annotator that uses the llama.cpp library to generate text completions with large language models. @@ -243,7 +241,6 @@ class AutoGGUFModel(AnnotatorModel, HasBatchedAnnotate, HasLlamaCppProperties): inputAnnotatorTypes = [AnnotatorType.DOCUMENT] outputAnnotatorType = AnnotatorType.DOCUMENT - @keyword_only def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.AutoGGUFModel", java_model=None): super(AutoGGUFModel, self).__init__( diff --git a/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py b/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py index 9308e1bc34a183..a632567b8b9dd1 100755 --- a/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py +++ b/python/sparknlp/annotator/seq2seq/auto_gguf_vision_model.py @@ -15,7 +15,7 @@ from sparknlp.common import * -class AutoGGUFVisionModel(AnnotatorModel, HasBatchedAnnotate, HasLlamaCppProperties): +class AutoGGUFVisionModel(AnnotatorModel, HasBatchedAnnotate, HasLlamaCppProperties, CompletionPostProcessing): """Multimodal annotator that uses the llama.cpp library to generate text completions with large language models. It supports ingesting images for captioning. diff --git a/python/sparknlp/common/__init__.py b/python/sparknlp/common/__init__.py index f5cdcb45079544..ffcb31c026a7b9 100644 --- a/python/sparknlp/common/__init__.py +++ b/python/sparknlp/common/__init__.py @@ -23,3 +23,4 @@ from sparknlp.common.utils import * from sparknlp.common.annotator_type import * from sparknlp.common.match_strategy import * +from sparknlp.common.completion_post_processing import * diff --git a/python/sparknlp/common/completion_post_processing.py b/python/sparknlp/common/completion_post_processing.py new file mode 100644 index 00000000000000..28bed519f8a052 --- /dev/null +++ b/python/sparknlp/common/completion_post_processing.py @@ -0,0 +1,37 @@ +# Copyright 2017-2025 John Snow Labs +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from pyspark.ml.param import Param, Params, TypeConverters + + +class CompletionPostProcessing: + removeThinkingTag = Param( + Params._dummy(), + "removeThinkingTag", + "Set a thinking tag (e.g. think) to be removed from output. Will match ...", + typeConverter=TypeConverters.toString, + ) + + def setRemoveThinkingTag(self, value: str): + """Set a thinking tag (e.g. `think`) to be removed from output. + Will produce the regex: `(?s)<$TAG>.+?` + """ + self._set(removeThinkingTag=value) + return self + + def getRemoveThinkingTag(self): + """Get the thinking tag to be removed from output.""" + value = None + if self.removeThinkingTag in self._paramMap: + value = self._paramMap[self.removeThinkingTag] + return value diff --git a/python/test/annotator/seq2seq/auto_gguf_model_test.py b/python/test/annotator/seq2seq/auto_gguf_model_test.py index bac3e9d373ffe0..94773bbb6162eb 100644 --- a/python/test/annotator/seq2seq/auto_gguf_model_test.py +++ b/python/test/annotator/seq2seq/auto_gguf_model_test.py @@ -257,6 +257,7 @@ def runTest(self): model_writer.save(model_path) AutoGGUFModel.load(model_path) + model_path = "file:///tmp/autogguf_spark_nlp" AutoGGUFModel.load(model_path) @@ -293,3 +294,40 @@ def runTest(self): print(f"Freed RAM after closing the model: {ramChange} MB") assert (ramChange < -100, "Freed RAM should be greater than 100 MB") + + +@pytest.mark.slow +class AutoGGUFModelThinkingTagTestSpec(unittest.TestCase): + def setUp(self): + self.spark = SparkContextForTest.spark + + def runTest(self): + document_assembler = ( + DocumentAssembler().setInputCol("text").setOutputCol("document") + ) + + think_tag = "think" + + model = ( + AutoGGUFModel.loadSavedModel("models/Qwen3-8B-Q4_K_M.gguf", self.spark) + .setInputCols(["document"]) + .setOutputCol("completions") + .setRemoveThinkingTag(think_tag) + .setNPredict(500) + .setTemperature(0.1) + ) + + data = self.spark.createDataFrame( + [("What is the meaning of life? Think shortly step by step.",)], + ["text"] + ) + + pipeline = Pipeline(stages=[document_assembler, model]) + result = pipeline.fit(data).transform(data) + + completions = result.select("completions").collect() + completion = completions[0][0][0].result + + print(completion) + assert f"<{think_tag}>" not in completion + assert f"" not in completion diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala index 9132cacc9aa400..50f43d286e393c 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModel.scala @@ -124,7 +124,8 @@ class AutoGGUFModel(override val uid: String) with HasEngine with HasLlamaCppModelProperties with HasLlamaCppInferenceProperties - with HasProtectedParams { + with HasProtectedParams + with CompletionPostProcessing { override val outputAnnotatorType: AnnotatorType = AnnotatorType.DOCUMENT override val inputAnnotatorTypes: Array[AnnotatorType] = Array(AnnotatorType.DOCUMENT) @@ -204,7 +205,8 @@ class AutoGGUFModel(override val uid: String) inferenceParams, getSystemPrompt, annotationsText) - (results, Map.empty) + val resultsCleaned = processCompletions(results) + (resultsCleaned, Map.empty) } catch { case e: LlamaException => logger.error("Error in llama.cpp batch completion", e) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala index 34152ecb9a9764..f7f8d0e8976608 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModel.scala @@ -157,7 +157,8 @@ class AutoGGUFVisionModel(override val uid: String) with HasEngine with HasLlamaCppModelProperties with HasLlamaCppInferenceProperties - with HasProtectedParams { + with HasProtectedParams + with CompletionPostProcessing { override val inputAnnotatorTypes: Array[AnnotatorType] = Array(AnnotatorType.IMAGE, AnnotatorType.DOCUMENT) override val outputAnnotatorType: AnnotatorType = AnnotatorType.DOCUMENT @@ -242,14 +243,14 @@ class AutoGGUFVisionModel(override val uid: String) .zip(base64EncodedImages) .map { case (prompt, base64Image) => try { - ( - LlamaExtensions.completeImage( - model, - getInferenceParameters, - getSystemPrompt, - prompt, - base64Image), - Map.empty[String, String]) + val results = LlamaExtensions.completeImage( + model, + getInferenceParameters, + getSystemPrompt, + prompt, + base64Image) + val resultsCleaned = processCompletions(Array(results)).head + (resultsCleaned, Map.empty[String, String]) } catch { case e: LlamaException => logger.error("Error in llama.cpp image batch completion", e) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/CompletionPostProcessing.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/CompletionPostProcessing.scala new file mode 100644 index 00000000000000..bda2565e81eba9 --- /dev/null +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/CompletionPostProcessing.scala @@ -0,0 +1,31 @@ +package com.johnsnowlabs.nlp.annotators.seq2seq + +import org.apache.spark.ml.param.{Param, Params} + +private[nlp] trait CompletionPostProcessing { + this: Params => + + /** @group param */ + val removeThinkingTag = + new Param[String]( + this, + "removeThinkingTag", + "Set a thinking tag (e.g. think) to be removed from output. Will match ...") + + /** Set a thinking tag (e.g. `think`) to be removed from output. Will produce the regex + * `(?s)<$TAG>.+?` + * @group setParam + */ + def setRemoveThinkingTag(value: String): this.type = set(removeThinkingTag, value) + + /** @group getParam */ + def getRemoveThinkingTag: Option[String] = get(removeThinkingTag) + + protected def processCompletions(results: Array[String]): Array[String] = { + getRemoveThinkingTag match { + case Some(thinkingTag) => + results.map(text => text.replaceFirst(s"(?s)<$thinkingTag>.*?", "").trim) + case None => results + } + } +} diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala index 70c0d2f3f93f3b..1175f7ffb645f7 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFModelTest.scala @@ -252,6 +252,27 @@ class AutoGGUFModelTest extends AnyFlatSpec { assert(ramChange < -100, "Freed RAM should be greater than 100 MB") } + it should "be able to remove thinking tags" taggedAs SlowTest in { + val thinkTag = "think" + val model = AutoGGUFModel + .loadSavedModel("models/Qwen3-8B-Q4_K_M.gguf", ResourceHelper.spark) + .setInputCols("document") + .setOutputCol("completions") + .setRemoveThinkingTag(thinkTag) + .setNPredict(500) + .setTemperature(0.1f) + + val data = Seq("What is the meaning of life? Think shortly step by step.").toDF("text") + + val pipeline = + new Pipeline().setStages(Array(documentAssembler, model)) + val result = pipeline.fit(data).transform(data) + + val completion = Annotation.collect(result, "completions").flatten.head.result + println(completion) + assert(!completion.contains(s"<$thinkTag>") && !completion.contains(s"")) + } + // it should "benchmark" taggedAs SlowTest in { // val model = AutoGGUFModel // .loadSavedModel("models/gemma-3-4b-it-qat-Q4_K_M.gguf", ResourceHelper.spark) diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala index 8683731b17253f..e5d5db3eaa93d6 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFVisionModelTestSpec.scala @@ -156,4 +156,34 @@ class AutoGGUFVisionModelTestSpec extends AnyFlatSpec { println("Freed RAM after closing the model: " + ramChange + " MB") assert(ramChange < -100, "Freed RAM should be greater than 100 MB") } + + it should "be able to remove thinking tags" taggedAs SlowTest in { + val thinkTag = "think" + val model = AutoGGUFVisionModel + .loadSavedModel( + "models/SmolVLM-256M-Instruct-Q8_0.gguf", + "models/mmproj-SmolVLM-256M-Instruct-Q8_0.gguf", + ResourceHelper.spark) + .setInputCols("caption_document", "image_assembler") + .setOutputCol("completions") + .setRemoveThinkingTag(thinkTag) + .setNPredict(500) + .setTemperature(0.1f) + + val pipeline = + new Pipeline().setStages(Array(documentAssembler, imageAssembler, model)) + val dataThinking = data + .limit(1) + .withColumn( + "caption", + lit("What is the meaning of life? Think real hard and relate it to the image.")) + + dataThinking.select("caption").show(false) + + val result = pipeline.fit(dataThinking).transform(dataThinking) + + val completion = Annotation.collect(result, "completions").flatten.head.result + println(completion) + assert(!completion.contains(s"<$thinkTag>") && !completion.contains(s"")) + } } From b3bb7828a7e2edcad5a5493f99d39be1dbb2263c Mon Sep 17 00:00:00 2001 From: Danilo Burbano Date: Wed, 15 Oct 2025 14:55:07 -0500 Subject: [PATCH 03/13] [SPARKNLP-1293] Adding extract entities to EntityRuler --- .../nlp/annotators/er/EntityRulerModel.scala | 53 ++++++++--- .../entity-ruler/email-ip-regex.json | 16 ++++ .../annotators/er/EntityRulerFixture.scala | 4 + .../annotators/er/EntityRulerModelTest.scala | 91 +++++++++++++++++++ 4 files changed, 151 insertions(+), 13 deletions(-) create mode 100644 src/test/resources/entity-ruler/email-ip-regex.json create mode 100644 src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala index eab4a1dd52b95c..36f3dbafa80aad 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala @@ -83,6 +83,11 @@ class EntityRulerModel(override val uid: String) private[er] val ahoCorasickAutomaton: StructFeature[Option[AhoCorasickAutomaton]] = new StructFeature[Option[AhoCorasickAutomaton]](this, "AhoCorasickAutomaton") + val extractEntities = new StringArrayParam( + this, + "extractEntities", + "List of entity labels to extract (empty = extract all entities)") + private[er] def setRegexEntities(value: Array[String]): this.type = set(regexEntities, value) private[er] def setEntityRulerFeatures(value: EntityRulerFeatures): this.type = @@ -95,6 +100,8 @@ class EntityRulerModel(override val uid: String) private[er] def setAhoCorasickAutomaton(value: Option[AhoCorasickAutomaton]): this.type = set(ahoCorasickAutomaton, value) + def setExtractEntities(value: Array[String]): this.type = set(extractEntities, value) + private var automatonModel: Option[Broadcast[AhoCorasickAutomaton]] = None def setAutomatonModelIfNotSet( @@ -114,7 +121,11 @@ class EntityRulerModel(override val uid: String) } } - setDefault(useStorage -> false, caseSensitive -> true, enablePatternRegex -> false) + setDefault( + useStorage -> false, + caseSensitive -> true, + enablePatternRegex -> false, + extractEntities -> Array()) /** Annotator reference id. Used to identify elements in metadata or to refer to this annotator * type @@ -172,28 +183,38 @@ class EntityRulerModel(override val uid: String) private def computeAnnotatedEntitiesByRegex( annotations: Seq[Annotation], sentences: Seq[Sentence]): Seq[Annotation] = { - if ($(regexEntities).nonEmpty) { + val entitiesToUse = + if ($(extractEntities).nonEmpty) + $(regexEntities).filter(e => $(extractEntities).contains(e.split(",")(0))) + else + $(regexEntities) + + if (entitiesToUse.nonEmpty) { val regexPatternsReader = if ($(useStorage)) Some(getReader(Database.ENTITY_REGEX_PATTERNS).asInstanceOf[RegexPatternsReader]) else None if ($(sentenceMatch)) { - annotateEntitiesFromRegexPatternsBySentence(sentences, regexPatternsReader) + annotateEntitiesFromRegexPatternsBySentence(sentences, regexPatternsReader, entitiesToUse) } else { val tokenizedWithSentences = TokenizedWithSentence.unpack(annotations) - annotateEntitiesFromRegexPatterns(tokenizedWithSentences, regexPatternsReader) + annotateEntitiesFromRegexPatterns( + tokenizedWithSentences, + regexPatternsReader, + entitiesToUse) } } else Seq() } private def annotateEntitiesFromRegexPatterns( tokenizedWithSentences: Seq[TokenizedSentence], - regexPatternsReader: Option[RegexPatternsReader]): Seq[Annotation] = { + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String]): Seq[Annotation] = { val annotatedEntities = tokenizedWithSentences.flatMap { tokenizedWithSentence => tokenizedWithSentence.indexedTokens.flatMap { indexedToken => - val entity = getMatchedEntity(indexedToken.token, regexPatternsReader) + val entity = getMatchedEntity(indexedToken.token, regexPatternsReader, activeEntities) if (entity.isDefined) { val entityMetadata = getEntityMetadata(entity) Some( @@ -212,9 +233,10 @@ class EntityRulerModel(override val uid: String) private def getMatchedEntity( token: String, - regexPatternsReader: Option[RegexPatternsReader]): Option[String] = { + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String]): Option[String] = { - val matchesByEntity = $(regexEntities).flatMap { regexEntity => + val matchesByEntity = activeEntities.flatMap { regexEntity => val regexPatterns: Option[Seq[String]] = regexPatternsReader match { case Some(rpr) => rpr.lookup(regexEntity) case None => $$(entityRulerFeatures).regexPatterns.get(regexEntity) @@ -234,9 +256,10 @@ class EntityRulerModel(override val uid: String) private def getMatchedEntityBySentence( sentence: Sentence, - regexPatternsReader: Option[RegexPatternsReader]): Array[(IndexedToken, String)] = { + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String]): Array[(IndexedToken, String)] = { - val matchesByEntity = $(regexEntities) + val matchesByEntity = activeEntities .flatMap { regexEntity => val regexPatterns: Option[Seq[String]] = regexPatternsReader match { case Some(rpr) => rpr.lookup(regexEntity) @@ -265,15 +288,19 @@ class EntityRulerModel(override val uid: String) .flatten .sortBy(_._2) - matchesByEntity.map(matches => (IndexedToken(matches._1, matches._2, matches._3), matches._4)) + // Convert to (IndexedToken, EntityLabel) tuples + matchesByEntity.map { case (matchedText, begin, end, entityLabel) => + (IndexedToken(matchedText, begin, end), entityLabel) + } } private def annotateEntitiesFromRegexPatternsBySentence( sentences: Seq[Sentence], - patternsReader: Option[RegexPatternsReader]): Seq[Annotation] = { + patternsReader: Option[RegexPatternsReader], + activeEntities: Array[String]): Seq[Annotation] = { val annotatedEntities = sentences.flatMap { sentence => - val matchedEntities = getMatchedEntityBySentence(sentence, patternsReader) + val matchedEntities = getMatchedEntityBySentence(sentence, patternsReader, activeEntities) matchedEntities.map { case (indexedToken, label) => val entityMetadata = getEntityMetadata(Some(label)) Annotation( diff --git a/src/test/resources/entity-ruler/email-ip-regex.json b/src/test/resources/entity-ruler/email-ip-regex.json new file mode 100644 index 00000000000000..00635d4f8d45bd --- /dev/null +++ b/src/test/resources/entity-ruler/email-ip-regex.json @@ -0,0 +1,16 @@ +[ + { + "id": "email-regex", + "label": "EMAIL", + "regex": true, + "patterns": ["(?i)[a-z0-9\\.\\-+_]+@[a-z0-9\\.\\-+_]+\\.[a-z]+"] + }, + { + "id": "ipv4-regex", + "label": "IP_ADDRESS", + "regex": true, + "patterns": [ + "(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)(?:\\.(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)){3}" + ] + } +] diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala index c443ee21fe0dc8..1aa3f86ee4a730 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala @@ -5,6 +5,10 @@ import com.johnsnowlabs.nlp.AnnotatorType.CHUNK object EntityRulerFixture { + val email1 = "admin@company.org" + val email2 = "john.smith@mailserver.net" + val ip1 = "192.168.1.10" + val ip2 = "10.0.0.1" val text1: String = "John Snow lives in Winterfell" val expectedEntitiesFromText1: Array[Seq[Annotation]] = Array( Seq( diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala new file mode 100644 index 00000000000000..c34b696ed43281 --- /dev/null +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala @@ -0,0 +1,91 @@ +package com.johnsnowlabs.nlp.annotators.er + +import com.johnsnowlabs.nlp.AssertAnnotations +import com.johnsnowlabs.nlp.annotators.SparkSessionTest +import com.johnsnowlabs.nlp.annotators.er.EntityRulerFixture.{email1, email2, ip1, ip2} +import com.johnsnowlabs.nlp.util.io.ReadAs +import org.apache.spark.ml.Pipeline +import org.scalatest.flatspec.AnyFlatSpec + +class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { + + import spark.implicits._ + + private val trainedModel: EntityRulerModel = getTrainedModel + private val basePipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, trainedModel)) + private val sampleData = Seq( + s"Contact $email1 or $email2 from $ip1 or $ip2." + ).toDF("text") + + "EntityRulerModel (default)" should "extract both email and IP entities" in { + trainedModel.setExtractEntities(Array()) + + val resultDf = basePipeline.fit(sampleData).transform(sampleData) + + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + assert(result.length == 4) + assert(result.contains(email1)) + assert(result.contains(email2)) + assert(result.contains(ip1)) + assert(result.contains(ip2)) + } + + "EntityRulerModel (email-only)" should "extract only email addresses" in { + trainedModel.setExtractEntities(Array("EMAIL")) + + val resultDf = basePipeline.fit(sampleData).transform(sampleData) + + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + assert(result.length == 2) + assert(result.contains(email1)) + assert(result.contains(email2)) + assert(!result.contains(ip1)) + assert(!result.contains(ip2)) + } + + "EntityRulerModel (ip-only)" should "extract only IP addresses" in { + trainedModel.setExtractEntities(Array("IP_ADDRESS")) + + val resultDf = basePipeline.fit(sampleData).transform(sampleData) + + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + assert(result.length == 2) + assert(!result.contains(email1)) + assert(!result.contains(email2)) + assert(result.contains(ip1)) + assert(result.contains(ip2)) + } + + "EntityRulerModel (email+ip)" should "extract both emails and IPs when both requested" in { + trainedModel.setExtractEntities(Array("EMAIL", "IP_ADDRESS")) + + val resultDf = basePipeline.fit(sampleData).transform(sampleData) + + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + assert(result.length == 4) + assert(result.contains(email1)) + assert(result.contains(email2)) + assert(result.contains(ip1)) + assert(result.contains(ip2)) + } + + private def getTrainedModel: EntityRulerModel = { + val entityRulerApproach = new EntityRulerApproach() + .setInputCols("document", "token") + .setOutputCol("entities") + .setPatternsResource( + path = "src/test/resources/entity-ruler/email-ip-regex.json", + readAs = ReadAs.TEXT, + options = Map("format" -> "JSON") + ) + .setSentenceMatch(false) + .setUseStorage(false) + + entityRulerApproach.fit(emptyDataSet) + } + +} From 3d86f9a8aa481c7f9a04e3dac07e31f039e43ccb Mon Sep 17 00:00:00 2001 From: Danilo Burbano Date: Thu, 16 Oct 2025 21:25:19 -0500 Subject: [PATCH 04/13] [SPARKNLP-1293] Adding auto mode to DocumentNormalizer and EntityRuler --- .../nlp/annotators/DocumentNormalizer.scala | 222 ++++++++++++-- .../nlp/annotators/er/EntityRulerModel.scala | 287 ++++++++++++++---- .../DocumentNormalizerBehaviors.scala | 22 ++ .../annotators/er/EntityRulerFixture.scala | 15 + .../annotators/er/EntityRulerModelTest.scala | 166 ++++++++++ 5 files changed, 617 insertions(+), 95 deletions(-) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala index 3337469a9a31d4..b310acd5d4f64d 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala @@ -17,14 +17,15 @@ package com.johnsnowlabs.nlp.annotators import com.johnsnowlabs.nlp.AnnotatorType.DOCUMENT +import com.johnsnowlabs.nlp.annotators.cleaners.util.CleanerHelper import com.johnsnowlabs.nlp.{Annotation, AnnotatorModel, AnnotatorType, HasSimpleAnnotate} import org.apache.spark.ml.param.{BooleanParam, Param, StringArrayParam} import org.apache.spark.ml.util.{DefaultParamsReadable, Identifiable} +import org.slf4j.{Logger, LoggerFactory} import java.nio.charset.{Charset, StandardCharsets} import scala.collection.mutable.ListBuffer import scala.util.matching.Regex -import scala.util.matching.Regex.Match import scala.util.{Failure, Success, Try} import scala.xml.XML @@ -104,7 +105,9 @@ import scala.xml.XML */ class DocumentNormalizer(override val uid: String) extends AnnotatorModel[DocumentNormalizer] - with HasSimpleAnnotate[DocumentNormalizer] { + with HasSimpleAnnotate[DocumentNormalizer]{ + + private val logger: Logger = LoggerFactory.getLogger(this.getClass) private val EMPTY_STR = "" private val BREAK_STR = "|##|" @@ -174,6 +177,15 @@ class DocumentNormalizer(override val uid: String) name = "encoding", "File encoding to apply on normalized documents (Default: `disable`)") + val presetPattern = new Param[String]( + this, + "presetPattern", + "Single functional cleaner preset (CLEAN_BULLETS, CLEAN_DASHES, etc.)" + ) + + val autoMode = new Param[String](this, "autoMode", + "Automatic cleaning mode grouping multiple cleaners: light_clean, document_clean, social_clean, html_clean, full_auto") + // Assuming non-html does not contain any < or > and that input string is correctly structured setDefault( inputCols -> Array(AnnotatorType.DOCUMENT), @@ -258,6 +270,8 @@ class DocumentNormalizer(override val uid: String) */ def setEncoding(value: String): this.type = set(encoding, value) + def setPresetPattern(value: String): this.type = set(presetPattern, value) + /** Applying document normalization without pretty formatting (removing multiple spaces) */ private def withAllFormatter( text: String, @@ -357,46 +371,188 @@ class DocumentNormalizer(override val uid: String) /** Apply document normalization on text using action, patterns, policy, lowercase and encoding * parameters. */ +// private def applyDocumentNormalization( +// text: String, +// action: String, +// patterns: Array[String], +// replacement: String, +// policy: String, +// lowercase: Boolean, +// encoding: String): String = { +// +// val builtinPatterns: Array[String] = { +// if (isDefined(presetPattern)) { +// val presetName = $(presetPattern) +// BUILTIN_PATTERNS.getOrElse( +// presetName, +// throw new IllegalArgumentException( +// s"Unknown preset pattern '$presetName'. Valid presets: ${BUILTIN_PATTERNS.keys.mkString(", ")}" +// ) +// ) +// } else { +// Array.empty[String] +// } +// } +// +// val combinedPatterns: Array[String] = { +// val userPatterns = Option(patterns).getOrElse(Array.empty[String]) +// (builtinPatterns ++ userPatterns).distinct +// } +// +// val processedWithActionPatterns: String = policy match { +// case "all" => withAllFormatter(text, action, combinedPatterns, replacement) +// case "pretty_all" => withPrettyAllFormatter(text, action, combinedPatterns, replacement) +// case "first" => withFirstFormatter(text, action, combinedPatterns, replacement) +// case "pretty_first" => withPrettyFirstFormatter(text, action, combinedPatterns, replacement) +// case _ => +// throw new Exception( +// "Unknown policy parameter in DocumentNormalizer annotation." + +// "Please select either: all, pretty_all, first, or pretty_first") +// } +// +// val cased = +// if (lowercase) +// processedWithActionPatterns.toLowerCase +// else +// processedWithActionPatterns +// +// encoding match { +// case "disable" => cased +// case "UTF-8" => withEncoding(cased, StandardCharsets.UTF_8) +// case "UTF-16" => withEncoding(cased, StandardCharsets.UTF_16) +// case "US-ASCII" => withEncoding(cased, StandardCharsets.US_ASCII) +// case "ISO-8859-1" => withEncoding(cased, StandardCharsets.ISO_8859_1) +// case "UTF-16BE" => withEncoding(cased, StandardCharsets.UTF_16BE) +// case "UTF-16LE" => withEncoding(cased, StandardCharsets.UTF_16LE) +// case _ => +// throw new Exception("Unknown encoding parameter in DocumentNormalizer annotation." + +// "Please select either: disable, UTF_8, UTF_16, US_ASCII, ISO-8859-1, UTF-16BE, UTF-16LE") +// } +// } + + /** + * Applies document normalization: + * 1. User-defined regex patterns (if any) + * 2. Either functional preset (CleanerHelper) OR autoMode (CleanerHelper bundles) + * 3. Lowercasing and encoding policy + */ private def applyDocumentNormalization( - text: String, - action: String, - patterns: Array[String], - replacement: String, - policy: String, - lowercase: Boolean, - encoding: String): String = { - - val processedWithActionPatterns: String = policy match { - case "all" => withAllFormatter(text, action, patterns, replacement) - case "pretty_all" => withPrettyAllFormatter(text, action, patterns, replacement) - case "first" => withFirstFormatter(text, action, patterns, replacement) - case "pretty_first" => withPrettyFirstFormatter(text, action, patterns, replacement) + text: String, + action: String, + patterns: Array[String], + replacement: String, + policy: String, + lowercase: Boolean, + encoding: String + ): String = { + + val hasPreset = isDefined(presetPattern) && FUNCTIONAL_PRESETS.contains($(presetPattern)) + val hasAutoMode = isDefined(autoMode) && AUTO_MODE_FUNCTIONS.contains($(autoMode)) + + val selectedCleaner: Either[String => String, Seq[String => String]] = (hasPreset, hasAutoMode) match { + case (true, true) => + logger.warn( + s"[DocumentNormalizer] Both presetPattern (${ $(presetPattern) }) and autoMode (${ $(autoMode) }) are set. " + + s"autoMode will take precedence." + ) + Right(AUTO_MODE_FUNCTIONS($(autoMode))) + case (true, false) => + Left(FUNCTIONAL_PRESETS($(presetPattern))) + case (false, true) => + Right(AUTO_MODE_FUNCTIONS($(autoMode))) case _ => - throw new Exception( - "Unknown policy parameter in DocumentNormalizer annotation." + - "Please select either: all, pretty_all, first, or pretty_first") + Right(Seq.empty) } - val cased = - if (lowercase) - processedWithActionPatterns.toLowerCase - else - processedWithActionPatterns + val userPatterns = Option(patterns).getOrElse(Array.empty[String]) + + val regexCleanedText: String = if (userPatterns.nonEmpty) { + policy match { + case "all" => withAllFormatter(text, action, userPatterns, replacement) + case "pretty_all" => withPrettyAllFormatter(text, action, userPatterns, replacement) + case "first" => withFirstFormatter(text, action, userPatterns, replacement) + case "pretty_first" => withPrettyFirstFormatter(text, action, userPatterns, replacement) + case _ => + throw new Exception( + "Unknown policy parameter in DocumentNormalizer. " + + "Valid options: all, pretty_all, first, pretty_first." + ) + } + } else text + + val cleanedText: String = selectedCleaner match { + case Left(fn) => + logger.info(s"[DocumentNormalizer] Applying preset cleaner: ${ $(presetPattern) }") + fn(regexCleanedText) + + case Right(funcs) if funcs.nonEmpty => + val modeName = $(autoMode) + val functionNames = funcs.flatMap(FUNCTION_NAME_LOOKUP.get) + logger.info( + s"[DocumentNormalizer] AutoMode '$modeName' active. Applying cleaners in order: " + + functionNames.mkString(", ") + ) + funcs.foldLeft(regexCleanedText) { (acc, cleanerFn) => cleanerFn(acc) } - encoding match { - case "disable" => cased - case "UTF-8" => withEncoding(cased, StandardCharsets.UTF_8) - case "UTF-16" => withEncoding(cased, StandardCharsets.UTF_16) - case "US-ASCII" => withEncoding(cased, StandardCharsets.US_ASCII) - case "ISO-8859-1" => withEncoding(cased, StandardCharsets.ISO_8859_1) - case "UTF-16BE" => withEncoding(cased, StandardCharsets.UTF_16BE) - case "UTF-16LE" => withEncoding(cased, StandardCharsets.UTF_16LE) case _ => - throw new Exception("Unknown encoding parameter in DocumentNormalizer annotation." + - "Please select either: disable, UTF_8, UTF_16, US_ASCII, ISO-8859-1, UTF-16BE, UTF-16LE") + regexCleanedText + } + + val casedText = if (lowercase) cleanedText.toLowerCase else cleanedText + + encoding match { + case "disable" => casedText + case "UTF-8" => withEncoding(casedText, StandardCharsets.UTF_8) + case "UTF-16" => withEncoding(casedText, StandardCharsets.UTF_16) + case "US-ASCII" => withEncoding(casedText, StandardCharsets.US_ASCII) + case "ISO-8859-1" => withEncoding(casedText, StandardCharsets.ISO_8859_1) + case "UTF-16BE" => withEncoding(casedText, StandardCharsets.UTF_16BE) + case "UTF-16LE" => withEncoding(casedText, StandardCharsets.UTF_16LE) + case other => + throw new Exception( + s"Unknown encoding parameter: $other. " + + "Please select one of disable, UTF-8, UTF-16, US-ASCII, ISO-8859-1, UTF-16BE, UTF-16LE." + ) } } + private lazy val FUNCTIONAL_PRESETS: Map[String, String => String] = Map( + "CLEAN_BULLETS" -> CleanerHelper.cleanBullets, + "CLEAN_ORDERED_BULLETS" -> CleanerHelper.cleanOrderedBullets, + "CLEAN_DASHES" -> CleanerHelper.cleanDashes, + "CLEAN_TRAILING_PUNCTUATION" -> CleanerHelper.cleanTrailingPunctuation, + "CLEAN_EXTRA_WHITESPACE" -> CleanerHelper.cleanExtraWhitespace, + "REMOVE_PUNCTUATION" -> CleanerHelper.removePunctuation, + "CLEAN_NON_ASCII" -> CleanerHelper.cleanNonAsciiChars, + "REPLACE_UNICODE" -> CleanerHelper.replaceUnicodeCharacters + ) + + private lazy val AUTO_MODE_FUNCTIONS: Map[String, Seq[String => String]] = Map( + "light_clean" -> Seq( + CleanerHelper.cleanExtraWhitespace, + CleanerHelper.cleanTrailingPunctuation + ), + "document_clean" -> Seq( + CleanerHelper.cleanBullets, + CleanerHelper.cleanOrderedBullets, + CleanerHelper.cleanDashes, + CleanerHelper.cleanExtraWhitespace + ), + "social_clean" -> Seq( + CleanerHelper.removePunctuation, + CleanerHelper.cleanDashes, + CleanerHelper.cleanExtraWhitespace + ), + "html_clean" -> Seq( + CleanerHelper.replaceUnicodeCharacters, + CleanerHelper.cleanNonAsciiChars + ), + "full_auto" -> FUNCTIONAL_PRESETS.values.toSeq + ) + + // Reverse-map functions to preset names (for logging) + private lazy val FUNCTION_NAME_LOOKUP: Map[String => String, String] = FUNCTIONAL_PRESETS.map(_.swap) + override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = annotations.map { annotation => Try( diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala index 36f3dbafa80aad..1b12394b42844e 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala @@ -18,17 +18,21 @@ package com.johnsnowlabs.nlp.annotators.er import com.johnsnowlabs.nlp.AnnotatorType.{CHUNK, DOCUMENT, TOKEN} import com.johnsnowlabs.nlp.annotators.common._ +import com.johnsnowlabs.nlp.annotators.er.EntityRulerModel.{AUTO_MODES, ENTITY_PRESETS, describeAutoMode, getPatternByName} import com.johnsnowlabs.nlp.serialization.StructFeature import com.johnsnowlabs.nlp.{Annotation, AnnotatorModel, HasPretrained, HasSimpleAnnotate} import com.johnsnowlabs.storage.Database.{ENTITY_REGEX_PATTERNS, Name} import com.johnsnowlabs.storage._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.ml.PipelineModel -import org.apache.spark.ml.param.{BooleanParam, StringArrayParam} +import org.apache.spark.ml.param.{BooleanParam, Param, ParamMap, StringArrayParam} import org.apache.spark.ml.util.Identifiable import org.apache.spark.sql.{DataFrame, Dataset, SparkSession} import org.slf4j.{Logger, LoggerFactory} +import java.util.Locale +import scala.util.Try + /** Instantiated model of the [[EntityRulerApproach]]. For usage and examples see the * documentation of the main class. * @@ -56,9 +60,10 @@ class EntityRulerModel(override val uid: String) with HasSimpleAnnotate[EntityRulerModel] with HasStorageModel { - def this() = this(Identifiable.randomUID("ENTITY_RULER")) + def this() = this(Identifiable.randomUID("ENTITY_RULER_MODEL")) - private val logger: Logger = LoggerFactory.getLogger("Credentials") + @transient + private val logger: Logger = LoggerFactory.getLogger("EntityRulerModel") // Keeping this parameter for backward compatibility private[er] val enablePatternRegex = @@ -88,6 +93,16 @@ class EntityRulerModel(override val uid: String) "extractEntities", "List of entity labels to extract (empty = extract all entities)") + /** + * AutoMode defines logical bundles of regex patterns to activate (e.g., "network_entities", "email_entities"). + * When set, overrides activeEntities selection. + */ + private[er] val autoMode: Param[String] = new Param[String]( + this, + "autoMode", + "Predefined bundle of entity patterns to use (e.g. 'network_entities', 'email_entities', 'all_entities')." + ) + private[er] def setRegexEntities(value: Array[String]): this.type = set(regexEntities, value) private[er] def setEntityRulerFeatures(value: EntityRulerFeatures): this.type = @@ -102,8 +117,16 @@ class EntityRulerModel(override val uid: String) def setExtractEntities(value: Array[String]): this.type = set(extractEntities, value) + def setAutoMode(value: String): this.type = set(autoMode, value) + private var automatonModel: Option[Broadcast[AhoCorasickAutomaton]] = None + val hasAutoMode: Boolean = { + val value = if (isDefined(autoMode)) Try($(autoMode)).toOption else None + val result = value.exists(AUTO_MODES.contains) + result + } + def setAutomatonModelIfNotSet( spark: SparkSession, automaton: Option[AhoCorasickAutomaton]): this.type = { @@ -117,15 +140,20 @@ class EntityRulerModel(override val uid: String) if (automatonModel.isDefined) { Some(automatonModel.get.value) } else { - if ($$(ahoCorasickAutomaton).isDefined) $$(ahoCorasickAutomaton) else None + if (this.get(ahoCorasickAutomaton).isDefined && $$(ahoCorasickAutomaton).isDefined) $$(ahoCorasickAutomaton) else None } } + setDefault( useStorage -> false, + sentenceMatch -> false, caseSensitive -> true, enablePatternRegex -> false, - extractEntities -> Array()) + extractEntities -> Array(), + regexEntities -> Array(), + autoMode -> "" + ) /** Annotator reference id. Used to identify elements in metadata or to refer to this annotator * type @@ -152,11 +180,26 @@ class EntityRulerModel(override val uid: String) } } + private def getActiveEntitiesFromAutoMode: Array[String] = { + if (isDefined(autoMode) && Option($(autoMode)).exists(_.nonEmpty)) { + val modeKey = $(autoMode).toUpperCase(Locale.ROOT) + val validEntities = EntityRulerModel.AUTO_MODES.getOrElse(modeKey, Seq.empty) + validEntities.toArray + } else { + //Fallback for legacy regex-based pipelines + $(regexEntities) + } + } + override def beforeAnnotate(dataset: Dataset[_]): Dataset[_] = { - this.setAutomatonModelIfNotSet(dataset.sparkSession, $$(ahoCorasickAutomaton)) + getAutomatonModelIfNotSet.foreach { automaton => + this.setAutomatonModelIfNotSet(dataset.sparkSession, Some(automaton)) + } dataset } + + /** takes a document and annotations and produces new annotations of this annotator's annotation * type * @@ -183,11 +226,15 @@ class EntityRulerModel(override val uid: String) private def computeAnnotatedEntitiesByRegex( annotations: Seq[Annotation], sentences: Seq[Sentence]): Seq[Annotation] = { - val entitiesToUse = - if ($(extractEntities).nonEmpty) - $(regexEntities).filter(e => $(extractEntities).contains(e.split(",")(0))) - else - $(regexEntities) + val entitiesToUse = { + val baseEntities = + if ($(extractEntities).nonEmpty) { + $(regexEntities).filter(e => $(extractEntities).contains(e.split(",")(0))) + } else if (isDefined(autoMode)) getActiveEntitiesFromAutoMode + else $(regexEntities) + + baseEntities + } if (entitiesToUse.nonEmpty) { val regexPatternsReader = @@ -208,9 +255,10 @@ class EntityRulerModel(override val uid: String) } private def annotateEntitiesFromRegexPatterns( - tokenizedWithSentences: Seq[TokenizedSentence], - regexPatternsReader: Option[RegexPatternsReader], - activeEntities: Array[String]): Seq[Annotation] = { + tokenizedWithSentences: Seq[TokenizedSentence], + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String] + ): Seq[Annotation] = { val annotatedEntities = tokenizedWithSentences.flatMap { tokenizedWithSentence => tokenizedWithSentence.indexedTokens.flatMap { indexedToken => @@ -223,77 +271,134 @@ class EntityRulerModel(override val uid: String) indexedToken.begin, indexedToken.end, indexedToken.token, - entityMetadata ++ Map("sentence" -> tokenizedWithSentence.sentenceIndex.toString))) - } else None + entityMetadata ++ Map("sentence" -> tokenizedWithSentence.sentenceIndex.toString) + ) + ) + } else { + None + } } } annotatedEntities } + private def getMatchedEntity( - token: String, - regexPatternsReader: Option[RegexPatternsReader], - activeEntities: Array[String]): Option[String] = { - - val matchesByEntity = activeEntities.flatMap { regexEntity => - val regexPatterns: Option[Seq[String]] = regexPatternsReader match { - case Some(rpr) => rpr.lookup(regexEntity) - case None => $$(entityRulerFeatures).regexPatterns.get(regexEntity) + token: String, + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String] + ): Option[String] = { + + val hasActiveEntities = activeEntities != null && activeEntities.nonEmpty + + val selectedEntities: Seq[(String, String)] = if (hasAutoMode) { + describeAutoMode($(autoMode)).flatMap(name => getPatternByName(name).map(name -> _)) + + } else if (hasActiveEntities) { + activeEntities.flatMap { regexEntity => + // load from reader if available, else from local model storage + val regexPatterns: Option[Seq[String]] = regexPatternsReader match { + case Some(rpr) => { + rpr.lookup(regexEntity) + } + case None => { + //fallback if entityRulerFeatures is not set + if (get(entityRulerFeatures).isDefined) + $$(entityRulerFeatures).regexPatterns.get(regexEntity) + else + EntityRulerModel.ENTITY_PRESETS.get(regexEntity).map(Seq(_)) + } + } + + regexPatterns match { + case Some(patterns) => patterns.map(p => regexEntity -> p) + case None => Seq.empty + } } - if (regexPatterns.isDefined) { - val matches = regexPatterns.get.flatMap(regexPattern => regexPattern.r.findFirstIn(token)) - if (matches.nonEmpty) Some(regexEntity) else None - } else None - }.toSeq - - if (matchesByEntity.size > 1) { - logger.warn("More than one entity found. Sending the first element of the array") + + } else { + ENTITY_PRESETS.toSeq } + val matchesByEntity = selectedEntities.flatMap { case (entityName, regexPattern) => + regexPattern.r.findFirstIn(token).map(_ => entityName) + } + + if (matchesByEntity.size > 1) + logger.warn( + s"[EntityRulerModel] Multiple entities matched token '$token': ${matchesByEntity.mkString(", ")}. " + + s"Returning first entity '${matchesByEntity.head}'." + ) + matchesByEntity.headOption } + + /** + * Extracts all regex-matched entities within a sentence, supporting both autoMode and manual patterns. + */ private def getMatchedEntityBySentence( - sentence: Sentence, - regexPatternsReader: Option[RegexPatternsReader], - activeEntities: Array[String]): Array[(IndexedToken, String)] = { + sentence: Sentence, + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String] + ): Array[(IndexedToken, String)] = { - val matchesByEntity = activeEntities - .flatMap { regexEntity => + import EntityRulerModel._ + + val hasActiveEntities = activeEntities != null && activeEntities.nonEmpty + + val selectedEntities: Seq[(String, String)] = if (hasAutoMode) { + describeAutoMode($(autoMode)).flatMap(name => getPatternByName(name).map(name -> _)) + + } else if (hasActiveEntities) { + activeEntities.flatMap { regexEntity => val regexPatterns: Option[Seq[String]] = regexPatternsReader match { case Some(rpr) => rpr.lookup(regexEntity) - case None => $$(entityRulerFeatures).regexPatterns.get(regexEntity) - } - if (regexPatterns.isDefined) { - - val resultMatches = regexPatterns.get.flatMap { regexPattern => - val matchedResult = regexPattern.r.findFirstMatchIn(sentence.content) - if (matchedResult.isDefined) { - val begin = matchedResult.get.start + sentence.start - val end = matchedResult.get.end + sentence.start - 1 - Some(matchedResult.get.toString(), begin, end, regexEntity) - } else None + case None => { + //fallback if entityRulerFeatures is not set + if (get(entityRulerFeatures).isDefined) + $$(entityRulerFeatures).regexPatterns.get(regexEntity) + else + EntityRulerModel.ENTITY_PRESETS.get(regexEntity).map(Seq(_)) } + } - val intervals = - resultMatches.map(resultMatch => List(resultMatch._2, resultMatch._3)).toList - val mergedIntervals = EntityRulerUtil.mergeIntervals(intervals) - val filteredMatches = - resultMatches.filter(x => mergedIntervals.contains(List(x._2, x._3))) - - if (filteredMatches.nonEmpty) Some(filteredMatches) else None - } else None + regexPatterns match { + case Some(patterns) => patterns.map(p => regexEntity -> p) + case None => Seq.empty + } } - .flatten - .sortBy(_._2) - // Convert to (IndexedToken, EntityLabel) tuples - matchesByEntity.map { case (matchedText, begin, end, entityLabel) => - (IndexedToken(matchedText, begin, end), entityLabel) + } else { + ENTITY_PRESETS.toSeq } + + val matchesByEntity = selectedEntities.flatMap { case (regexEntity, regexPattern) => + val allMatches = regexPattern.r.findAllMatchIn(sentence.content).map { matched => + val begin = matched.start + sentence.start + val end = matched.end + sentence.start - 1 + (matched.matched, begin, end, regexEntity) + }.toSeq + + // Merge overlapping intervals for same pattern + val intervals = allMatches.map { case (_, b, e, _) => List(b, e) }.toList + val mergedIntervals = EntityRulerUtil.mergeIntervals(intervals) + + val filteredMatches = + allMatches.filter { case (_, b, e, _) => mergedIntervals.contains(List(b, e)) } + + if (filteredMatches.nonEmpty) Some(filteredMatches) else None + }.flatten + .sortBy(_._2) // sort by begin position + + matchesByEntity.map { + case (matchedText, begin, end, entityLabel) => + (IndexedToken(matchedText, begin, end), entityLabel) + }.toArray } + private def annotateEntitiesFromRegexPatternsBySentence( sentences: Seq[Sentence], patternsReader: Option[RegexPatternsReader], @@ -329,6 +434,12 @@ class EntityRulerModel(override val uid: String) entityMetadata } + override def copy(extra: ParamMap): EntityRulerModel = { + val copied = defaultCopy(extra) + if (isDefined(autoMode)) this.setAutoMode($(autoMode)) + copied + } + override def deserializeStorage(path: String, spark: SparkSession): Unit = { if ($(useStorage)) { super.deserializeStorage(path: String, spark: SparkSession) @@ -368,4 +479,56 @@ trait ReadablePretrainedEntityRuler } -object EntityRulerModel extends ReadablePretrainedEntityRuler +object EntityRulerModel extends ReadablePretrainedEntityRuler { + + val EMAIL_DATETIMETZ_PATTERN = "EMAIL_DATETIMETZ_PATTERN" + val EMAIL_ADDRESS_PATTERN = "EMAIL_ADDRESS_PATTERN" + val IPV4_PATTERN = "IPV4_PATTERN" + val IPV6_PATTERN = "IPV6_PATTERN" + val IP_ADDRESS_PATTERN = "IP_ADDRESS_PATTERN" + val IP_ADDRESS_NAME_PATTERN = "IP_ADDRESS_NAME_PATTERN" + val MAPI_ID_PATTERN = "MAPI_ID_PATTERN" + val US_PHONE_NUMBERS_PATTERN = "US_PHONE_NUMBERS_PATTERN" + val IMAGE_URL_PATTERN = "IMAGE_URL_PATTERN" + + val NETWORK_ENTITIES = "NETWORK_ENTITIES" + val EMAIL_ENTITIES = "EMAIL_ENTITIES" + val COMMUNICATION_ENTITIES = "COMMUNICATION_ENTITIES" + val CONTACT_ENTITIES = "CONTACT_ENTITIES" + val MEDIA_ENTITIES = "MEDIA_ENTITIES" + val ALL_ENTITIES = "ALL_ENTITIES" + + private lazy val ENTITY_PRESETS: Map[String, String] = Map( + EMAIL_DATETIMETZ_PATTERN -> "[A-Za-z]{3},\\s\\d{1,2}\\s[A-Za-z]{3}\\s\\d{4}\\s\\d{2}:\\d{2}:\\d{2}\\s[+-]\\d{4}", + EMAIL_ADDRESS_PATTERN -> "(?i)[a-z0-9\\.\\-+_]+@[a-z0-9\\.\\-+_]+\\.[a-z]+", + IPV4_PATTERN -> "(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)(?:\\.(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)){3}", + IPV6_PATTERN -> "[a-z0-9]{4}::[a-z0-9]{4}:[a-z0-9]{4}:[a-z0-9]{4}:[a-z0-9]{4}%?[0-9]*", + IP_ADDRESS_NAME_PATTERN -> "[a-zA-Z0-9-]*\\.[a-zA-Z]*\\.[a-zA-Z]*", + MAPI_ID_PATTERN -> "[0-9]*\\.[0-9]*\\.[0-9]*\\.[0-9]*", + US_PHONE_NUMBERS_PATTERN -> "(?:\\+?(\\d{1,3}))?[-. (]*(\\d{3})?[-. )]*(\\d{3})[-. ]*(\\d{4})(?: *x(\\d+))?\\s*$", + IMAGE_URL_PATTERN -> "(?i)https?://(?:[a-z0-9$_@.&+!*\\(\\),%-])+(?:/[a-z0-9$_@.&+!*\\(\\),%-]*)*\\.(?:jpg|jpeg|png|gif|bmp|heic)" + ) + + private lazy val AUTO_MODES: Map[String, Seq[String]] = Map( + NETWORK_ENTITIES.toUpperCase(Locale.ROOT) -> Seq( + IPV4_PATTERN, IPV6_PATTERN, IP_ADDRESS_NAME_PATTERN + ), + EMAIL_ENTITIES.toUpperCase(Locale.ROOT) -> Seq( + EMAIL_ADDRESS_PATTERN, EMAIL_DATETIMETZ_PATTERN, MAPI_ID_PATTERN + ), + COMMUNICATION_ENTITIES.toUpperCase(Locale.ROOT) -> Seq( + EMAIL_ADDRESS_PATTERN, US_PHONE_NUMBERS_PATTERN + ), + CONTACT_ENTITIES.toUpperCase(Locale.ROOT) -> ( + Seq(EMAIL_ADDRESS_PATTERN, US_PHONE_NUMBERS_PATTERN) ++ + Seq(IPV4_PATTERN, IPV6_PATTERN, IP_ADDRESS_NAME_PATTERN) + ), + MEDIA_ENTITIES.toUpperCase(Locale.ROOT) -> Seq(IMAGE_URL_PATTERN), + ALL_ENTITIES.toUpperCase(Locale.ROOT) -> ENTITY_PRESETS.keys.toSeq + ) + + def getPatternByName(name: String): Option[String] = ENTITY_PRESETS.get(name) + + def describeAutoMode(mode: String): Seq[String] = AUTO_MODES.getOrElse(mode, Seq.empty) + +} diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala index 3be943f5b27170..e8202cf4db494e 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala @@ -426,4 +426,26 @@ trait DocumentNormalizerBehaviors extends AnyFlatSpec { println(Annotation.apply("")) docPatternRemoverPipeline.fit(data).transform(data).show(false) } + + it should "clean bullets" in { + val spark = SparkAccessor.spark + import spark.implicits._ + + val documentAssembler = new DocumentAssembler() + .setInputCol("text") + .setOutputCol("document") + + val documentNormalizer = new DocumentNormalizer() + .setInputCols("document") + .setOutputCol("cleanedDocument") + .setPresetPattern("CLEAN_BULLETS") + .setPolicy("pretty_all") + .setLowercase(true) + + val data = Seq("• Buy milk\n- Feed the cat\n* Call mom").toDF("text") + val pipeline = new Pipeline().setStages(Array(documentAssembler, documentNormalizer)) + val result = pipeline.fit(data).transform(data) + result.selectExpr("cleanedDocument.result").show(false) + } + } diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala index 1aa3f86ee4a730..d08fca4ec4041d 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerFixture.scala @@ -1,3 +1,18 @@ +/* + * Copyright 2017-2025 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ package com.johnsnowlabs.nlp.annotators.er import com.johnsnowlabs.nlp.Annotation diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala index c34b696ed43281..e0fff6a043bb64 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala @@ -1,3 +1,18 @@ +/* + * Copyright 2017-2025 John Snow Labs + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ package com.johnsnowlabs.nlp.annotators.er import com.johnsnowlabs.nlp.AssertAnnotations @@ -5,6 +20,7 @@ import com.johnsnowlabs.nlp.annotators.SparkSessionTest import com.johnsnowlabs.nlp.annotators.er.EntityRulerFixture.{email1, email2, ip1, ip2} import com.johnsnowlabs.nlp.util.io.ReadAs import org.apache.spark.ml.Pipeline +import org.apache.spark.sql.DataFrame import org.scalatest.flatspec.AnyFlatSpec class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { @@ -73,6 +89,146 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.contains(ip2)) } + "EntityRulerModel (autoMode='contact_entities')" should "extract emails" in { + val autoModel = new EntityRulerModel() + .setInputCols("document", "token") + .setOutputCol("entities") + .setAutoMode("contact_entities") // activates EMAIL + IP patterns internally + + val autoPipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, autoModel)) + + val resultDf = autoPipeline.fit(sampleData).transform(sampleData) + + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + + assert(result.length == 4) + assert(result.contains(email1)) + assert(result.contains(email2)) + assert(result.contains(ip1)) + assert(result.contains(ip2)) + } + + it should "detect only emails and phones with COMMUNICATION_ENTITIES mode" in { + val resultDf = runPipeline(EntityRulerModel.COMMUNICATION_ENTITIES) + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + + assert(result.contains(email1)) + assert(result.contains(email2)) + assert(result.length == 2) + } + + it should "detect only IPs and hostnames with NETWORK_ENTITIES mode" in { + val resultDf = runPipeline(EntityRulerModel.NETWORK_ENTITIES) + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + + assert(result.contains(ip1)) + assert(result.contains(ip2)) + } + + it should "detect both emails, phones, and IPs with CONTACT_ENTITIES mode" in { + val resultDf = runPipeline(EntityRulerModel.CONTACT_ENTITIES) + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + + assert(result.contains(email1)) + assert(result.contains(email2)) + assert(result.contains(ip1)) + assert(result.contains(ip2)) + assert(result.length >= 4) + } + + it should "detect everything with ALL_ENTITIES mode" in { + val resultDf = runPipeline(EntityRulerModel.ALL_ENTITIES) + val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + .map(annotation => annotation.result) + + assert(result.contains(email1)) + assert(result.contains(email2)) + assert(result.contains(ip1)) + assert(result.contains(ip2)) + assert(result.length >= 4) + } + + it should "handle case-insensitive autoMode values" in { + val mixedCaseModel = runPipeline("Contact_Entities") + val upperCaseModel = runPipeline("CONTACT_ENTITIES") + val lowerCaseModel = runPipeline("contact_entities") + + val e1 = mixedCaseModel.selectExpr("explode(entities.result)").as[String].collect().sorted + val e2 = upperCaseModel.selectExpr("explode(entities.result)").as[String].collect().sorted + val e3 = lowerCaseModel.selectExpr("explode(entities.result)").as[String].collect().sorted + + assert(e1.sameElements(e2)) + assert(e2.sameElements(e3)) + } + + it should "detect email addresses using sentence-level regex matching" in { + val data = Seq( + "Please contact us at info@mydomain.com or support@helpdesk.net for assistance." + ).toDF("text") + + val model = new EntityRulerModel() + .setInputCols("document", "token") + .setOutputCol("entities") + .setAutoMode(EntityRulerModel.COMMUNICATION_ENTITIES) + .setSentenceMatch(true) + + val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, model)) + val result = pipeline.fit(data).transform(data) + val entities = result.selectExpr("explode(entities.result)").as[String].collect() + + assert(entities.contains("info@mydomain.com")) + assert(entities.contains("support@helpdesk.net")) + assert(entities.length == 2) + } + + it should "detect full IP range patterns at sentence-level when using NETWORK_ENTITIES mode" in { + val data = Seq( + "This server list includes 192.168.1.1, 10.0.0.45 and 172.16.0.2 for internal routing." + ).toDF("text") + + val model = new EntityRulerModel() + .setInputCols("document", "token") + .setOutputCol("entities") + .setAutoMode(EntityRulerModel.NETWORK_ENTITIES) + .setSentenceMatch(true) // ✅ sentence-based regex match + + val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, model)) + val result = pipeline.fit(data).transform(data) + + val entities = result.selectExpr("explode(entities.result)").as[String].collect() + + // We expect all three IPs to match since sentence regex scans full content + assert(entities.exists(_.contains("192.168.1.1"))) + assert(entities.exists(_.contains("10.0.0.45"))) + assert(entities.exists(_.contains("172.16.0.2"))) + assert(entities.length == 3) + } + + it should "handle case-insensitive autoMode with sentenceMatch = true" in { + val data = Seq( + "Reach us via contact@team.io or sales@biz.org — thank you." + ).toDF("text") + + val model = new EntityRulerModel() + .setInputCols("document", "token") + .setOutputCol("entities") + .setAutoMode("contact_entities") // lowercase intentional + .setSentenceMatch(true) + + val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, model)) + val result = pipeline.fit(data).transform(data) + + val entities = result.selectExpr("explode(entities.result)").as[String].collect() + + assert(entities.exists(_.contains("contact@team.io"))) + assert(entities.exists(_.contains("sales@biz.org"))) + assert(entities.length == 2) + } + private def getTrainedModel: EntityRulerModel = { val entityRulerApproach = new EntityRulerApproach() .setInputCols("document", "token") @@ -88,4 +244,14 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { entityRulerApproach.fit(emptyDataSet) } + private def runPipeline(autoMode: String): DataFrame = { + val model = new EntityRulerModel() + .setInputCols("document", "token") + .setOutputCol("entities") + .setAutoMode(autoMode) + + val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, model)) + pipeline.fit(sampleData).transform(sampleData) + } + } From 825306dc075e93c38b5120bb14876fe47228262c Mon Sep 17 00:00:00 2001 From: Danilo Burbano Date: Fri, 17 Oct 2025 10:43:52 -0500 Subject: [PATCH 05/13] [SPARKNLP-1293] Adding unit tests and python wrapper parameters for auto mode feature --- .../sparknlp/annotator/document_normalizer.py | 36 ++++ python/sparknlp/annotator/er/entity_ruler.py | 35 ++++ ...er_spec.py => document_normalizer_test.py} | 25 +++ python/test/annotator/er/entity_ruler_test.py | 23 +++ .../nlp/annotators/DocumentNormalizer.scala | 189 ++++++------------ .../cleaners/util/CleanerHelper.scala | 13 ++ .../nlp/annotators/er/EntityRulerModel.scala | 162 ++++++++------- .../DocumentNormalizerBehaviors.scala | 84 +++++++- .../cleaners/util/CleanerHelperTestSpec.scala | 20 ++ .../annotators/er/EntityRulerModelTest.scala | 64 +++--- 10 files changed, 405 insertions(+), 246 deletions(-) rename python/test/annotator/{document_normalizer_spec.py => document_normalizer_test.py} (78%) diff --git a/python/sparknlp/annotator/document_normalizer.py b/python/sparknlp/annotator/document_normalizer.py index 5ef5b7671db85f..00cbef41743e08 100755 --- a/python/sparknlp/annotator/document_normalizer.py +++ b/python/sparknlp/annotator/document_normalizer.py @@ -122,6 +122,21 @@ class DocumentNormalizer(AnnotatorModel): "file encoding to apply on normalized documents", typeConverter=TypeConverters.toString) + presetPattern = Param( + Params._dummy(), + "presetPattern", + "Selects a single text cleaning function from the functional presets (e.g., 'CLEAN_BULLETS', 'CLEAN_DASHES', etc.).", + typeConverter=TypeConverters.toString + ) + + autoMode = Param( + Params._dummy(), + "autoMode", + "Enables a predefined cleaning mode combining multiple text cleaner functions (e.g., 'light_clean', 'document_clean', 'html_clean', 'full_auto').", + typeConverter=TypeConverters.toString + ) + + @keyword_only def __init__(self): super(DocumentNormalizer, self).__init__(classname="com.johnsnowlabs.nlp.annotators.DocumentNormalizer") @@ -197,3 +212,24 @@ def setEncoding(self, value): File encoding to apply on normalized documents, by default "UTF-8" """ return self._set(encoding=value) + + def setPresetPattern(self, value): + """Sets a single text cleaning preset pattern. + + Parameters + ---------- + value : str + Preset cleaning pattern name, e.g., 'CLEAN_BULLETS', 'CLEAN_DASHES'. + """ + return self._set(presetPattern=value) + + + def setAutoMode(self, value): + """Sets an automatic text cleaning mode using predefined groups of cleaning functions. + + Parameters + ---------- + value : str + Auto cleaning mode, e.g., 'light_clean', 'document_clean', 'social_clean', 'html_clean', 'full_auto'. + """ + return self._set(autoMode=value) \ No newline at end of file diff --git a/python/sparknlp/annotator/er/entity_ruler.py b/python/sparknlp/annotator/er/entity_ruler.py index daae01cfd74cd6..41ffedb2f8b377 100755 --- a/python/sparknlp/annotator/er/entity_ruler.py +++ b/python/sparknlp/annotator/er/entity_ruler.py @@ -215,6 +215,20 @@ class EntityRulerModel(AnnotatorModel, HasStorageModel): outputAnnotatorType = AnnotatorType.CHUNK + autoMode = Param( + Params._dummy(), + "autoMode", + "Enable built-in regex presets that combine related entity patterns (e.g., 'communication_entities', 'network_entities', 'media_entities', etc.).", + typeConverter=TypeConverters.toString + ) + + extractEntities = Param( + Params._dummy(), + "extractEntities", + "List of entity types to extract. If not set, all entities in the active autoMode or from regexPatterns are used.", + typeConverter=TypeConverters.toListString + ) + def __init__(self, classname="com.johnsnowlabs.nlp.annotators.er.EntityRulerModel", java_model=None): super(EntityRulerModel, self).__init__( classname=classname, @@ -230,3 +244,24 @@ def pretrained(name, lang="en", remote_loc=None): def loadStorage(path, spark, storage_ref): HasStorageModel.loadStorages(path, spark, storage_ref, EntityRulerModel.database) + + def setAutoMode(self, value): + """Sets the auto mode for predefined regex entity groups. + + Parameters + ---------- + value : str + Name of the auto mode to activate (e.g., 'communication_entities', 'network_entities', etc.) + """ + return self._set(autoMode=value) + + + def setExtractEntities(self, value): + """Sets specific entities to extract, filtering only those defined in regexPatterns or autoMode. + + Parameters + ---------- + value : list[str] + List of entity names to extract, e.g., ['EMAIL_ADDRESS_PATTERN', 'IPV4_PATTERN']. + """ + return self._set(extractEntities=value) diff --git a/python/test/annotator/document_normalizer_spec.py b/python/test/annotator/document_normalizer_test.py similarity index 78% rename from python/test/annotator/document_normalizer_spec.py rename to python/test/annotator/document_normalizer_test.py index f5fc1f761d0999..b39d8134750d09 100644 --- a/python/test/annotator/document_normalizer_spec.py +++ b/python/test/annotator/document_normalizer_test.py @@ -80,3 +80,28 @@ def runTest(self): ds.select("normalizedDocument").show() + +@pytest.mark.fast +class DocumentNormalizerAutoModeTestSpec(unittest.TestCase): + + def setUp(self): + self.data = SparkSessionForTest.spark.createDataFrame( + [["• Buy milk & bread!!! 😄 Click - Thanks!"]] + ).toDF("text") + + def runTest(self): + df = self.data + + document_assembler = DocumentAssembler().setInputCol('text').setOutputCol('document') + + document_normalizer = DocumentNormalizer() \ + .setInputCols("document") \ + .setOutputCol("normalizedDocument") \ + .setAutoMode("FULL_AUTO") + + doc_normalizer_pipeline = \ + Pipeline().setStages([document_assembler, document_normalizer]) + + result = doc_normalizer_pipeline.fit(df).transform(df) + + self.assertTrue(result.select("normalizedDocument").count() > 0) \ No newline at end of file diff --git a/python/test/annotator/er/entity_ruler_test.py b/python/test/annotator/er/entity_ruler_test.py index b50195a9b963e5..701666cdf64841 100644 --- a/python/test/annotator/er/entity_ruler_test.py +++ b/python/test/annotator/er/entity_ruler_test.py @@ -85,3 +85,26 @@ def runTest(self): result = light_pipeline.annotate("This is Google's URI http://google.com. And this is Yahoo's URI http://yahoo.com") self.assertTrue(len(result["entity"]) == 2) + +@pytest.mark.fast +class EntityRulerAutoModeTestSpec(unittest.TestCase): + + def setUp(self): + self.data = SparkContextForTest.spark.createDataFrame( + [["This server list includes 192.168.1.1, 10.0.0.45 and 172.16.0.2 for internal routing."]] + ).toDF("text") + + def runTest(self): + document_assembler = DocumentAssembler().setInputCol("text").setOutputCol("document") + tokenizer = Tokenizer().setInputCols("document").setOutputCol("token") + + entity_ruler = EntityRulerModel() \ + .setInputCols(["document", "token"]) \ + .setOutputCol("entity") \ + .setAutoMode("NETWORK_ENTITIES") + + pipeline = Pipeline(stages=[document_assembler, tokenizer, entity_ruler]) + pipeline_model = pipeline.fit(self.data) + result = pipeline_model.transform(self.data) + + self.assertTrue(result.select("entity").count() > 0) diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala index b310acd5d4f64d..b403763ce72f78 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizer.scala @@ -24,6 +24,7 @@ import org.apache.spark.ml.util.{DefaultParamsReadable, Identifiable} import org.slf4j.{Logger, LoggerFactory} import java.nio.charset.{Charset, StandardCharsets} +import java.util.Locale import scala.collection.mutable.ListBuffer import scala.util.matching.Regex import scala.util.{Failure, Success, Try} @@ -105,7 +106,7 @@ import scala.xml.XML */ class DocumentNormalizer(override val uid: String) extends AnnotatorModel[DocumentNormalizer] - with HasSimpleAnnotate[DocumentNormalizer]{ + with HasSimpleAnnotate[DocumentNormalizer] { private val logger: Logger = LoggerFactory.getLogger(this.getClass) @@ -180,10 +181,11 @@ class DocumentNormalizer(override val uid: String) val presetPattern = new Param[String]( this, "presetPattern", - "Single functional cleaner preset (CLEAN_BULLETS, CLEAN_DASHES, etc.)" - ) + "Single functional cleaner preset (CLEAN_BULLETS, CLEAN_DASHES, etc.)") - val autoMode = new Param[String](this, "autoMode", + val autoMode = new Param[String]( + this, + "autoMode", "Automatic cleaning mode grouping multiple cleaners: light_clean, document_clean, social_clean, html_clean, full_auto") // Assuming non-html does not contain any < or > and that input string is correctly structured @@ -194,7 +196,8 @@ class DocumentNormalizer(override val uid: String) replacement -> SPACE_STR, lowercase -> false, policy -> "pretty_all", - encoding -> "disable") + encoding -> "disable", + autoMode -> "") /** Action to perform on text. (Default `"clean"`). * @@ -272,6 +275,8 @@ class DocumentNormalizer(override val uid: String) def setPresetPattern(value: String): this.type = set(presetPattern, value) + def setAutoMode(value: String): this.type = set(autoMode, value) + /** Applying document normalization without pretty formatting (removing multiple spaces) */ private def withAllFormatter( text: String, @@ -368,130 +373,64 @@ class DocumentNormalizer(override val uid: String) new String(text.getBytes(defaultCharset), encoding) } - /** Apply document normalization on text using action, patterns, policy, lowercase and encoding - * parameters. + /** Applies document normalization: + * 1. User-defined regex patterns (if any) 2. Either functional preset (CleanerHelper) OR + * autoMode (CleanerHelper bundles) 3. Lowercasing and encoding policy */ -// private def applyDocumentNormalization( -// text: String, -// action: String, -// patterns: Array[String], -// replacement: String, -// policy: String, -// lowercase: Boolean, -// encoding: String): String = { -// -// val builtinPatterns: Array[String] = { -// if (isDefined(presetPattern)) { -// val presetName = $(presetPattern) -// BUILTIN_PATTERNS.getOrElse( -// presetName, -// throw new IllegalArgumentException( -// s"Unknown preset pattern '$presetName'. Valid presets: ${BUILTIN_PATTERNS.keys.mkString(", ")}" -// ) -// ) -// } else { -// Array.empty[String] -// } -// } -// -// val combinedPatterns: Array[String] = { -// val userPatterns = Option(patterns).getOrElse(Array.empty[String]) -// (builtinPatterns ++ userPatterns).distinct -// } -// -// val processedWithActionPatterns: String = policy match { -// case "all" => withAllFormatter(text, action, combinedPatterns, replacement) -// case "pretty_all" => withPrettyAllFormatter(text, action, combinedPatterns, replacement) -// case "first" => withFirstFormatter(text, action, combinedPatterns, replacement) -// case "pretty_first" => withPrettyFirstFormatter(text, action, combinedPatterns, replacement) -// case _ => -// throw new Exception( -// "Unknown policy parameter in DocumentNormalizer annotation." + -// "Please select either: all, pretty_all, first, or pretty_first") -// } -// -// val cased = -// if (lowercase) -// processedWithActionPatterns.toLowerCase -// else -// processedWithActionPatterns -// -// encoding match { -// case "disable" => cased -// case "UTF-8" => withEncoding(cased, StandardCharsets.UTF_8) -// case "UTF-16" => withEncoding(cased, StandardCharsets.UTF_16) -// case "US-ASCII" => withEncoding(cased, StandardCharsets.US_ASCII) -// case "ISO-8859-1" => withEncoding(cased, StandardCharsets.ISO_8859_1) -// case "UTF-16BE" => withEncoding(cased, StandardCharsets.UTF_16BE) -// case "UTF-16LE" => withEncoding(cased, StandardCharsets.UTF_16LE) -// case _ => -// throw new Exception("Unknown encoding parameter in DocumentNormalizer annotation." + -// "Please select either: disable, UTF_8, UTF_16, US_ASCII, ISO-8859-1, UTF-16BE, UTF-16LE") -// } -// } - - /** - * Applies document normalization: - * 1. User-defined regex patterns (if any) - * 2. Either functional preset (CleanerHelper) OR autoMode (CleanerHelper bundles) - * 3. Lowercasing and encoding policy - */ private def applyDocumentNormalization( - text: String, - action: String, - patterns: Array[String], - replacement: String, - policy: String, - lowercase: Boolean, - encoding: String - ): String = { + text: String, + action: String, + patterns: Array[String], + replacement: String, + policy: String, + lowercase: Boolean, + encoding: String): String = { + val normAutoMode = $(autoMode).toLowerCase(Locale.ROOT) val hasPreset = isDefined(presetPattern) && FUNCTIONAL_PRESETS.contains($(presetPattern)) - val hasAutoMode = isDefined(autoMode) && AUTO_MODE_FUNCTIONS.contains($(autoMode)) - - val selectedCleaner: Either[String => String, Seq[String => String]] = (hasPreset, hasAutoMode) match { - case (true, true) => - logger.warn( - s"[DocumentNormalizer] Both presetPattern (${ $(presetPattern) }) and autoMode (${ $(autoMode) }) are set. " + - s"autoMode will take precedence." - ) - Right(AUTO_MODE_FUNCTIONS($(autoMode))) - case (true, false) => - Left(FUNCTIONAL_PRESETS($(presetPattern))) - case (false, true) => - Right(AUTO_MODE_FUNCTIONS($(autoMode))) - case _ => - Right(Seq.empty) - } + val hasAutoMode = isDefined(autoMode) && AUTO_MODE_FUNCTIONS.contains(normAutoMode) + + val selectedCleaner: Either[String => String, Seq[String => String]] = + (hasPreset, hasAutoMode) match { + case (true, true) => + logger.warn( + s"[DocumentNormalizer] Both presetPattern (${$(presetPattern)}) and autoMode (${normAutoMode}) are set. " + + s"autoMode will take precedence.") + Right(AUTO_MODE_FUNCTIONS(normAutoMode)) + case (true, false) => + Left(FUNCTIONAL_PRESETS($(presetPattern))) + case (false, true) => + Right(AUTO_MODE_FUNCTIONS(normAutoMode)) + case _ => + Right(Seq.empty) + } val userPatterns = Option(patterns).getOrElse(Array.empty[String]) val regexCleanedText: String = if (userPatterns.nonEmpty) { policy match { - case "all" => withAllFormatter(text, action, userPatterns, replacement) - case "pretty_all" => withPrettyAllFormatter(text, action, userPatterns, replacement) - case "first" => withFirstFormatter(text, action, userPatterns, replacement) + case "all" => withAllFormatter(text, action, userPatterns, replacement) + case "pretty_all" => withPrettyAllFormatter(text, action, userPatterns, replacement) + case "first" => withFirstFormatter(text, action, userPatterns, replacement) case "pretty_first" => withPrettyFirstFormatter(text, action, userPatterns, replacement) case _ => throw new Exception( "Unknown policy parameter in DocumentNormalizer. " + - "Valid options: all, pretty_all, first, pretty_first." - ) + "Valid options: all, pretty_all, first, pretty_first.") } } else text val cleanedText: String = selectedCleaner match { case Left(fn) => - logger.info(s"[DocumentNormalizer] Applying preset cleaner: ${ $(presetPattern) }") + logger.info(s"[DocumentNormalizer] Applying preset cleaner: ${$(presetPattern)}") fn(regexCleanedText) case Right(funcs) if funcs.nonEmpty => - val modeName = $(autoMode) + val modeName = normAutoMode val functionNames = funcs.flatMap(FUNCTION_NAME_LOOKUP.get) logger.info( s"[DocumentNormalizer] AutoMode '$modeName' active. Applying cleaners in order: " + - functionNames.mkString(", ") - ) + functionNames.mkString(", ")) funcs.foldLeft(regexCleanedText) { (acc, cleanerFn) => cleanerFn(acc) } case _ => @@ -501,18 +440,16 @@ class DocumentNormalizer(override val uid: String) val casedText = if (lowercase) cleanedText.toLowerCase else cleanedText encoding match { - case "disable" => casedText - case "UTF-8" => withEncoding(casedText, StandardCharsets.UTF_8) - case "UTF-16" => withEncoding(casedText, StandardCharsets.UTF_16) - case "US-ASCII" => withEncoding(casedText, StandardCharsets.US_ASCII) + case "disable" => casedText + case "UTF-8" => withEncoding(casedText, StandardCharsets.UTF_8) + case "UTF-16" => withEncoding(casedText, StandardCharsets.UTF_16) + case "US-ASCII" => withEncoding(casedText, StandardCharsets.US_ASCII) case "ISO-8859-1" => withEncoding(casedText, StandardCharsets.ISO_8859_1) - case "UTF-16BE" => withEncoding(casedText, StandardCharsets.UTF_16BE) - case "UTF-16LE" => withEncoding(casedText, StandardCharsets.UTF_16LE) + case "UTF-16BE" => withEncoding(casedText, StandardCharsets.UTF_16BE) + case "UTF-16LE" => withEncoding(casedText, StandardCharsets.UTF_16LE) case other => - throw new Exception( - s"Unknown encoding parameter: $other. " + - "Please select one of disable, UTF-8, UTF-16, US-ASCII, ISO-8859-1, UTF-16BE, UTF-16LE." - ) + throw new Exception(s"Unknown encoding parameter: $other. " + + "Please select one of disable, UTF-8, UTF-16, US-ASCII, ISO-8859-1, UTF-16BE, UTF-16LE.") } } @@ -524,34 +461,30 @@ class DocumentNormalizer(override val uid: String) "CLEAN_EXTRA_WHITESPACE" -> CleanerHelper.cleanExtraWhitespace, "REMOVE_PUNCTUATION" -> CleanerHelper.removePunctuation, "CLEAN_NON_ASCII" -> CleanerHelper.cleanNonAsciiChars, - "REPLACE_UNICODE" -> CleanerHelper.replaceUnicodeCharacters - ) + "REPLACE_UNICODE" -> CleanerHelper.replaceUnicodeCharacters) private lazy val AUTO_MODE_FUNCTIONS: Map[String, Seq[String => String]] = Map( "light_clean" -> Seq( CleanerHelper.cleanExtraWhitespace, - CleanerHelper.cleanTrailingPunctuation - ), + CleanerHelper.cleanTrailingPunctuation), "document_clean" -> Seq( CleanerHelper.cleanBullets, CleanerHelper.cleanOrderedBullets, CleanerHelper.cleanDashes, - CleanerHelper.cleanExtraWhitespace - ), + CleanerHelper.cleanExtraWhitespace), "social_clean" -> Seq( CleanerHelper.removePunctuation, CleanerHelper.cleanDashes, - CleanerHelper.cleanExtraWhitespace - ), + CleanerHelper.cleanExtraWhitespace), "html_clean" -> Seq( CleanerHelper.replaceUnicodeCharacters, - CleanerHelper.cleanNonAsciiChars - ), - "full_auto" -> FUNCTIONAL_PRESETS.values.toSeq - ) + CleanerHelper.cleanNonAsciiChars, + CleanerHelper.decodeHtmlEntities), + "full_auto" -> FUNCTIONAL_PRESETS.values.toSeq) // Reverse-map functions to preset names (for logging) - private lazy val FUNCTION_NAME_LOOKUP: Map[String => String, String] = FUNCTIONAL_PRESETS.map(_.swap) + private lazy val FUNCTION_NAME_LOOKUP: Map[String => String, String] = + FUNCTIONAL_PRESETS.map(_.swap) override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = annotations.map { annotation => diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelper.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelper.scala index a6c68f7b0a7a5d..a506f2912b24b4 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelper.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelper.scala @@ -237,4 +237,17 @@ object CleanerHelper { new String(textBytes, Charset.forName(formattedEncoding)) } + def decodeHtmlEntities(text: String): String = { + text + .replaceAll("&", "&") + .replaceAll("<", "<") + .replaceAll(">", ">") + .replaceAll(""", "\"") + .replaceAll("'", "'") + .replaceAll("'", "'") + .replaceAll("'", "'") + .replaceAll("©", "©") + .replaceAll(" ", " ") + } + } diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala index 1b12394b42844e..68fac62f04f46b 100644 --- a/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala +++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModel.scala @@ -18,7 +18,12 @@ package com.johnsnowlabs.nlp.annotators.er import com.johnsnowlabs.nlp.AnnotatorType.{CHUNK, DOCUMENT, TOKEN} import com.johnsnowlabs.nlp.annotators.common._ -import com.johnsnowlabs.nlp.annotators.er.EntityRulerModel.{AUTO_MODES, ENTITY_PRESETS, describeAutoMode, getPatternByName} +import com.johnsnowlabs.nlp.annotators.er.EntityRulerModel.{ + AUTO_MODES, + ENTITY_PRESETS, + describeAutoMode, + getPatternByName +} import com.johnsnowlabs.nlp.serialization.StructFeature import com.johnsnowlabs.nlp.{Annotation, AnnotatorModel, HasPretrained, HasSimpleAnnotate} import com.johnsnowlabs.storage.Database.{ENTITY_REGEX_PATTERNS, Name} @@ -88,20 +93,18 @@ class EntityRulerModel(override val uid: String) private[er] val ahoCorasickAutomaton: StructFeature[Option[AhoCorasickAutomaton]] = new StructFeature[Option[AhoCorasickAutomaton]](this, "AhoCorasickAutomaton") - val extractEntities = new StringArrayParam( + private[er] val extractEntities = new StringArrayParam( this, "extractEntities", "List of entity labels to extract (empty = extract all entities)") - /** - * AutoMode defines logical bundles of regex patterns to activate (e.g., "network_entities", "email_entities"). - * When set, overrides activeEntities selection. - */ + /** AutoMode defines logical bundles of regex patterns to activate (e.g., "network_entities", + * "email_entities"). When set, overrides activeEntities selection. + */ private[er] val autoMode: Param[String] = new Param[String]( this, "autoMode", - "Predefined bundle of entity patterns to use (e.g. 'network_entities', 'email_entities', 'all_entities')." - ) + "Predefined bundle of entity patterns to use (e.g. 'network_entities', 'email_entities', 'all_entities').") private[er] def setRegexEntities(value: Array[String]): this.type = set(regexEntities, value) @@ -140,11 +143,12 @@ class EntityRulerModel(override val uid: String) if (automatonModel.isDefined) { Some(automatonModel.get.value) } else { - if (this.get(ahoCorasickAutomaton).isDefined && $$(ahoCorasickAutomaton).isDefined) $$(ahoCorasickAutomaton) else None + if (this.get(ahoCorasickAutomaton).isDefined && $$(ahoCorasickAutomaton).isDefined) + $$(ahoCorasickAutomaton) + else None } } - setDefault( useStorage -> false, sentenceMatch -> false, @@ -152,8 +156,7 @@ class EntityRulerModel(override val uid: String) enablePatternRegex -> false, extractEntities -> Array(), regexEntities -> Array(), - autoMode -> "" - ) + autoMode -> "") /** Annotator reference id. Used to identify elements in metadata or to refer to this annotator * type @@ -186,7 +189,7 @@ class EntityRulerModel(override val uid: String) val validEntities = EntityRulerModel.AUTO_MODES.getOrElse(modeKey, Seq.empty) validEntities.toArray } else { - //Fallback for legacy regex-based pipelines + // Fallback for legacy regex-based pipelines $(regexEntities) } } @@ -198,8 +201,6 @@ class EntityRulerModel(override val uid: String) dataset } - - /** takes a document and annotations and produces new annotations of this annotator's annotation * type * @@ -255,10 +256,9 @@ class EntityRulerModel(override val uid: String) } private def annotateEntitiesFromRegexPatterns( - tokenizedWithSentences: Seq[TokenizedSentence], - regexPatternsReader: Option[RegexPatternsReader], - activeEntities: Array[String] - ): Seq[Annotation] = { + tokenizedWithSentences: Seq[TokenizedSentence], + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String]): Seq[Annotation] = { val annotatedEntities = tokenizedWithSentences.flatMap { tokenizedWithSentence => tokenizedWithSentence.indexedTokens.flatMap { indexedToken => @@ -271,9 +271,7 @@ class EntityRulerModel(override val uid: String) indexedToken.begin, indexedToken.end, indexedToken.token, - entityMetadata ++ Map("sentence" -> tokenizedWithSentence.sentenceIndex.toString) - ) - ) + entityMetadata ++ Map("sentence" -> tokenizedWithSentence.sentenceIndex.toString))) } else { None } @@ -283,12 +281,10 @@ class EntityRulerModel(override val uid: String) annotatedEntities } - private def getMatchedEntity( - token: String, - regexPatternsReader: Option[RegexPatternsReader], - activeEntities: Array[String] - ): Option[String] = { + token: String, + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String]): Option[String] = { val hasActiveEntities = activeEntities != null && activeEntities.nonEmpty @@ -302,8 +298,8 @@ class EntityRulerModel(override val uid: String) case Some(rpr) => { rpr.lookup(regexEntity) } - case None => { - //fallback if entityRulerFeatures is not set + case None => { + // fallback if entityRulerFeatures is not set if (get(entityRulerFeatures).isDefined) $$(entityRulerFeatures).regexPatterns.get(regexEntity) else @@ -313,7 +309,7 @@ class EntityRulerModel(override val uid: String) regexPatterns match { case Some(patterns) => patterns.map(p => regexEntity -> p) - case None => Seq.empty + case None => Seq.empty } } @@ -328,21 +324,18 @@ class EntityRulerModel(override val uid: String) if (matchesByEntity.size > 1) logger.warn( s"[EntityRulerModel] Multiple entities matched token '$token': ${matchesByEntity.mkString(", ")}. " + - s"Returning first entity '${matchesByEntity.head}'." - ) + s"Returning first entity '${matchesByEntity.head}'.") matchesByEntity.headOption } - - /** - * Extracts all regex-matched entities within a sentence, supporting both autoMode and manual patterns. - */ + /** Extracts all regex-matched entities within a sentence, supporting both autoMode and manual + * patterns. + */ private def getMatchedEntityBySentence( - sentence: Sentence, - regexPatternsReader: Option[RegexPatternsReader], - activeEntities: Array[String] - ): Array[(IndexedToken, String)] = { + sentence: Sentence, + regexPatternsReader: Option[RegexPatternsReader], + activeEntities: Array[String]): Array[(IndexedToken, String)] = { import EntityRulerModel._ @@ -355,8 +348,8 @@ class EntityRulerModel(override val uid: String) activeEntities.flatMap { regexEntity => val regexPatterns: Option[Seq[String]] = regexPatternsReader match { case Some(rpr) => rpr.lookup(regexEntity) - case None => { - //fallback if entityRulerFeatures is not set + case None => { + // fallback if entityRulerFeatures is not set if (get(entityRulerFeatures).isDefined) $$(entityRulerFeatures).regexPatterns.get(regexEntity) else @@ -366,7 +359,7 @@ class EntityRulerModel(override val uid: String) regexPatterns match { case Some(patterns) => patterns.map(p => regexEntity -> p) - case None => Seq.empty + case None => Seq.empty } } @@ -374,12 +367,16 @@ class EntityRulerModel(override val uid: String) ENTITY_PRESETS.toSeq } - val matchesByEntity = selectedEntities.flatMap { case (regexEntity, regexPattern) => - val allMatches = regexPattern.r.findAllMatchIn(sentence.content).map { matched => - val begin = matched.start + sentence.start - val end = matched.end + sentence.start - 1 - (matched.matched, begin, end, regexEntity) - }.toSeq + val matchesByEntity = selectedEntities + .flatMap { case (regexEntity, regexPattern) => + val allMatches = regexPattern.r + .findAllMatchIn(sentence.content) + .map { matched => + val begin = matched.start + sentence.start + val end = matched.end + sentence.start - 1 + (matched.matched, begin, end, regexEntity) + } + .toSeq // Merge overlapping intervals for same pattern val intervals = allMatches.map { case (_, b, e, _) => List(b, e) }.toList @@ -389,16 +386,15 @@ class EntityRulerModel(override val uid: String) allMatches.filter { case (_, b, e, _) => mergedIntervals.contains(List(b, e)) } if (filteredMatches.nonEmpty) Some(filteredMatches) else None - }.flatten + } + .flatten .sortBy(_._2) // sort by begin position - matchesByEntity.map { - case (matchedText, begin, end, entityLabel) => - (IndexedToken(matchedText, begin, end), entityLabel) + matchesByEntity.map { case (matchedText, begin, end, entityLabel) => + (IndexedToken(matchedText, begin, end), entityLabel) }.toArray } - private def annotateEntitiesFromRegexPatternsBySentence( sentences: Seq[Sentence], patternsReader: Option[RegexPatternsReader], @@ -482,50 +478,48 @@ trait ReadablePretrainedEntityRuler object EntityRulerModel extends ReadablePretrainedEntityRuler { val EMAIL_DATETIMETZ_PATTERN = "EMAIL_DATETIMETZ_PATTERN" - val EMAIL_ADDRESS_PATTERN = "EMAIL_ADDRESS_PATTERN" - val IPV4_PATTERN = "IPV4_PATTERN" - val IPV6_PATTERN = "IPV6_PATTERN" - val IP_ADDRESS_PATTERN = "IP_ADDRESS_PATTERN" - val IP_ADDRESS_NAME_PATTERN = "IP_ADDRESS_NAME_PATTERN" - val MAPI_ID_PATTERN = "MAPI_ID_PATTERN" + val EMAIL_ADDRESS_PATTERN = "EMAIL_ADDRESS_PATTERN" + val IPV4_PATTERN = "IPV4_PATTERN" + val IPV6_PATTERN = "IPV6_PATTERN" + val IP_ADDRESS_PATTERN = "IP_ADDRESS_PATTERN" + val IP_ADDRESS_NAME_PATTERN = "IP_ADDRESS_NAME_PATTERN" + val MAPI_ID_PATTERN = "MAPI_ID_PATTERN" val US_PHONE_NUMBERS_PATTERN = "US_PHONE_NUMBERS_PATTERN" - val IMAGE_URL_PATTERN = "IMAGE_URL_PATTERN" + val IMAGE_URL_PATTERN = "IMAGE_URL_PATTERN" - val NETWORK_ENTITIES = "NETWORK_ENTITIES" - val EMAIL_ENTITIES = "EMAIL_ENTITIES" - val COMMUNICATION_ENTITIES = "COMMUNICATION_ENTITIES" - val CONTACT_ENTITIES = "CONTACT_ENTITIES" - val MEDIA_ENTITIES = "MEDIA_ENTITIES" - val ALL_ENTITIES = "ALL_ENTITIES" + val NETWORK_ENTITIES = "NETWORK_ENTITIES" + val EMAIL_ENTITIES = "EMAIL_ENTITIES" + val COMMUNICATION_ENTITIES = "COMMUNICATION_ENTITIES" + val CONTACT_ENTITIES = "CONTACT_ENTITIES" + val MEDIA_ENTITIES = "MEDIA_ENTITIES" + val ALL_ENTITIES = "ALL_ENTITIES" private lazy val ENTITY_PRESETS: Map[String, String] = Map( EMAIL_DATETIMETZ_PATTERN -> "[A-Za-z]{3},\\s\\d{1,2}\\s[A-Za-z]{3}\\s\\d{4}\\s\\d{2}:\\d{2}:\\d{2}\\s[+-]\\d{4}", - EMAIL_ADDRESS_PATTERN -> "(?i)[a-z0-9\\.\\-+_]+@[a-z0-9\\.\\-+_]+\\.[a-z]+", - IPV4_PATTERN -> "(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)(?:\\.(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)){3}", - IPV6_PATTERN -> "[a-z0-9]{4}::[a-z0-9]{4}:[a-z0-9]{4}:[a-z0-9]{4}:[a-z0-9]{4}%?[0-9]*", - IP_ADDRESS_NAME_PATTERN -> "[a-zA-Z0-9-]*\\.[a-zA-Z]*\\.[a-zA-Z]*", - MAPI_ID_PATTERN -> "[0-9]*\\.[0-9]*\\.[0-9]*\\.[0-9]*", + EMAIL_ADDRESS_PATTERN -> "(?i)[a-z0-9\\.\\-+_]+@[a-z0-9\\.\\-+_]+\\.[a-z]+", + IPV4_PATTERN -> "(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)(?:\\.(?:25[0-5]|2[0-4]\\d|1\\d{2}|[1-9]?\\d)){3}", + IPV6_PATTERN -> "[a-z0-9]{4}::[a-z0-9]{4}:[a-z0-9]{4}:[a-z0-9]{4}:[a-z0-9]{4}%?[0-9]*", + IP_ADDRESS_NAME_PATTERN -> "[a-zA-Z0-9-]*\\.[a-zA-Z]*\\.[a-zA-Z]*", + MAPI_ID_PATTERN -> "[0-9]*\\.[0-9]*\\.[0-9]*\\.[0-9]*", US_PHONE_NUMBERS_PATTERN -> "(?:\\+?(\\d{1,3}))?[-. (]*(\\d{3})?[-. )]*(\\d{3})[-. ]*(\\d{4})(?: *x(\\d+))?\\s*$", - IMAGE_URL_PATTERN -> "(?i)https?://(?:[a-z0-9$_@.&+!*\\(\\),%-])+(?:/[a-z0-9$_@.&+!*\\(\\),%-]*)*\\.(?:jpg|jpeg|png|gif|bmp|heic)" - ) + IMAGE_URL_PATTERN -> "(?i)https?://(?:[a-z0-9$_@.&+!*\\(\\),%-])+(?:/[a-z0-9$_@.&+!*\\(\\),%-]*)*\\.(?:jpg|jpeg|png|gif|bmp|heic)") private lazy val AUTO_MODES: Map[String, Seq[String]] = Map( - NETWORK_ENTITIES.toUpperCase(Locale.ROOT) -> Seq( - IPV4_PATTERN, IPV6_PATTERN, IP_ADDRESS_NAME_PATTERN - ), + NETWORK_ENTITIES + .toUpperCase(Locale.ROOT) -> Seq(IPV4_PATTERN, IPV6_PATTERN, IP_ADDRESS_NAME_PATTERN), EMAIL_ENTITIES.toUpperCase(Locale.ROOT) -> Seq( - EMAIL_ADDRESS_PATTERN, EMAIL_DATETIMETZ_PATTERN, MAPI_ID_PATTERN - ), + EMAIL_ADDRESS_PATTERN, + EMAIL_DATETIMETZ_PATTERN, + MAPI_ID_PATTERN), COMMUNICATION_ENTITIES.toUpperCase(Locale.ROOT) -> Seq( - EMAIL_ADDRESS_PATTERN, US_PHONE_NUMBERS_PATTERN - ), + EMAIL_ADDRESS_PATTERN, + US_PHONE_NUMBERS_PATTERN), CONTACT_ENTITIES.toUpperCase(Locale.ROOT) -> ( Seq(EMAIL_ADDRESS_PATTERN, US_PHONE_NUMBERS_PATTERN) ++ Seq(IPV4_PATTERN, IPV6_PATTERN, IP_ADDRESS_NAME_PATTERN) - ), + ), MEDIA_ENTITIES.toUpperCase(Locale.ROOT) -> Seq(IMAGE_URL_PATTERN), - ALL_ENTITIES.toUpperCase(Locale.ROOT) -> ENTITY_PRESETS.keys.toSeq - ) + ALL_ENTITIES.toUpperCase(Locale.ROOT) -> ENTITY_PRESETS.keys.toSeq) def getPatternByName(name: String): Option[String] = ENTITY_PRESETS.get(name) diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala index e8202cf4db494e..dda175a7526a6a 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/DocumentNormalizerBehaviors.scala @@ -391,8 +391,6 @@ trait DocumentNormalizerBehaviors extends AnyFlatSpec { val data: DataFrame = spark.createDataFrame(List(("Some title!", ""))).toDF("title", "description") - data.show() - val documentAssembler = new DocumentAssembler() .setInputCol("description") .setOutputCol("document") @@ -427,7 +425,76 @@ trait DocumentNormalizerBehaviors extends AnyFlatSpec { docPatternRemoverPipeline.fit(data).transform(data).show(false) } - it should "clean bullets" in { + it should "clean bullets using the CLEAN_BULLETS preset" in { + + val text = "• Buy milk\n- Feed the cat\n* Call mom" + val cleanedText = runPipeline("social_clean", text) + + cleanedText should not include "•" + cleanedText should not include "-" + cleanedText should not include "*" + + cleanedText should include("buy milk") + cleanedText should include("feed the cat") + cleanedText should include("call mom") + + cleanedText.trim.nonEmpty shouldBe true + } + + it should "apply light_clean mode (basic spacing and case only)" in { + val text = "Hello!!! This is a test... " + val cleaned = runPipeline("light_clean", text) + + // Light clean should only remove extra spaces and lowercase + cleaned should include("hello!!! this is a test") + cleaned should not include " " // extra spaces + cleaned.trim.nonEmpty shouldBe true + } + + it should "apply document_clean mode (paragraphs, dashes, bullets)" in { + val text = + """• Introduction + |This - is a document -- with bullets + |and double spaces""".stripMargin + val cleaned = runPipeline("document_clean", text) + + cleaned should include("introduction") + cleaned should not include "•" + cleaned should not include "-" + cleaned should not include "--" + cleaned.trim.nonEmpty shouldBe true + } + + it should "apply html_clean mode (remove tags and entities)" in { + val text = + """

Hello & Welcome!

This is bold text.

""" + val cleaned = runPipeline("html_clean", text) + + cleaned should include("hello") + cleaned should include("welcome") + cleaned should include("bold text") + cleaned should not include "" + cleaned should not include "&" + cleaned should not include "" + } + + it should "apply full_auto mode (combine all normalizations)" in { + val text = + """• Buy milk & bread!!! 😄 Click - Thanks!""" + val cleaned = runPipeline("FULL_AUTO", text) + + cleaned should include("buy milk") + cleaned should include("bread") + cleaned should not include "•" + cleaned should not include "&" + cleaned should not include "" + cleaned.trim.nonEmpty shouldBe true + } + + private def runPipeline(autoMode: String, text: String): String = { val spark = SparkAccessor.spark import spark.implicits._ @@ -435,17 +502,16 @@ trait DocumentNormalizerBehaviors extends AnyFlatSpec { .setInputCol("text") .setOutputCol("document") - val documentNormalizer = new DocumentNormalizer() + val normalizer = new DocumentNormalizer() .setInputCols("document") .setOutputCol("cleanedDocument") - .setPresetPattern("CLEAN_BULLETS") - .setPolicy("pretty_all") + .setAutoMode(autoMode) .setLowercase(true) - val data = Seq("• Buy milk\n- Feed the cat\n* Call mom").toDF("text") - val pipeline = new Pipeline().setStages(Array(documentAssembler, documentNormalizer)) + val data = Seq(text).toDF("text") + val pipeline = new Pipeline().setStages(Array(documentAssembler, normalizer)) val result = pipeline.fit(data).transform(data) - result.selectExpr("cleanedDocument.result").show(false) + result.selectExpr("cleanedDocument.result[0]").as[String].collect().head } } diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelperTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelperTestSpec.scala index 391ada8e06306b..8fdaaba60aeb7f 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelperTestSpec.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/cleaners/util/CleanerHelperTestSpec.scala @@ -24,6 +24,7 @@ import com.johnsnowlabs.nlp.annotators.cleaners.util.CleanerHelper.{ cleanPostfix, cleanPrefix, cleanTrailingPunctuation, + decodeHtmlEntities, removePunctuation, replaceUnicodeCharacters } @@ -346,4 +347,23 @@ class CleanerHelperTestSpec extends AnyFlatSpec { assert(actual == expected) } + "decodeHtmlEntities" should "decode common HTML entities" taggedAs FastTest in { + val inputs = Seq( + "Tom & Jerry are friends.", + "5 < 10 > 3", + "He said "Hello" 'World'", + "Rock 'n' Roll © 2025") + + val expectedOutputs = Seq( + "Tom & Jerry are friends.", + "5 < 10 > 3", + "He said \"Hello\" 'World'", + "Rock 'n' Roll © 2025") + + inputs.zip(expectedOutputs).foreach { case (input, expected) => + val actual = decodeHtmlEntities(input) + assert(actual == expected, s"Expected '$expected' but got '$actual'") + } + } + } diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala index e0fff6a043bb64..e3890eaa80405b 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala @@ -28,17 +28,18 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { import spark.implicits._ private val trainedModel: EntityRulerModel = getTrainedModel - private val basePipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, trainedModel)) - private val sampleData = Seq( - s"Contact $email1 or $email2 from $ip1 or $ip2." - ).toDF("text") + private val basePipeline = + new Pipeline().setStages(Array(documentAssembler, tokenizer, trainedModel)) + private val sampleData = Seq(s"Contact $email1 or $email2 from $ip1 or $ip2.").toDF("text") "EntityRulerModel (default)" should "extract both email and IP entities" in { trainedModel.setExtractEntities(Array()) val resultDf = basePipeline.fit(sampleData).transform(sampleData) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.length == 4) assert(result.contains(email1)) @@ -52,7 +53,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { val resultDf = basePipeline.fit(sampleData).transform(sampleData) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.length == 2) assert(result.contains(email1)) @@ -66,7 +69,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { val resultDf = basePipeline.fit(sampleData).transform(sampleData) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.length == 2) assert(!result.contains(email1)) @@ -80,7 +85,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { val resultDf = basePipeline.fit(sampleData).transform(sampleData) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.length == 4) assert(result.contains(email1)) @@ -99,7 +106,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { val resultDf = autoPipeline.fit(sampleData).transform(sampleData) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.length == 4) @@ -111,7 +120,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { it should "detect only emails and phones with COMMUNICATION_ENTITIES mode" in { val resultDf = runPipeline(EntityRulerModel.COMMUNICATION_ENTITIES) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.contains(email1)) @@ -121,7 +132,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { it should "detect only IPs and hostnames with NETWORK_ENTITIES mode" in { val resultDf = runPipeline(EntityRulerModel.NETWORK_ENTITIES) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.contains(ip1)) @@ -130,7 +143,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { it should "detect both emails, phones, and IPs with CONTACT_ENTITIES mode" in { val resultDf = runPipeline(EntityRulerModel.CONTACT_ENTITIES) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.contains(email1)) @@ -142,7 +157,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { it should "detect everything with ALL_ENTITIES mode" in { val resultDf = runPipeline(EntityRulerModel.ALL_ENTITIES) - val result = AssertAnnotations.getActualResult(resultDf, "entities").flatten + val result = AssertAnnotations + .getActualResult(resultDf, "entities") + .flatten .map(annotation => annotation.result) assert(result.contains(email1)) @@ -166,9 +183,9 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { } it should "detect email addresses using sentence-level regex matching" in { - val data = Seq( - "Please contact us at info@mydomain.com or support@helpdesk.net for assistance." - ).toDF("text") + val data = + Seq("Please contact us at info@mydomain.com or support@helpdesk.net for assistance.").toDF( + "text") val model = new EntityRulerModel() .setInputCols("document", "token") @@ -186,15 +203,15 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { } it should "detect full IP range patterns at sentence-level when using NETWORK_ENTITIES mode" in { - val data = Seq( - "This server list includes 192.168.1.1, 10.0.0.45 and 172.16.0.2 for internal routing." - ).toDF("text") + val data = + Seq("This server list includes 192.168.1.1, 10.0.0.45 and 172.16.0.2 for internal routing.") + .toDF("text") val model = new EntityRulerModel() .setInputCols("document", "token") .setOutputCol("entities") .setAutoMode(EntityRulerModel.NETWORK_ENTITIES) - .setSentenceMatch(true) // ✅ sentence-based regex match + .setSentenceMatch(true) val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, model)) val result = pipeline.fit(data).transform(data) @@ -209,9 +226,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { } it should "handle case-insensitive autoMode with sentenceMatch = true" in { - val data = Seq( - "Reach us via contact@team.io or sales@biz.org — thank you." - ).toDF("text") + val data = Seq("Reach us via contact@team.io or sales@biz.org — thank you.").toDF("text") val model = new EntityRulerModel() .setInputCols("document", "token") @@ -236,8 +251,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { .setPatternsResource( path = "src/test/resources/entity-ruler/email-ip-regex.json", readAs = ReadAs.TEXT, - options = Map("format" -> "JSON") - ) + options = Map("format" -> "JSON")) .setSentenceMatch(false) .setUseStorage(false) From 2e8e3f6113265a8ef1d1d40fece5d5dcd27b39dc Mon Sep 17 00:00:00 2001 From: Devin Ha Date: Mon, 20 Oct 2025 14:41:34 +0200 Subject: [PATCH 06/13] [SPARKNLP-1293] Add FastTest tag to Scala tests --- .../annotators/er/EntityRulerModelTest.scala | 27 ++++++++++--------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala index e3890eaa80405b..38766a8931468e 100644 --- a/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala +++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/er/EntityRulerModelTest.scala @@ -19,6 +19,7 @@ import com.johnsnowlabs.nlp.AssertAnnotations import com.johnsnowlabs.nlp.annotators.SparkSessionTest import com.johnsnowlabs.nlp.annotators.er.EntityRulerFixture.{email1, email2, ip1, ip2} import com.johnsnowlabs.nlp.util.io.ReadAs +import com.johnsnowlabs.tags.FastTest import org.apache.spark.ml.Pipeline import org.apache.spark.sql.DataFrame import org.scalatest.flatspec.AnyFlatSpec @@ -32,7 +33,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { new Pipeline().setStages(Array(documentAssembler, tokenizer, trainedModel)) private val sampleData = Seq(s"Contact $email1 or $email2 from $ip1 or $ip2.").toDF("text") - "EntityRulerModel (default)" should "extract both email and IP entities" in { + "EntityRulerModel (default)" should "extract both email and IP entities" taggedAs FastTest in { trainedModel.setExtractEntities(Array()) val resultDf = basePipeline.fit(sampleData).transform(sampleData) @@ -48,7 +49,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.contains(ip2)) } - "EntityRulerModel (email-only)" should "extract only email addresses" in { + "EntityRulerModel (email-only)" should "extract only email addresses" taggedAs FastTest in { trainedModel.setExtractEntities(Array("EMAIL")) val resultDf = basePipeline.fit(sampleData).transform(sampleData) @@ -64,7 +65,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(!result.contains(ip2)) } - "EntityRulerModel (ip-only)" should "extract only IP addresses" in { + "EntityRulerModel (ip-only)" should "extract only IP addresses" taggedAs FastTest in { trainedModel.setExtractEntities(Array("IP_ADDRESS")) val resultDf = basePipeline.fit(sampleData).transform(sampleData) @@ -80,7 +81,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.contains(ip2)) } - "EntityRulerModel (email+ip)" should "extract both emails and IPs when both requested" in { + "EntityRulerModel (email+ip)" should "extract both emails and IPs when both requested" taggedAs FastTest in { trainedModel.setExtractEntities(Array("EMAIL", "IP_ADDRESS")) val resultDf = basePipeline.fit(sampleData).transform(sampleData) @@ -96,7 +97,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.contains(ip2)) } - "EntityRulerModel (autoMode='contact_entities')" should "extract emails" in { + "EntityRulerModel (autoMode='contact_entities')" should "extract emails" taggedAs FastTest in { val autoModel = new EntityRulerModel() .setInputCols("document", "token") .setOutputCol("entities") @@ -118,7 +119,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.contains(ip2)) } - it should "detect only emails and phones with COMMUNICATION_ENTITIES mode" in { + it should "detect only emails and phones with COMMUNICATION_ENTITIES mode" taggedAs FastTest in { val resultDf = runPipeline(EntityRulerModel.COMMUNICATION_ENTITIES) val result = AssertAnnotations .getActualResult(resultDf, "entities") @@ -130,7 +131,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.length == 2) } - it should "detect only IPs and hostnames with NETWORK_ENTITIES mode" in { + it should "detect only IPs and hostnames with NETWORK_ENTITIES mode" taggedAs FastTest in { val resultDf = runPipeline(EntityRulerModel.NETWORK_ENTITIES) val result = AssertAnnotations .getActualResult(resultDf, "entities") @@ -141,7 +142,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.contains(ip2)) } - it should "detect both emails, phones, and IPs with CONTACT_ENTITIES mode" in { + it should "detect both emails, phones, and IPs with CONTACT_ENTITIES mode" taggedAs FastTest in { val resultDf = runPipeline(EntityRulerModel.CONTACT_ENTITIES) val result = AssertAnnotations .getActualResult(resultDf, "entities") @@ -155,7 +156,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.length >= 4) } - it should "detect everything with ALL_ENTITIES mode" in { + it should "detect everything with ALL_ENTITIES mode" taggedAs FastTest in { val resultDf = runPipeline(EntityRulerModel.ALL_ENTITIES) val result = AssertAnnotations .getActualResult(resultDf, "entities") @@ -169,7 +170,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(result.length >= 4) } - it should "handle case-insensitive autoMode values" in { + it should "handle case-insensitive autoMode values" taggedAs FastTest in { val mixedCaseModel = runPipeline("Contact_Entities") val upperCaseModel = runPipeline("CONTACT_ENTITIES") val lowerCaseModel = runPipeline("contact_entities") @@ -182,7 +183,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(e2.sameElements(e3)) } - it should "detect email addresses using sentence-level regex matching" in { + it should "detect email addresses using sentence-level regex matching" taggedAs FastTest in { val data = Seq("Please contact us at info@mydomain.com or support@helpdesk.net for assistance.").toDF( "text") @@ -202,7 +203,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(entities.length == 2) } - it should "detect full IP range patterns at sentence-level when using NETWORK_ENTITIES mode" in { + it should "detect full IP range patterns at sentence-level when using NETWORK_ENTITIES mode" taggedAs FastTest in { val data = Seq("This server list includes 192.168.1.1, 10.0.0.45 and 172.16.0.2 for internal routing.") .toDF("text") @@ -225,7 +226,7 @@ class EntityRulerModelTest extends AnyFlatSpec with SparkSessionTest { assert(entities.length == 3) } - it should "handle case-insensitive autoMode with sentenceMatch = true" in { + it should "handle case-insensitive autoMode with sentenceMatch = true" taggedAs FastTest in { val data = Seq("Reach us via contact@team.io or sales@biz.org — thank you.").toDF("text") val model = new EntityRulerModel() From 61ef922ea50212ad88dbf45d9fa232e375a21942 Mon Sep 17 00:00:00 2001 From: Danilo Burbano Date: Fri, 17 Oct 2025 20:32:21 -0500 Subject: [PATCH 07/13] [SPARKNLP-1299] Add Hierarchical Element Identification to HTMLReader --- .../com/johnsnowlabs/reader/HTMLReader.scala | 56 +++++++++----- .../resources/reader/html/simple-book.html | 64 ++++++++++++++++ .../johnsnowlabs/reader/HTMLReaderTest.scala | 74 +++++++++++++++++++ 3 files changed, 176 insertions(+), 18 deletions(-) create mode 100644 src/test/resources/reader/html/simple-book.html diff --git a/src/main/scala/com/johnsnowlabs/reader/HTMLReader.scala b/src/main/scala/com/johnsnowlabs/reader/HTMLReader.scala index 3d1862104fa08c..e66e68e9546069 100644 --- a/src/main/scala/com/johnsnowlabs/reader/HTMLReader.scala +++ b/src/main/scala/com/johnsnowlabs/reader/HTMLReader.scala @@ -25,6 +25,7 @@ import org.apache.spark.sql.functions.{col, udf} import org.jsoup.Jsoup import org.jsoup.nodes.{Document, Element, Node, TextNode} +import java.util.UUID import scala.collection.JavaConverters._ import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -205,6 +206,11 @@ class HTMLReader( val trackingNodes = mutable.Map[Node, NodeMetadata]() var pageNumber = 1 + // Track parent-child hierarchy + var currentParentId: Option[String] = None + + def newUUID(): String = UUID.randomUUID().toString + def isNodeHidden(node: Node): Boolean = { node match { case elem: Element => @@ -237,7 +243,6 @@ class HTMLReader( trackingNodes(elem).visited = true val text = elem.ownText().trim if (text.nonEmpty) textBuffer += text - // Recursively collect text from all child nodes elem.childNodes().asScala.foreach(traverseAndCollect) case _ => // Ignore other node types @@ -245,7 +250,6 @@ class HTMLReader( } } - // Start traversal for each node in the list nodes.foreach(traverseAndCollect) textBuffer.mkString(" ").replaceAll("\\s+", " ").trim } @@ -261,9 +265,7 @@ class HTMLReader( NodeMetadata(tagName = tagName, hidden = isNodeHidden(childNode), visited = false)) } - if (trackingNodes(node).hidden) { - return - } + if (trackingNodes(node).hidden) return node match { case element: Element => @@ -279,48 +281,53 @@ class HTMLReader( val linkText = element.text().trim if (href.nonEmpty && linkText.nonEmpty && !visitedNode) { trackingNodes(element).visited = true + pageMetadata("element_id") = newUUID() + currentParentId.foreach(pid => pageMetadata("parent_id") = pid) elements += HTMLElement( ElementType.LINK, content = s"[$linkText]($href)", metadata = pageMetadata) } + case "table" => pageMetadata("sentence") = sentenceIndex.toString sentenceIndex += 1 val tableContent = outputFormat match { - case "plain-text" => - extractNestedTableContent(element).trim + case "plain-text" => extractNestedTableContent(element).trim case "html-table" => element .outerHtml() .replaceAll("\\n", "") .replaceAll(">\\s+<", "><") .replaceAll("^\\s+|\\s+$", "") - case "json-table" => - tableElementToJson(element) - case _ => - extractNestedTableContent(element).trim + case "json-table" => tableElementToJson(element) + case _ => extractNestedTableContent(element).trim } if (tableContent.nonEmpty && !visitedNode) { trackingNodes(element).visited = true + pageMetadata("element_id") = newUUID() + currentParentId.foreach(pid => pageMetadata("parent_id") = pid) elements += HTMLElement( ElementType.TABLE, content = tableContent, metadata = pageMetadata) } + case "li" => pageMetadata("sentence") = sentenceIndex.toString sentenceIndex += 1 val itemText = element.text().trim if (itemText.nonEmpty && !visitedNode) { trackingNodes(element).visited = true + pageMetadata("element_id") = newUUID() + currentParentId.foreach(pid => pageMetadata("parent_id") = pid) elements += HTMLElement( ElementType.LIST_ITEM, content = itemText, metadata = pageMetadata) } + case "pre" => - // A 
 tag typically contains a  child
               val codeElem = element.getElementsByTag("code").first()
               val codeText =
                 if (codeElem != null) codeElem.text().trim
@@ -329,22 +336,22 @@ class HTMLReader(
                 pageMetadata("sentence") = sentenceIndex.toString
                 sentenceIndex += 1
                 trackingNodes(element).visited = true
+                pageMetadata("element_id") = newUUID()
+                currentParentId.foreach(pid => pageMetadata("parent_id") = pid)
                 elements += HTMLElement(
                   ElementType.UNCATEGORIZED_TEXT,
                   content = codeText,
                   metadata = pageMetadata)
               }
+
             case tag if isParagraphLikeElement(element) =>
               if (!visitedNode) {
                 val classType = classifyParagraphElement(element)
-
-                // Traverse children first so that , , etc. inside the paragraph are processed
                 element.childNodes().asScala.foreach { childNode =>
                   val tagName = getTagName(childNode)
                   traverse(childNode, tagName)
                 }
 
-                // Now handle the paragraph itself
                 classType match {
                   case ElementType.NARRATIVE_TEXT =>
                     val childNodes = element.childNodes().asScala.toList
@@ -353,6 +360,8 @@ class HTMLReader(
                       pageMetadata("sentence") = sentenceIndex.toString
                       sentenceIndex += 1
                       trackingNodes(element).visited = true
+                      pageMetadata("element_id") = newUUID()
+                      currentParentId.foreach(pid => pageMetadata("parent_id") = pid)
                       elements += HTMLElement(
                         ElementType.NARRATIVE_TEXT,
                         content = aggregatedText,
@@ -365,10 +374,13 @@ class HTMLReader(
                       pageMetadata("sentence") = sentenceIndex.toString
                       sentenceIndex += 1
                       trackingNodes(element).visited = true
+                      val titleId = newUUID()
+                      pageMetadata("element_id") = titleId
                       elements += HTMLElement(
                         ElementType.TITLE,
                         content = titleText,
                         metadata = pageMetadata)
+                      currentParentId = Some(titleId)
                     }
 
                   case ElementType.UNCATEGORIZED_TEXT =>
@@ -377,6 +389,8 @@ class HTMLReader(
                       pageMetadata("sentence") = sentenceIndex.toString
                       sentenceIndex += 1
                       trackingNodes(element).visited = true
+                      pageMetadata("element_id") = newUUID()
+                      currentParentId.foreach(pid => pageMetadata("parent_id") = pid)
                       elements += HTMLElement(
                         ElementType.UNCATEGORIZED_TEXT,
                         content = text,
@@ -384,21 +398,27 @@ class HTMLReader(
                     }
                 }
               }
+
             case _ if isTitleElement(element) && !visitedNode =>
               trackingNodes(element).visited = true
               val titleText = element.text().trim
               if (titleText.nonEmpty) {
                 pageMetadata("sentence") = sentenceIndex.toString
                 sentenceIndex += 1
+                val titleId = newUUID()
+                pageMetadata("element_id") = titleId
                 elements += HTMLElement(
                   ElementType.TITLE,
                   content = titleText,
                   metadata = pageMetadata)
+                currentParentId = Some(titleId)
               }
+
             case "hr" =>
               if (element.attr("style").toLowerCase.contains("page-break")) {
                 pageNumber = pageNumber + 1
               }
+
             case "img" =>
               pageMetadata("sentence") = sentenceIndex.toString
               sentenceIndex += 1
@@ -411,7 +431,6 @@ class HTMLReader(
                 val height = element.attr("height").trim
 
                 val imgMetadata = mutable.Map[String, String]("alt" -> alt) ++ pageMetadata
-
                 var contentValue = src
                 if (isBase64) {
                   val commaIndex = src.indexOf(',')
@@ -422,14 +441,16 @@ class HTMLReader(
                     contentValue = base64Payload
                   }
                 }
-
                 if (width.nonEmpty) imgMetadata("width") = width
                 if (height.nonEmpty) imgMetadata("height") = height
+                imgMetadata("element_id") = newUUID()
+                currentParentId.foreach(pid => imgMetadata("parent_id") = pid)
                 elements += HTMLElement(
                   ElementType.IMAGE,
                   content = contentValue,
                   metadata = imgMetadata)
               }
+
             case _ =>
               element.childNodes().asScala.foreach { childNode =>
                 val tagName = getTagName(childNode)
@@ -440,7 +461,6 @@ class HTMLReader(
       }
     }
 
-    // Start traversal from the root node
     val tagName = getTagName(root)
     traverse(root, tagName)
     elements.toArray
diff --git a/src/test/resources/reader/html/simple-book.html b/src/test/resources/reader/html/simple-book.html
new file mode 100644
index 00000000000000..e4a6d11c2cb4a2
--- /dev/null
+++ b/src/test/resources/reader/html/simple-book.html
@@ -0,0 +1,64 @@
+
+
+
+
+  
+  Simple Book: 3 Chapters
+  
+  
+
+
+  
Simple Book

+
+  
+
+  

+
+  

+    
Chapter 1: Beginnings

+    

+      Every project starts with a simple idea and a clear intention. In this chapter, we set the stage and outline the basic goals. 
+      Small steps help build momentum and reduce uncertainty. With a plan in place, moving forward becomes much easier.
+    

+    Back to top
+  

+
+  

+
+  

+    
Chapter 2: Middle Path

+    

+      Progress is rarely a straight line, and that is perfectly fine. Here we adjust our approach based on what we learn. 
+      Iteration helps refine ideas and improves the final outcome. Staying flexible keeps the project healthy and on track.
+    

+    Back to top
+  

+
+  

+
+  

+    
Chapter 3: Finishing Touch

+    

+      The final phase focuses on clarity and polish. We review the work, remove distractions, and keep what matters. 
+      A simple, tidy result is easier to use and maintain. With that, the project is ready to share.
+    

+    Back to top
+  

+
+
\ No newline at end of file
diff --git a/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala b/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala
index 001d284e53fc22..00c374df4163ca 100644
--- a/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala
+++ b/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala
@@ -125,6 +125,7 @@ class HTMLReaderTest extends AnyFlatSpec {
   it should "correctly parse caption and th tags" taggedAs FastTest in {
     val HTMLReader = new HTMLReader()
     val htmlDF = HTMLReader.read(s"$htmlFilesDirectory/example-caption-th.html")
+    htmlDF.show(truncate = false)
     val titleDF = htmlDF
       .select(explode(col("html")).as("exploded_html"))
       .filter(col("exploded_html.elementType") === ElementType.TABLE)
@@ -185,4 +186,77 @@ class HTMLReaderTest extends AnyFlatSpec {
     assert(imagesDF.count() == 1)
   }
 
+  it should "include parent and element ids" taggedAs FastTest in {
+    val HTMLReader = new HTMLReader()
+    val htmlDF = HTMLReader.read(s"$htmlFilesDirectory/simple-book.html")
+    htmlDF.show(truncate = false)
+    val parentChildDF = htmlDF
+      .select(explode(col("html")).as("exploded_html"))
+
+    parentChildDF.show(truncate = false)
+
+//    assert(parentChildDF.count() == 3)
+  }
+
+  it should "produce valid element_id and parent_id relationships" taggedAs FastTest in {
+    val HTMLReader = new HTMLReader()
+    val htmlDF = HTMLReader.read(s"$htmlFilesDirectory/simple-book.html")
+
+    val explodedDF = htmlDF
+      .select(explode(col("html")).as("elem"))
+      .select(
+        col("elem.elementType").as("elementType"),
+        col("elem.content").as("content"),
+        col("elem.metadata").as("metadata")
+      )
+      .withColumn("element_id", col("metadata")("element_id"))
+      .withColumn("parent_id", col("metadata")("parent_id"))
+      .cache() // << important to prevent recomputation inconsistencies
+
+    val allElementIds = explodedDF
+      .select("element_id")
+      .where(col("element_id").isNotNull)
+      .distinct()
+      .collect()
+      .map(_.getString(0))
+      .toSet
+
+    val allParentIds = explodedDF
+      .select("parent_id")
+      .where(col("parent_id").isNotNull)
+      .distinct()
+      .collect()
+      .map(_.getString(0))
+      .toSet
+
+    // 1. There should be at least one element with an element_id
+    assert(allElementIds.nonEmpty, "No elements have element_id metadata")
+
+    // 2. There should be at least one element with a parent_id
+    assert(allParentIds.nonEmpty, "No elements have parent_id metadata")
+
+    // 3. Every parent_id should exist as an element_id
+    val missingParents = allParentIds.diff(allElementIds)
+    assert(
+      missingParents.isEmpty,
+      s"Some parent_ids do not correspond to existing element_ids: $missingParents"
+    )
+
+    // 4. Each parent should have at least one child
+    val parentChildCount = explodedDF
+      .filter(col("parent_id").isNotNull)
+      .groupBy("parent_id")
+      .count()
+      .collect()
+      .map(r => r.getString(0) -> r.getLong(1))
+      .toMap
+
+    assert(
+      parentChildCount.nonEmpty && parentChildCount.values.forall(_ >= 1),
+      "Each parent_id should have at least one child element"
+    )
+  }
+
+
+
 }

From bb259e0775ce62f8e17771fa4e621d15e6e35e33 Mon Sep 17 00:00:00 2001
From: Danilo Burbano 
Date: Sun, 19 Oct 2025 18:48:56 -0500
Subject: [PATCH 08/13] [SPARKNLP-1299] Include metadata to Sentence Detectors

---
 .../nlp/annotators/common/SentenceSplit.scala |  2 +-
 .../sbd/pragmatic/SentenceDetector.scala      | 15 +++----
 .../SentenceDetectorDLModel.scala             |  4 +-
 .../johnsnowlabs/reader/HTMLReaderTest.scala  | 11 ++---
 .../johnsnowlabs/reader/Reader2DocTest.scala  | 41 ++++++++++++++++++-
 5 files changed, 54 insertions(+), 19 deletions(-)

diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/common/SentenceSplit.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/common/SentenceSplit.scala
index 3b4ecc0bb2d0ff..9d778aebaa5311 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/annotators/common/SentenceSplit.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/common/SentenceSplit.scala
@@ -62,7 +62,7 @@ object SentenceSplit extends Annotated[Sentence] {
         item.start,
         item.end,
         item.content,
-        Map("sentence" -> index.toString))
+        item.metadata.getOrElse(Map()) ++ Map("sentence" -> index.toString))
     }
   }
 }
diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/sbd/pragmatic/SentenceDetector.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/sbd/pragmatic/SentenceDetector.scala
index d8451f3c843a60..6738ff02344561 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/annotators/sbd/pragmatic/SentenceDetector.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/sbd/pragmatic/SentenceDetector.scala
@@ -198,13 +198,14 @@ class SentenceDetector(override val uid: String)
     *   document
     */
   override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = {
-    val docs = annotations.map(_.result)
-    val sentences = docs
-      .flatMap(doc => tag(doc))
-      .filter(t =>
-        t.content.nonEmpty && t.content.length >= $(minLength) && get(maxLength).forall(m =>
-          t.content.length <= m))
-    SentenceSplit.pack(sentences)
+    val sentencesWithMetadata = annotations.flatMap { annotation =>
+      tag(annotation.result)
+        .filter(t =>
+          t.content.nonEmpty && t.content.length >= $(minLength) && get(maxLength).forall(m =>
+            t.content.length <= m))
+        .map(sentence => sentence.copy(metadata = Some(annotation.metadata)))
+    }
+    SentenceSplit.pack(sentencesWithMetadata)
   }
 
   override protected def afterAnnotate(dataset: DataFrame): DataFrame = {
diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala
index 9278a1dd65adc8..b286a8e85ab6ad 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/sentence_detector_dl/SentenceDetectorDLModel.scala
@@ -558,7 +558,7 @@ class SentenceDetectorDLModel(override val uid: String)
                 begin = sentenceBegin,
                 end = sentenceBegin + splitSentence.length - 1,
                 result = splitSentence,
-                metadata = mutable.Map("sentence" -> sentenceNo.toString)))
+                metadata = doc.metadata ++ mutable.Map("sentence" -> sentenceNo.toString)))
             sentenceBegin += splitSentence.length
             sentenceNo += 1
           })
@@ -571,7 +571,7 @@ class SentenceDetectorDLModel(override val uid: String)
             begin = doc.begin,
             end = doc.end,
             result = doc.result,
-            metadata = mutable.Map("sentence" -> sentenceNo.toString)))
+            metadata = doc.metadata ++ mutable.Map("sentence" -> sentenceNo.toString)))
       }
     })
 
diff --git a/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala b/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala
index 00c374df4163ca..8097629e94ca52 100644
--- a/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala
+++ b/src/test/scala/com/johnsnowlabs/reader/HTMLReaderTest.scala
@@ -207,8 +207,7 @@ class HTMLReaderTest extends AnyFlatSpec {
       .select(
         col("elem.elementType").as("elementType"),
         col("elem.content").as("content"),
-        col("elem.metadata").as("metadata")
-      )
+        col("elem.metadata").as("metadata"))
       .withColumn("element_id", col("metadata")("element_id"))
       .withColumn("parent_id", col("metadata")("parent_id"))
       .cache() // << important to prevent recomputation inconsistencies
@@ -239,8 +238,7 @@ class HTMLReaderTest extends AnyFlatSpec {
     val missingParents = allParentIds.diff(allElementIds)
     assert(
       missingParents.isEmpty,
-      s"Some parent_ids do not correspond to existing element_ids: $missingParents"
-    )
+      s"Some parent_ids do not correspond to existing element_ids: $missingParents")
 
     // 4. Each parent should have at least one child
     val parentChildCount = explodedDF
@@ -253,10 +251,7 @@ class HTMLReaderTest extends AnyFlatSpec {
 
     assert(
       parentChildCount.nonEmpty && parentChildCount.values.forall(_ >= 1),
-      "Each parent_id should have at least one child element"
-    )
+      "Each parent_id should have at least one child element")
   }
 
-
-
 }
diff --git a/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala b/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala
index 5dbbc9085827fe..c22fd3cf7784e9 100644
--- a/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala
+++ b/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala
@@ -15,12 +15,13 @@
  */
 package com.johnsnowlabs.reader
 
+import com.johnsnowlabs.nlp.annotator.SentenceDetector
 import com.johnsnowlabs.nlp.annotators.SparkSessionTest
 import com.johnsnowlabs.nlp.{Annotation, AssertAnnotations}
 import com.johnsnowlabs.tags.{FastTest, SlowTest}
 import org.apache.hadoop.mapreduce.lib.input.InvalidInputException
-import org.apache.spark.sql.functions.col
 import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions.col
 import org.scalatest.flatspec.AnyFlatSpec
 
 class Reader2DocTest extends AnyFlatSpec with SparkSessionTest {
@@ -441,4 +442,42 @@ class Reader2DocTest extends AnyFlatSpec with SparkSessionTest {
 
     assert(attributeElements.length > 0, "Expected to find attribute elements in the XML content")
   }
+
+  it should "add metadata to sentenceDetector" taggedAs FastTest in {
+    val reader2Doc = new Reader2Doc()
+      .setContentType("text/html")
+      .setContentPath(s"$htmlFilesDirectory/simple-book.html")
+      .setOutputCol("document")
+
+    val pipeline = new Pipeline().setStages(Array(reader2Doc, sentenceDetector))
+
+    val pipelineModel = pipeline.fit(emptyDataSet)
+    val resultDf = pipelineModel.transform(emptyDataSet)
+
+    resultDf.show(truncate = false)
+  }
+
+  it should "add metadata to sentence detector" taggedAs FastTest in {
+    val reader2Doc = new Reader2Doc()
+      .setContentType("text/html")
+      .setContentPath(s"$htmlFilesDirectory/simple-book.html")
+      .setOutputCol("document")
+
+    val sentenceDetectorDL = new SentenceDetector()
+      .setInputCols("document")
+      .setOutputCol("sentence")
+
+    val pipeline = new Pipeline().setStages(Array(reader2Doc, sentenceDetectorDL))
+
+    val pipelineModel = pipeline.fit(emptyDataSet)
+    val resultDf = pipelineModel.transform(emptyDataSet)
+
+    val annotationsResult = AssertAnnotations.getActualResult(resultDf, "sentence")
+    val attributeElements = annotationsResult.flatMap { annotations =>
+      annotations.filter(ann => ann.metadata.contains("element_id"))
+    }
+
+    assert(attributeElements.length > 0, "Expected to find attribute elements in the HTML content")
+  }
+
 }

From 67a4810a38158237b16130fc3ac81c72d35e51e9 Mon Sep 17 00:00:00 2001
From: Danilo Burbano 
Date: Mon, 20 Oct 2025 08:26:04 -0500
Subject: [PATCH 09/13] [SPARKNLP-1299] Adding python test

---
 python/test/reader/reader2doc_test.py         | 35 ++++++++++++++++++-
 .../johnsnowlabs/reader/Reader2DocTest.scala  |  4 +--
 2 files changed, 36 insertions(+), 3 deletions(-)

diff --git a/python/test/reader/reader2doc_test.py b/python/test/reader/reader2doc_test.py
index aace3ce9b35d36..b11ceb9378c8f9 100644
--- a/python/test/reader/reader2doc_test.py
+++ b/python/test/reader/reader2doc_test.py
@@ -18,6 +18,7 @@
 
 import pytest
 from pyspark.ml import Pipeline
+from pyspark.sql.functions import explode
 
 from sparknlp.annotator import *
 from sparknlp.reader.reader2doc import Reader2Doc
@@ -131,4 +132,36 @@ def runTest(self):
 
         result_df = model.transform(self.html_df)
 
-        self.assertTrue(result_df.select("document").count() > 0)
\ No newline at end of file
+        self.assertTrue(result_df.select("document").count() > 0)
+
+@pytest.mark.fast
+class Reader2DocTestHierarchy(unittest.TestCase):
+
+    def setUp(self):
+        spark = SparkContextForTest.spark
+        self.empty_df = spark.createDataFrame([], "string").toDF("text")
+
+    def runTest(self):
+        reader2doc = Reader2Doc() \
+            .setContentType("text/html") \
+            .setContentPath(f"file:///{os.getcwd()}/../src/test/resources/reader/html/simple-book.html") \
+            .setOutputCol("document")
+
+        sentence_detector = SentenceDetector() \
+            .setInputCols(["document"]) \
+            .setOutputCol("sentence")
+
+        pipeline = Pipeline(stages=[reader2doc, sentence_detector])
+        model = pipeline.fit(self.empty_df)
+
+        result_df = model.transform(self.empty_df)
+        rows = result_df.select("sentence").collect()
+
+        all_sentences = [elem for row in rows for elem in row.sentence]
+
+        # Check for required metadata keys
+        for s in all_sentences:
+            metadata = s.metadata
+            assert (
+                    "element_id" in metadata or "parent_id" in metadata
+            ), f"❌ Missing 'element_id' or 'parent_id' in metadata: {metadata}"
\ No newline at end of file
diff --git a/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala b/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala
index c22fd3cf7784e9..5b742f941d2522 100644
--- a/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala
+++ b/src/test/scala/com/johnsnowlabs/reader/Reader2DocTest.scala
@@ -463,11 +463,11 @@ class Reader2DocTest extends AnyFlatSpec with SparkSessionTest {
       .setContentPath(s"$htmlFilesDirectory/simple-book.html")
       .setOutputCol("document")
 
-    val sentenceDetectorDL = new SentenceDetector()
+    val sentenceDetector = new SentenceDetector()
       .setInputCols("document")
       .setOutputCol("sentence")
 
-    val pipeline = new Pipeline().setStages(Array(reader2Doc, sentenceDetectorDL))
+    val pipeline = new Pipeline().setStages(Array(reader2Doc, sentenceDetector))
 
     val pipelineModel = pipeline.fit(emptyDataSet)
     val resultDf = pipelineModel.transform(emptyDataSet)

From 427de3761dd96c61a52601d55941072a3bb91247 Mon Sep 17 00:00:00 2001
From: ahmedlone127 
Date: Tue, 21 Oct 2025 18:39:30 +0500
Subject: [PATCH 10/13] [SPARKNLP-1300] changing token sequence in warmup test
 (#14677)

* changing token sequence in warmup test

* updating warmup token sequence
---
 src/main/scala/com/johnsnowlabs/ml/ai/RoBerta.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/RoBerta.scala b/src/main/scala/com/johnsnowlabs/ml/ai/RoBerta.scala
index 4550c1ed0146be..2e824a57dd469f 100644
--- a/src/main/scala/com/johnsnowlabs/ml/ai/RoBerta.scala
+++ b/src/main/scala/com/johnsnowlabs/ml/ai/RoBerta.scala
@@ -70,7 +70,7 @@ private[johnsnowlabs] class RoBerta(
 
   private def sessionWarmup(): Unit = {
     val dummyInput =
-      Array(0, 7939, 18, 3279, 658, 5, 19374, 13, 5, 78, 42752, 4, 2)
+      Array(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2)
     if (modelArch == ModelArch.wordEmbeddings) {
       tag(Seq(dummyInput))
     } else if (modelArch == ModelArch.sentenceEmbeddings) {

From 1f5f51f23a779ac8ec88ee39df6b710c79c96d46 Mon Sep 17 00:00:00 2001
From: Abdullah mubeen <77073730+AbdullahMubeenAnwar@users.noreply.github.com>
Date: Tue, 21 Oct 2025 19:48:03 +0500
Subject: [PATCH 11/13] [SPARKNLP-1149] Update documentation (#14673)

* Update documentation

* Update README.md sparknlp -v 6.1.2 --> 6.1.5

* Update Gemfile.lock

versions i used to run locally

* Update dependencies for docs frontend and plugins

Updated Ruby gem versions in docs/Gemfile and docs/Gemfile.lock, including nokogiri and webrick. Added package-lock.json for docs/_frontend, updated dependencies in docs/_frontend/package.json, and updated package.json files for jquery-scrollTo and lightbox plugins. These changes improve dependency management and ensure compatibility with newer versions.

* Add Reader2Doc, Reader2Image, Reader2Table, and ReaderAssembler docs

Added documentation entries for Reader2Doc, Reader2Image, Reader2Table, and ReaderAssembler annotators, including usage examples and supported parameters. Updated the main annotators list to include these new components with brief summaries.

* Updating Reader2Table doc

---------

Co-authored-by: Danilo Burbano 
---
 README.md                                     |    2 +-
 docs/Gemfile                                  |    6 +-
 docs/Gemfile.lock                             |  132 +-
 docs/_config.yml                              |    3 +-
 docs/_frontend/package-lock.json              | 8966 +++++++++++++++++
 docs/_frontend/package.json                   |    3 +-
 docs/_frontend/yarn.lock                      | 2992 +++---
 .../plugins/jquery-scrollTo/package.json      |    2 +-
 docs/assets/plugins/lightbox/package.json     |    2 +-
 docs/en/annotator_entries/Reader2Doc.md       |  107 +
 docs/en/annotator_entries/Reader2Image.md     |  113 +
 docs/en/annotator_entries/Reader2Table.md     |  104 +
 docs/en/annotator_entries/ReaderAssembler.md  |  106 +
 docs/en/annotators.md                         |   51 +-
 docs/en/faq.md                                |   90 +-
 docs/en/install.md                            |   89 +-
 docs/en/quickstart.md                         |    2 +-
 docs/en/spark_nlp.md                          |    2 +-
 docs/en/tasks/automatic_speech_recognition.md |   86 +-
 docs/en/tasks/image_captioning.md             |  105 +-
 docs/en/tasks/image_classification.md         |   96 +-
 docs/en/tasks/image_text_to_text.md           |  151 +
 docs/en/tasks/landing_page.md                 |   50 +-
 docs/en/tasks/question_answering.md           |  120 +-
 docs/en/tasks/summarization.md                |  173 +-
 docs/en/tasks/table_question_answering.md     |  239 +-
 docs/en/tasks/text_classification.md          |   79 +-
 docs/en/tasks/text_generation.md              |  193 +-
 docs/en/tasks/text_preprocessing.md           |   94 +-
 docs/en/tasks/token_classification.md         |   79 +-
 docs/en/tasks/translation.md                  |   70 +-
 docs/en/tasks/zero_shot_classification.md     |   97 +-
 .../tasks/zero_shot_image_classification.md   |   90 +-
 .../BLIPForQuestionAnswering.md               |  133 +
 docs/en/transformer_entries/CPMTransformer.md |  135 +
 .../DeBertaForZeroShotClassification.md       |  178 +
 .../transformer_entries/LLAMA2Transformer.md  |  169 +
 .../transformer_entries/LLAMA3Transformer.md  |  153 +
 .../transformer_entries/M2M100Transformer.md  |  130 +
 .../MPNetForTokenClassification.md            |  125 +
 .../transformer_entries/MistralTransformer.md |  115 +
 .../en/transformer_entries/MxbaiEmbeddings.md |  124 +
 .../en/transformer_entries/NLLBTransformer.md |  130 +
 .../en/transformer_entries/NomicEmbeddings.md |  129 +
 .../en/transformer_entries/Phi2Transformer.md |  127 +
 .../en/transformer_entries/Phi3Transformer.md |  125 +
 .../en/transformer_entries/QwenTransformer.md |  142 +
 .../SnowFlakeEmbeddings.md                    |  143 +
 .../StarCoderTransformer.md                   |  128 +
 docs/index.md                                 |    2 +-
 python/README.md                              |    2 +-
 51 files changed, 13907 insertions(+), 2777 deletions(-)
 create mode 100644 docs/_frontend/package-lock.json
 create mode 100644 docs/en/annotator_entries/Reader2Doc.md
 create mode 100644 docs/en/annotator_entries/Reader2Image.md
 create mode 100644 docs/en/annotator_entries/Reader2Table.md
 create mode 100644 docs/en/annotator_entries/ReaderAssembler.md
 create mode 100644 docs/en/tasks/image_text_to_text.md
 create mode 100644 docs/en/transformer_entries/BLIPForQuestionAnswering.md
 create mode 100644 docs/en/transformer_entries/CPMTransformer.md
 create mode 100644 docs/en/transformer_entries/DeBertaForZeroShotClassification.md
 create mode 100644 docs/en/transformer_entries/LLAMA2Transformer.md
 create mode 100644 docs/en/transformer_entries/LLAMA3Transformer.md
 create mode 100644 docs/en/transformer_entries/M2M100Transformer.md
 create mode 100644 docs/en/transformer_entries/MPNetForTokenClassification.md
 create mode 100644 docs/en/transformer_entries/MistralTransformer.md
 create mode 100644 docs/en/transformer_entries/MxbaiEmbeddings.md
 create mode 100644 docs/en/transformer_entries/NLLBTransformer.md
 create mode 100644 docs/en/transformer_entries/NomicEmbeddings.md
 create mode 100644 docs/en/transformer_entries/Phi2Transformer.md
 create mode 100644 docs/en/transformer_entries/Phi3Transformer.md
 create mode 100644 docs/en/transformer_entries/QwenTransformer.md
 create mode 100644 docs/en/transformer_entries/SnowFlakeEmbeddings.md
 create mode 100644 docs/en/transformer_entries/StarCoderTransformer.md

diff --git a/README.md b/README.md
index 22ef0f4f0d4844..d6bb96deb9e73e 100644
--- a/README.md
+++ b/README.md
@@ -267,7 +267,7 @@ Please check [these instructions](https://sparknlp.org/docs/en/install#s3-integr
 Need more **examples**? Check out our dedicated [Spark NLP Examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples)
 repository to showcase all Spark NLP use cases!
 
-Also, don't forget to check [Spark NLP in Action](https://sparknlp.org/demo) built by Streamlit.
+Also, don't forget to check [Spark NLP in Action](https://sparknlp.org/demos) built by Streamlit.
 
 #### All examples: [spark-nlp/examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples)
 
diff --git a/docs/Gemfile b/docs/Gemfile
index 8a4e98f169603c..396fb0958d138f 100644
--- a/docs/Gemfile
+++ b/docs/Gemfile
@@ -1,19 +1,19 @@
 source "https://rubygems.org"
 
 gem "github-pages", "227"
-gem "nokogiri", ">= 1.13.9"
+
+gem "nokogiri", ">= 1.18.9"
 
 gem "elasticsearch", "~> 7.10"
 
 gem 'wdm', '~> 0.1.0'
 
-gem "webrick"
+gem "webrick", ">= 1.8.2"
 
 gem "jekyll", "~> 3.9"
 
 gem "aws-sdk-s3", "~>1"
 
-
 group "jekyll-plugins" do
     gem "jekyll-incremental", "0.1.0", path: "_plugins/jekyll-incremental"
 end
diff --git a/docs/Gemfile.lock b/docs/Gemfile.lock
index 02bbee56ff83ba..7a7cd4bd0cc67b 100644
--- a/docs/Gemfile.lock
+++ b/docs/Gemfile.lock
@@ -13,78 +13,65 @@ GEM
       minitest (~> 5.1)
       tzinfo (~> 1.1)
       zeitwerk (~> 2.2, >= 2.2.2)
-    addressable (2.8.1)
-      public_suffix (>= 2.0.2, < 6.0)
+    addressable (2.8.7)
+      public_suffix (>= 2.0.2, < 7.0)
     aws-eventstream (1.4.0)
-    aws-partitions (1.1126.0)
-    aws-sdk-core (3.226.2)
+    aws-partitions (1.1172.0)
+    aws-sdk-core (3.233.0)
       aws-eventstream (~> 1, >= 1.3.0)
       aws-partitions (~> 1, >= 1.992.0)
       aws-sigv4 (~> 1.9)
       base64
+      bigdecimal
       jmespath (~> 1, >= 1.6.1)
       logger
-    aws-sdk-kms (1.106.0)
-      aws-sdk-core (~> 3, >= 3.225.0)
+    aws-sdk-kms (1.113.0)
+      aws-sdk-core (~> 3, >= 3.231.0)
       aws-sigv4 (~> 1.5)
-    aws-sdk-s3 (1.192.0)
-      aws-sdk-core (~> 3, >= 3.225.0)
+    aws-sdk-s3 (1.199.1)
+      aws-sdk-core (~> 3, >= 3.231.0)
       aws-sdk-kms (~> 1)
       aws-sigv4 (~> 1.5)
     aws-sigv4 (1.12.1)
       aws-eventstream (~> 1, >= 1.0.2)
     base64 (0.3.0)
+    bigdecimal (3.3.1)
     coffee-script (2.4.1)
       coffee-script-source
       execjs
     coffee-script-source (1.11.1)
     colorator (1.1.0)
-    commonmarker (0.23.8)
-    concurrent-ruby (1.2.2)
-    dnsruby (1.61.9)
-      simpleidn (~> 0.1)
-    elasticsearch (7.17.7)
-      elasticsearch-api (= 7.17.7)
-      elasticsearch-transport (= 7.17.7)
-    elasticsearch-api (7.17.7)
+    commonmarker (0.23.12)
+    concurrent-ruby (1.3.5)
+    dnsruby (1.73.0)
+      base64 (>= 0.2)
+      logger (~> 1.6)
+      simpleidn (~> 0.2.1)
+    elasticsearch (7.17.11)
+      elasticsearch-api (= 7.17.11)
+      elasticsearch-transport (= 7.17.11)
+    elasticsearch-api (7.17.11)
       multi_json
-    elasticsearch-transport (7.17.7)
-      faraday (~> 1)
+    elasticsearch-transport (7.17.11)
+      base64
+      faraday (>= 1, < 3)
       multi_json
     em-websocket (0.5.3)
       eventmachine (>= 0.12.9)
       http_parser.rb (~> 0)
-    ethon (0.16.0)
+    ethon (0.17.0)
       ffi (>= 1.15.0)
     eventmachine (1.2.7)
     eventmachine (1.2.7-x64-mingw32)
-    execjs (2.8.1)
-    faraday (1.10.3)
-      faraday-em_http (~> 1.0)
-      faraday-em_synchrony (~> 1.0)
-      faraday-excon (~> 1.1)
-      faraday-httpclient (~> 1.0)
-      faraday-multipart (~> 1.0)
-      faraday-net_http (~> 1.0)
-      faraday-net_http_persistent (~> 1.0)
-      faraday-patron (~> 1.0)
-      faraday-rack (~> 1.0)
-      faraday-retry (~> 1.0)
-      ruby2_keywords (>= 0.0.4)
-    faraday-em_http (1.0.0)
-    faraday-em_synchrony (1.0.0)
-    faraday-excon (1.1.0)
-    faraday-httpclient (1.0.1)
-    faraday-multipart (1.0.4)
-      multipart-post (~> 2)
-    faraday-net_http (1.0.1)
-    faraday-net_http_persistent (1.2.0)
-    faraday-patron (1.0.0)
-    faraday-rack (1.0.0)
-    faraday-retry (1.0.3)
-    ffi (1.15.5)
-    ffi (1.15.5-x64-mingw-ucrt)
-    ffi (1.15.5-x64-mingw32)
+    execjs (2.10.0)
+    faraday (2.14.0)
+      faraday-net_http (>= 2.0, < 3.5)
+      json
+      logger
+    faraday-net_http (3.4.1)
+      net-http (>= 0.5.0)
+    ffi (1.17.2)
+    ffi (1.17.2-arm64-darwin)
     forwardable-extended (2.6.0)
     gemoji (3.0.1)
     github-pages (227)
@@ -253,26 +240,36 @@ GEM
       html-pipeline (~> 2.2)
       jekyll (>= 3.0, < 5.0)
     jmespath (1.6.2)
+    json (2.15.1)
     kramdown (2.3.2)
       rexml
     kramdown-parser-gfm (1.1.0)
       kramdown (~> 2.0)
     liquid (4.0.3)
-    listen (3.8.0)
+    listen (3.9.0)
       rb-fsevent (~> 0.10, >= 0.10.3)
       rb-inotify (~> 0.9, >= 0.9.10)
     logger (1.7.0)
     mercenary (0.3.6)
-    mini_portile2 (2.8.1)
+    mini_portile2 (2.8.9)
     minima (2.5.1)
       jekyll (>= 3.5, < 5.0)
       jekyll-feed (~> 0.9)
       jekyll-seo-tag (~> 2.1)
-    minitest (5.18.0)
-    multi_json (1.15.0)
-    multipart-post (2.3.0)
-    nokogiri (1.14.2)
-      mini_portile2 (~> 2.8.0)
+    minitest (5.26.0)
+    multi_json (1.17.0)
+    net-http (0.6.0)
+      uri
+    nokogiri (1.18.10)
+      mini_portile2 (~> 2.8.2)
+      racc (~> 1.4)
+    nokogiri (1.18.10-arm64-darwin)
+      racc (~> 1.4)
+    nokogiri (1.18.10-x64-mingw-ucrt)
+      racc (~> 1.4)
+    nokogiri (1.18.10-x86_64-darwin)
+      racc (~> 1.4)
+    nokogiri (1.18.10-x86_64-linux-gnu)
       racc (~> 1.4)
     octokit (4.25.1)
       faraday (>= 1, < 3)
@@ -280,14 +277,13 @@ GEM
     pathutil (0.16.2)
       forwardable-extended (~> 2.6)
     public_suffix (4.0.7)
-    racc (1.6.2)
+    racc (1.8.1)
     rb-fsevent (0.11.2)
-    rb-inotify (0.10.1)
+    rb-inotify (0.11.1)
       ffi (~> 1.0)
-    rexml (3.2.5)
+    rexml (3.4.4)
     rouge (3.26.0)
-    ruby2_keywords (0.0.5)
-    rubyzip (2.3.2)
+    rubyzip (2.4.1)
     safe_yaml (1.0.5)
     sass (3.7.4)
       sass-listen (~> 4.0.0)
@@ -297,24 +293,22 @@ GEM
     sawyer (0.9.2)
       addressable (>= 2.3.5)
       faraday (>= 0.17.3, < 3)
-    simpleidn (0.2.1)
-      unf (~> 0.1.4)
+    simpleidn (0.2.3)
     terminal-table (1.8.0)
       unicode-display_width (~> 1.1, >= 1.1.1)
     thread_safe (0.3.6)
-    typhoeus (1.4.0)
+    typhoeus (1.4.1)
       ethon (>= 0.9.0)
     tzinfo (1.2.11)
       thread_safe (~> 0.1)
-    unf (0.1.4)
-      unf_ext
-    unf_ext (0.0.8.2)
     unicode-display_width (1.8.0)
+    uri (1.0.4)
     wdm (0.1.1)
-    webrick (1.8.1)
-    zeitwerk (2.6.7)
+    webrick (1.9.1)
+    zeitwerk (2.6.18)
 
 PLATFORMS
+  arm64-darwin-24
   x64-mingw-ucrt
   x64-mingw32
   x86_64-darwin-21
@@ -327,9 +321,9 @@ DEPENDENCIES
   github-pages (= 227)
   jekyll (~> 3.9)
   jekyll-incremental (= 0.1.0)!
-  nokogiri (>= 1.13.9)
+  nokogiri (>= 1.18.9)
   wdm (~> 0.1.0)
-  webrick
+  webrick (>= 1.8.2)
 
 BUNDLED WITH
-   2.3.24
+   2.3.26
diff --git a/docs/_config.yml b/docs/_config.yml
index f0bd2aed2d53f8..8ebdb84d0da1c7 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -104,7 +104,8 @@ paginate_path: /page:num # don't change this unless for special need
 
 ## => Sources
 ##############################
-sources: bootcdn # bootcdn (default), unpkg
+# sources: bootcdn # bootcdn (default), unpkg
+sources: unpkg # was bootcdn
 
 
 ## => Sharing
diff --git a/docs/_frontend/package-lock.json b/docs/_frontend/package-lock.json
new file mode 100644
index 00000000000000..2e5dd707be8825
--- /dev/null
+++ b/docs/_frontend/package-lock.json
@@ -0,0 +1,8966 @@
+{
+  "name": "frontend",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "frontend",
+      "version": "1.0.0",
+      "license": "UNLICENSED",
+      "dependencies": {
+        "@floating-ui/react-dom": "^0.7.1",
+        "@floating-ui/react-dom-interactions": "^0.6.3",
+        "classnames": "^2.3.1",
+        "downshift": "^6.1.7",
+        "http-proxy-middleware": "^2.0.9",
+        "preact": "^10.7.0",
+        "react": "17",
+        "react-dom": "17"
+      },
+      "devDependencies": {
+        "@babel/core": "^7.17.8",
+        "@babel/preset-env": "^7.16.11",
+        "@babel/preset-react": "^7.16.7",
+        "babel-loader": "^8.2.4",
+        "css-loader": "^6.7.1",
+        "css-minimizer-webpack-plugin": "^3.4.1",
+        "mini-css-extract-plugin": "^2.6.0",
+        "postcss-loader": "^6.2.1",
+        "postcss-preset-env": "^7.4.3",
+        "style-loader": "^3.3.1",
+        "terser-webpack-plugin": "^5.3.1",
+        "webpack": "^5.70.0",
+        "webpack-cli": "^4.9.2",
+        "webpack-dev-server": "^5.2.2"
+      }
+    },
+    "node_modules/@ampproject/remapping": {
+      "version": "2.1.2",
+      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.1.2.tgz",
+      "integrity": "sha512-hoyByceqwKirw7w3Z7gnIIZC3Wx3J484Y3L/cMpXFbr7d9ZQj2mODrirNzcJa+SM3UlpWXYvKV4RlRpFXlWgXg==",
+      "dev": true,
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@jridgewell/trace-mapping": "^0.3.0"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.27.1",
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+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
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+        "js-tokens": "^4.0.0",
+        "picocolors": "^1.1.1"
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+      "dev": true,
+      "license": "MIT",
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+      "dev": true,
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+        "@babel/code-frame": "^7.16.7",
+        "@babel/generator": "^7.17.7",
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+        "@babel/types": "^7.17.0",
+        "convert-source-map": "^1.7.0",
+        "debug": "^4.1.0",
+        "gensync": "^1.0.0-beta.2",
+        "json5": "^2.1.2",
+        "semver": "^6.3.0"
+      },
+      "engines": {
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+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/babel"
+      }
+    },
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+      "license": "MIT",
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+      "license": "MIT",
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+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/webpack/node_modules/schema-utils": {
+      "version": "4.3.3",
+      "resolved": "https://registry.npmjs.org/schema-utils/-/schema-utils-4.3.3.tgz",
+      "integrity": "sha512-eflK8wEtyOE6+hsaRVPxvUKYCpRgzLqDTb8krvAsRIwOGlHoSgYLgBXoubGgLd2fT41/OUYdb48v4k4WWHQurA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@types/json-schema": "^7.0.9",
+        "ajv": "^8.9.0",
+        "ajv-formats": "^2.1.1",
+        "ajv-keywords": "^5.1.0"
+      },
+      "engines": {
+        "node": ">= 10.13.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/webpack"
+      }
+    },
+    "node_modules/websocket-driver": {
+      "version": "0.7.4",
+      "resolved": "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz",
+      "integrity": "sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg==",
+      "dev": true,
+      "license": "Apache-2.0",
+      "dependencies": {
+        "http-parser-js": ">=0.5.1",
+        "safe-buffer": ">=5.1.0",
+        "websocket-extensions": ">=0.1.1"
+      },
+      "engines": {
+        "node": ">=0.8.0"
+      }
+    },
+    "node_modules/websocket-extensions": {
+      "version": "0.1.4",
+      "resolved": "https://registry.npmjs.org/websocket-extensions/-/websocket-extensions-0.1.4.tgz",
+      "integrity": "sha512-OqedPIGOfsDlo31UNwYbCFMSaO9m9G/0faIHj5/dZFDMFqPTcx6UwqyOy3COEaEOg/9VsGIpdqn62W5KhoKSpg==",
+      "dev": true,
+      "license": "Apache-2.0",
+      "engines": {
+        "node": ">=0.8.0"
+      }
+    },
+    "node_modules/which": {
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
+      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
+      "dev": true,
+      "license": "ISC",
+      "dependencies": {
+        "isexe": "^2.0.0"
+      },
+      "bin": {
+        "node-which": "bin/node-which"
+      },
+      "engines": {
+        "node": ">= 8"
+      }
+    },
+    "node_modules/wildcard": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/wildcard/-/wildcard-2.0.0.tgz",
+      "integrity": "sha512-JcKqAHLPxcdb9KM49dufGXn2x3ssnfjbcaQdLlfZsL9rH9wgDQjUtDxbo8NE0F6SFvydeu1VhZe7hZuHsB2/pw==",
+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/ws": {
+      "version": "8.18.3",
+      "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz",
+      "integrity": "sha512-PEIGCY5tSlUt50cqyMXfCzX+oOPqN0vuGqWzbcJ2xvnkzkq46oOpz7dQaTDBdfICb4N14+GARUDw2XV2N4tvzg==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=10.0.0"
+      },
+      "peerDependencies": {
+        "bufferutil": "^4.0.1",
+        "utf-8-validate": ">=5.0.2"
+      },
+      "peerDependenciesMeta": {
+        "bufferutil": {
+          "optional": true
+        },
+        "utf-8-validate": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/wsl-utils": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/wsl-utils/-/wsl-utils-0.1.0.tgz",
+      "integrity": "sha512-h3Fbisa2nKGPxCpm89Hk33lBLsnaGBvctQopaBSOW/uIs6FTe1ATyAnKFJrzVs9vpGdsTe73WF3V4lIsk4Gacw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "is-wsl": "^3.1.0"
+      },
+      "engines": {
+        "node": ">=18"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/yaml": {
+      "version": "1.10.2",
+      "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz",
+      "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
+      "dev": true,
+      "license": "ISC",
+      "engines": {
+        "node": ">= 6"
+      }
+    }
+  }
+}
diff --git a/docs/_frontend/package.json b/docs/_frontend/package.json
index 3a0575571a38b2..9d099c807fb070 100644
--- a/docs/_frontend/package.json
+++ b/docs/_frontend/package.json
@@ -21,13 +21,14 @@
     "terser-webpack-plugin": "^5.3.1",
     "webpack": "^5.70.0",
     "webpack-cli": "^4.9.2",
-    "webpack-dev-server": "^4.7.4"
+    "webpack-dev-server": "^5.2.2"
   },
   "dependencies": {
     "@floating-ui/react-dom": "^0.7.1",
     "@floating-ui/react-dom-interactions": "^0.6.3",
     "classnames": "^2.3.1",
     "downshift": "^6.1.7",
+    "http-proxy-middleware": "^2.0.9",
     "preact": "^10.7.0",
     "react": "17",
     "react-dom": "17"
diff --git a/docs/_frontend/yarn.lock b/docs/_frontend/yarn.lock
index ee4db779bb9955..34d22d753221e8 100644
--- a/docs/_frontend/yarn.lock
+++ b/docs/_frontend/yarn.lock
@@ -4,38 +4,28 @@
 
 "@ampproject/remapping@^2.1.0":
   version "2.1.2"
-  resolved "https://registry.yarnpkg.com/@ampproject/remapping/-/remapping-2.1.2.tgz#4edca94973ded9630d20101cd8559cedb8d8bd34"
+  resolved "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.1.2.tgz"
   integrity sha512-hoyByceqwKirw7w3Z7gnIIZC3Wx3J484Y3L/cMpXFbr7d9ZQj2mODrirNzcJa+SM3UlpWXYvKV4RlRpFXlWgXg==
   dependencies:
     "@jridgewell/trace-mapping" "^0.3.0"
 
-"@babel/code-frame@^7.0.0", "@babel/code-frame@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.16.0.tgz"
-  integrity sha512-IF4EOMEV+bfYwOmNxGzSnjR2EmQod7f1UXOpZM3l4i4o4QNwzjtJAu/HxdjHq0aYBvdqMuQEY1eg0nqW9ZPORA==
-  dependencies:
-    "@babel/highlight" "^7.16.0"
-
-"@babel/code-frame@^7.16.7":
-  version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.16.7.tgz#44416b6bd7624b998f5b1af5d470856c40138789"
-  integrity sha512-iAXqUn8IIeBTNd72xsFlgaXHkMBMt6y4HJp1tIaK465CWLT/fG1aqB7ykr95gHHmlBdGbFeWWfyB4NJJ0nmeIg==
+"@babel/code-frame@^7.0.0", "@babel/code-frame@^7.16.7", "@babel/code-frame@^7.27.1":
+  version "7.27.1"
+  resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz"
+  integrity sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==
   dependencies:
-    "@babel/highlight" "^7.16.7"
-
-"@babel/compat-data@^7.13.11", "@babel/compat-data@^7.16.0":
-  version "7.16.4"
-  resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.16.4.tgz"
-  integrity sha512-1o/jo7D+kC9ZjHX5v+EHrdjl3PhxMrLSOTGsOdHJ+KL8HCaEK6ehrVL2RS6oHDZp+L7xLirLrPmQtEng769J/Q==
+    "@babel/helper-validator-identifier" "^7.27.1"
+    js-tokens "^4.0.0"
+    picocolors "^1.1.1"
 
-"@babel/compat-data@^7.16.8", "@babel/compat-data@^7.17.0", "@babel/compat-data@^7.17.7":
+"@babel/compat-data@^7.13.11", "@babel/compat-data@^7.16.8", "@babel/compat-data@^7.17.0", "@babel/compat-data@^7.17.7":
   version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/compat-data/-/compat-data-7.17.7.tgz#078d8b833fbbcc95286613be8c716cef2b519fa2"
+  resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.17.7.tgz"
   integrity sha512-p8pdE6j0a29TNGebNm7NzYZWB3xVZJBZ7XGs42uAKzQo8VQ3F0By/cQCtUEABwIqw5zo6WA4NbmxsfzADzMKnQ==
 
-"@babel/core@^7.17.8":
+"@babel/core@^7.0.0", "@babel/core@^7.0.0-0", "@babel/core@^7.12.0", "@babel/core@^7.13.0", "@babel/core@^7.17.8", "@babel/core@^7.4.0-0":
   version "7.17.8"
-  resolved "https://registry.yarnpkg.com/@babel/core/-/core-7.17.8.tgz#3dac27c190ebc3a4381110d46c80e77efe172e1a"
+  resolved "https://registry.npmjs.org/@babel/core/-/core-7.17.8.tgz"
   integrity sha512-OdQDV/7cRBtJHLSOBqqbYNkOcydOgnX59TZx4puf41fzcVtN3e/4yqY8lMQsK+5X2lJtAdmA+6OHqsj1hBJ4IQ==
   dependencies:
     "@ampproject/remapping" "^2.1.0"
@@ -54,59 +44,35 @@
     json5 "^2.1.2"
     semver "^6.3.0"
 
-"@babel/generator@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/generator/-/generator-7.16.0.tgz"
-  integrity sha512-RR8hUCfRQn9j9RPKEVXo9LiwoxLPYn6hNZlvUOR8tSnaxlD0p0+la00ZP9/SnRt6HchKr+X0fO2r8vrETiJGew==
+"@babel/generator@^7.17.7", "@babel/generator@^7.28.3":
+  version "7.28.3"
+  resolved "https://registry.npmjs.org/@babel/generator/-/generator-7.28.3.tgz"
+  integrity sha512-3lSpxGgvnmZznmBkCRnVREPUFJv2wrv9iAoFDvADJc0ypmdOxdUtcLeBgBJ6zE0PMeTKnxeQzyk0xTBq4Ep7zw==
   dependencies:
-    "@babel/types" "^7.16.0"
-    jsesc "^2.5.1"
-    source-map "^0.5.0"
-
-"@babel/generator@^7.17.3", "@babel/generator@^7.17.7":
-  version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.17.7.tgz#8da2599beb4a86194a3b24df6c085931d9ee45ad"
-  integrity sha512-oLcVCTeIFadUoArDTwpluncplrYBmTCCZZgXCbgNGvOBBiSDDK3eWO4b/+eOTli5tKv1lg+a5/NAXg+nTcei1w==
-  dependencies:
-    "@babel/types" "^7.17.0"
-    jsesc "^2.5.1"
-    source-map "^0.5.0"
-
-"@babel/helper-annotate-as-pure@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/helper-annotate-as-pure/-/helper-annotate-as-pure-7.16.0.tgz"
-  integrity sha512-ItmYF9vR4zA8cByDocY05o0LGUkp1zhbTQOH1NFyl5xXEqlTJQCEJjieriw+aFpxo16swMxUnUiKS7a/r4vtHg==
-  dependencies:
-    "@babel/types" "^7.16.0"
+    "@babel/parser" "^7.28.3"
+    "@babel/types" "^7.28.2"
+    "@jridgewell/gen-mapping" "^0.3.12"
+    "@jridgewell/trace-mapping" "^0.3.28"
+    jsesc "^3.0.2"
 
 "@babel/helper-annotate-as-pure@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-annotate-as-pure/-/helper-annotate-as-pure-7.16.7.tgz#bb2339a7534a9c128e3102024c60760a3a7f3862"
+  resolved "https://registry.npmjs.org/@babel/helper-annotate-as-pure/-/helper-annotate-as-pure-7.16.7.tgz"
   integrity sha512-s6t2w/IPQVTAET1HitoowRGXooX8mCgtuP5195wD/QJPV6wYjpujCGF7JuMODVX2ZAJOf1GT6DT9MHEZvLOFSw==
   dependencies:
     "@babel/types" "^7.16.7"
 
 "@babel/helper-builder-binary-assignment-operator-visitor@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-builder-binary-assignment-operator-visitor/-/helper-builder-binary-assignment-operator-visitor-7.16.7.tgz#38d138561ea207f0f69eb1626a418e4f7e6a580b"
+  resolved "https://registry.npmjs.org/@babel/helper-builder-binary-assignment-operator-visitor/-/helper-builder-binary-assignment-operator-visitor-7.16.7.tgz"
   integrity sha512-C6FdbRaxYjwVu/geKW4ZeQ0Q31AftgRcdSnZ5/jsH6BzCJbtvXvhpfkbkThYSuutZA7nCXpPR6AD9zd1dprMkA==
   dependencies:
     "@babel/helper-explode-assignable-expression" "^7.16.7"
     "@babel/types" "^7.16.7"
 
-"@babel/helper-compilation-targets@^7.13.0":
-  version "7.16.3"
-  resolved "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.16.3.tgz"
-  integrity sha512-vKsoSQAyBmxS35JUOOt+07cLc6Nk/2ljLIHwmq2/NM6hdioUaqEXq/S+nXvbvXbZkNDlWOymPanJGOc4CBjSJA==
-  dependencies:
-    "@babel/compat-data" "^7.16.0"
-    "@babel/helper-validator-option" "^7.14.5"
-    browserslist "^4.17.5"
-    semver "^6.3.0"
-
-"@babel/helper-compilation-targets@^7.16.7", "@babel/helper-compilation-targets@^7.17.7":
+"@babel/helper-compilation-targets@^7.13.0", "@babel/helper-compilation-targets@^7.16.7", "@babel/helper-compilation-targets@^7.17.7":
   version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-compilation-targets/-/helper-compilation-targets-7.17.7.tgz#a3c2924f5e5f0379b356d4cfb313d1414dc30e46"
+  resolved "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.17.7.tgz"
   integrity sha512-UFzlz2jjd8kroj0hmCFV5zr+tQPi1dpC2cRsDV/3IEW8bJfCPrPpmcSN6ZS8RqIq4LXcmpipCQFPddyFA5Yc7w==
   dependencies:
     "@babel/compat-data" "^7.17.7"
@@ -116,7 +82,7 @@
 
 "@babel/helper-create-class-features-plugin@^7.16.10", "@babel/helper-create-class-features-plugin@^7.16.7", "@babel/helper-create-class-features-plugin@^7.17.6":
   version "7.17.6"
-  resolved "https://registry.yarnpkg.com/@babel/helper-create-class-features-plugin/-/helper-create-class-features-plugin-7.17.6.tgz#3778c1ed09a7f3e65e6d6e0f6fbfcc53809d92c9"
+  resolved "https://registry.npmjs.org/@babel/helper-create-class-features-plugin/-/helper-create-class-features-plugin-7.17.6.tgz"
   integrity sha512-SogLLSxXm2OkBbSsHZMM4tUi8fUzjs63AT/d0YQIzr6GSd8Hxsbk2KYDX0k0DweAzGMj/YWeiCsorIdtdcW8Eg==
   dependencies:
     "@babel/helper-annotate-as-pure" "^7.16.7"
@@ -127,39 +93,17 @@
     "@babel/helper-replace-supers" "^7.16.7"
     "@babel/helper-split-export-declaration" "^7.16.7"
 
-"@babel/helper-create-regexp-features-plugin@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/helper-create-regexp-features-plugin/-/helper-create-regexp-features-plugin-7.16.0.tgz"
-  integrity sha512-3DyG0zAFAZKcOp7aVr33ddwkxJ0Z0Jr5V99y3I690eYLpukJsJvAbzTy1ewoCqsML8SbIrjH14Jc/nSQ4TvNPA==
-  dependencies:
-    "@babel/helper-annotate-as-pure" "^7.16.0"
-    regexpu-core "^4.7.1"
-
 "@babel/helper-create-regexp-features-plugin@^7.16.7":
   version "7.17.0"
-  resolved "https://registry.yarnpkg.com/@babel/helper-create-regexp-features-plugin/-/helper-create-regexp-features-plugin-7.17.0.tgz#1dcc7d40ba0c6b6b25618997c5dbfd310f186fe1"
+  resolved "https://registry.npmjs.org/@babel/helper-create-regexp-features-plugin/-/helper-create-regexp-features-plugin-7.17.0.tgz"
   integrity sha512-awO2So99wG6KnlE+TPs6rn83gCz5WlEePJDTnLEqbchMVrBeAujURVphRdigsk094VhvZehFoNOihSlcBjwsXA==
   dependencies:
     "@babel/helper-annotate-as-pure" "^7.16.7"
     regexpu-core "^5.0.1"
 
-"@babel/helper-define-polyfill-provider@^0.3.0":
-  version "0.3.0"
-  resolved "https://registry.npmjs.org/@babel/helper-define-polyfill-provider/-/helper-define-polyfill-provider-0.3.0.tgz"
-  integrity sha512-7hfT8lUljl/tM3h+izTX/pO3W3frz2ok6Pk+gzys8iJqDfZrZy2pXjRTZAvG2YmfHun1X4q8/UZRLatMfqc5Tg==
-  dependencies:
-    "@babel/helper-compilation-targets" "^7.13.0"
-    "@babel/helper-module-imports" "^7.12.13"
-    "@babel/helper-plugin-utils" "^7.13.0"
-    "@babel/traverse" "^7.13.0"
-    debug "^4.1.1"
-    lodash.debounce "^4.0.8"
-    resolve "^1.14.2"
-    semver "^6.1.2"
-
-"@babel/helper-define-polyfill-provider@^0.3.1":
+"@babel/helper-define-polyfill-provider@^0.3.0", "@babel/helper-define-polyfill-provider@^0.3.1":
   version "0.3.1"
-  resolved "https://registry.yarnpkg.com/@babel/helper-define-polyfill-provider/-/helper-define-polyfill-provider-0.3.1.tgz#52411b445bdb2e676869e5a74960d2d3826d2665"
+  resolved "https://registry.npmjs.org/@babel/helper-define-polyfill-provider/-/helper-define-polyfill-provider-0.3.1.tgz"
   integrity sha512-J9hGMpJQmtWmj46B3kBHmL38UhJGhYX7eqkcq+2gsstyYt341HmPeWspihX43yVRA0mS+8GGk2Gckc7bY/HCmA==
   dependencies:
     "@babel/helper-compilation-targets" "^7.13.0"
@@ -173,88 +117,63 @@
 
 "@babel/helper-environment-visitor@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-environment-visitor/-/helper-environment-visitor-7.16.7.tgz#ff484094a839bde9d89cd63cba017d7aae80ecd7"
+  resolved "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.16.7.tgz"
   integrity sha512-SLLb0AAn6PkUeAfKJCCOl9e1R53pQlGAfc4y4XuMRZfqeMYLE0dM1LMhqbGAlGQY0lfw5/ohoYWAe9V1yibRag==
   dependencies:
     "@babel/types" "^7.16.7"
 
 "@babel/helper-explode-assignable-expression@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-explode-assignable-expression/-/helper-explode-assignable-expression-7.16.7.tgz#12a6d8522fdd834f194e868af6354e8650242b7a"
+  resolved "https://registry.npmjs.org/@babel/helper-explode-assignable-expression/-/helper-explode-assignable-expression-7.16.7.tgz"
   integrity sha512-KyUenhWMC8VrxzkGP0Jizjo4/Zx+1nNZhgocs+gLzyZyB8SHidhoq9KK/8Ato4anhwsivfkBLftky7gvzbZMtQ==
   dependencies:
     "@babel/types" "^7.16.7"
 
-"@babel/helper-function-name@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.16.0.tgz"
-  integrity sha512-BZh4mEk1xi2h4HFjWUXRQX5AEx4rvaZxHgax9gcjdLWdkjsY7MKt5p0otjsg5noXw+pB+clMCjw+aEVYADMjog==
-  dependencies:
-    "@babel/helper-get-function-arity" "^7.16.0"
-    "@babel/template" "^7.16.0"
-    "@babel/types" "^7.16.0"
-
 "@babel/helper-function-name@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-function-name/-/helper-function-name-7.16.7.tgz#f1ec51551fb1c8956bc8dd95f38523b6cf375f8f"
+  resolved "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.16.7.tgz"
   integrity sha512-QfDfEnIUyyBSR3HtrtGECuZ6DAyCkYFp7GHl75vFtTnn6pjKeK0T1DB5lLkFvBea8MdaiUABx3osbgLyInoejA==
   dependencies:
     "@babel/helper-get-function-arity" "^7.16.7"
     "@babel/template" "^7.16.7"
     "@babel/types" "^7.16.7"
 
-"@babel/helper-get-function-arity@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/helper-get-function-arity/-/helper-get-function-arity-7.16.0.tgz"
-  integrity sha512-ASCquNcywC1NkYh/z7Cgp3w31YW8aojjYIlNg4VeJiHkqyP4AzIvr4qx7pYDb4/s8YcsZWqqOSxgkvjUz1kpDQ==
-  dependencies:
-    "@babel/types" "^7.16.0"
-
 "@babel/helper-get-function-arity@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-get-function-arity/-/helper-get-function-arity-7.16.7.tgz#ea08ac753117a669f1508ba06ebcc49156387419"
+  resolved "https://registry.npmjs.org/@babel/helper-get-function-arity/-/helper-get-function-arity-7.16.7.tgz"
   integrity sha512-flc+RLSOBXzNzVhcLu6ujeHUrD6tANAOU5ojrRx/as+tbzf8+stUCj7+IfRRoAbEZqj/ahXEMsjhOhgeZsrnTw==
   dependencies:
     "@babel/types" "^7.16.7"
 
-"@babel/helper-hoist-variables@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.16.0.tgz"
-  integrity sha512-1AZlpazjUR0EQZQv3sgRNfM9mEVWPK3M6vlalczA+EECcPz3XPh6VplbErL5UoMpChhSck5wAJHthlj1bYpcmg==
-  dependencies:
-    "@babel/types" "^7.16.0"
+"@babel/helper-globals@^7.28.0":
+  version "7.28.0"
+  resolved "https://registry.npmjs.org/@babel/helper-globals/-/helper-globals-7.28.0.tgz"
+  integrity sha512-+W6cISkXFa1jXsDEdYA8HeevQT/FULhxzR99pxphltZcVaugps53THCeiWA8SguxxpSp3gKPiuYfSWopkLQ4hw==
 
 "@babel/helper-hoist-variables@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-hoist-variables/-/helper-hoist-variables-7.16.7.tgz#86bcb19a77a509c7b77d0e22323ef588fa58c246"
+  resolved "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.16.7.tgz"
   integrity sha512-m04d/0Op34H5v7pbZw6pSKP7weA6lsMvfiIAMeIvkY/R4xQtBSMFEigu9QTZ2qB/9l22vsxtM8a+Q8CzD255fg==
   dependencies:
     "@babel/types" "^7.16.7"
 
 "@babel/helper-member-expression-to-functions@^7.16.7":
   version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.17.7.tgz#a34013b57d8542a8c4ff8ba3f747c02452a4d8c4"
+  resolved "https://registry.npmjs.org/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.17.7.tgz"
   integrity sha512-thxXgnQ8qQ11W2wVUObIqDL4p148VMxkt5T/qpN5k2fboRyzFGFmKsTGViquyM5QHKUy48OZoca8kw4ajaDPyw==
   dependencies:
     "@babel/types" "^7.17.0"
 
-"@babel/helper-module-imports@^7.12.13":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.16.0.tgz"
-  integrity sha512-kkH7sWzKPq0xt3H1n+ghb4xEMP8k0U7XV3kkB+ZGy69kDk2ySFW1qPi06sjKzFY3t1j6XbJSqr4mF9L7CYVyhg==
-  dependencies:
-    "@babel/types" "^7.16.0"
-
-"@babel/helper-module-imports@^7.16.7":
+"@babel/helper-module-imports@^7.12.13", "@babel/helper-module-imports@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-module-imports/-/helper-module-imports-7.16.7.tgz#25612a8091a999704461c8a222d0efec5d091437"
+  resolved "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.16.7.tgz"
   integrity sha512-LVtS6TqjJHFc+nYeITRo6VLXve70xmq7wPhWTqDJusJEgGmkAACWwMiTNrvfoQo6hEhFwAIixNkvB0jPXDL8Wg==
   dependencies:
     "@babel/types" "^7.16.7"
 
 "@babel/helper-module-transforms@^7.16.7", "@babel/helper-module-transforms@^7.17.7":
   version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-module-transforms/-/helper-module-transforms-7.17.7.tgz#3943c7f777139e7954a5355c815263741a9c1cbd"
+  resolved "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.17.7.tgz"
   integrity sha512-VmZD99F3gNTYB7fJRDTi+u6l/zxY0BE6OIxPSU7a50s6ZUQkHwSDmV92FfM+oCG0pZRVojGYhkR8I0OGeCVREw==
   dependencies:
     "@babel/helper-environment-visitor" "^7.16.7"
@@ -268,24 +187,19 @@
 
 "@babel/helper-optimise-call-expression@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-optimise-call-expression/-/helper-optimise-call-expression-7.16.7.tgz#a34e3560605abbd31a18546bd2aad3e6d9a174f2"
+  resolved "https://registry.npmjs.org/@babel/helper-optimise-call-expression/-/helper-optimise-call-expression-7.16.7.tgz"
   integrity sha512-EtgBhg7rd/JcnpZFXpBy0ze1YRfdm7BnBX4uKMBd3ixa3RGAE002JZB66FJyNH7g0F38U05pXmA5P8cBh7z+1w==
   dependencies:
     "@babel/types" "^7.16.7"
 
-"@babel/helper-plugin-utils@^7.0.0", "@babel/helper-plugin-utils@^7.10.4", "@babel/helper-plugin-utils@^7.12.13", "@babel/helper-plugin-utils@^7.13.0", "@babel/helper-plugin-utils@^7.14.5", "@babel/helper-plugin-utils@^7.8.0", "@babel/helper-plugin-utils@^7.8.3":
-  version "7.14.5"
-  resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.14.5.tgz"
-  integrity sha512-/37qQCE3K0vvZKwoK4XU/irIJQdIfCJuhU5eKnNxpFDsOkgFaUAwbv+RYw6eYgsC0E4hS7r5KqGULUogqui0fQ==
-
-"@babel/helper-plugin-utils@^7.16.7":
+"@babel/helper-plugin-utils@^7.0.0", "@babel/helper-plugin-utils@^7.10.4", "@babel/helper-plugin-utils@^7.12.13", "@babel/helper-plugin-utils@^7.13.0", "@babel/helper-plugin-utils@^7.14.5", "@babel/helper-plugin-utils@^7.16.7", "@babel/helper-plugin-utils@^7.8.0", "@babel/helper-plugin-utils@^7.8.3":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-plugin-utils/-/helper-plugin-utils-7.16.7.tgz#aa3a8ab4c3cceff8e65eb9e73d87dc4ff320b2f5"
+  resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.16.7.tgz"
   integrity sha512-Qg3Nk7ZxpgMrsox6HreY1ZNKdBq7K72tDSliA6dCl5f007jR4ne8iD5UzuNnCJH2xBf2BEEVGr+/OL6Gdp7RxA==
 
 "@babel/helper-remap-async-to-generator@^7.16.8":
   version "7.16.8"
-  resolved "https://registry.yarnpkg.com/@babel/helper-remap-async-to-generator/-/helper-remap-async-to-generator-7.16.8.tgz#29ffaade68a367e2ed09c90901986918d25e57e3"
+  resolved "https://registry.npmjs.org/@babel/helper-remap-async-to-generator/-/helper-remap-async-to-generator-7.16.8.tgz"
   integrity sha512-fm0gH7Flb8H51LqJHy3HJ3wnE1+qtYR2A99K06ahwrawLdOFsCEWjZOrYricXJHoPSudNKxrMBUPEIPxiIIvBw==
   dependencies:
     "@babel/helper-annotate-as-pure" "^7.16.7"
@@ -294,7 +208,7 @@
 
 "@babel/helper-replace-supers@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-replace-supers/-/helper-replace-supers-7.16.7.tgz#e9f5f5f32ac90429c1a4bdec0f231ef0c2838ab1"
+  resolved "https://registry.npmjs.org/@babel/helper-replace-supers/-/helper-replace-supers-7.16.7.tgz"
   integrity sha512-y9vsWilTNaVnVh6xiJfABzsNpgDPKev9HnAgz6Gb1p6UUwf9NepdlsV7VXGCftJM+jqD5f7JIEubcpLjZj5dBw==
   dependencies:
     "@babel/helper-environment-visitor" "^7.16.7"
@@ -305,7 +219,7 @@
 
 "@babel/helper-simple-access@^7.17.7":
   version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-simple-access/-/helper-simple-access-7.17.7.tgz#aaa473de92b7987c6dfa7ce9a7d9674724823367"
+  resolved "https://registry.npmjs.org/@babel/helper-simple-access/-/helper-simple-access-7.17.7.tgz"
   integrity sha512-txyMCGroZ96i+Pxr3Je3lzEJjqwaRC9buMUgtomcrLe5Nd0+fk1h0LLA+ixUF5OW7AhHuQ7Es1WcQJZmZsz2XA==
   dependencies:
     "@babel/types" "^7.17.0"
@@ -317,43 +231,31 @@
   dependencies:
     "@babel/types" "^7.16.0"
 
-"@babel/helper-split-export-declaration@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.16.0.tgz"
-  integrity sha512-0YMMRpuDFNGTHNRiiqJX19GjNXA4H0E8jZ2ibccfSxaCogbm3am5WN/2nQNj0YnQwGWM1J06GOcQ2qnh3+0paw==
-  dependencies:
-    "@babel/types" "^7.16.0"
-
 "@babel/helper-split-export-declaration@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.16.7.tgz#0b648c0c42da9d3920d85ad585f2778620b8726b"
+  resolved "https://registry.npmjs.org/@babel/helper-split-export-declaration/-/helper-split-export-declaration-7.16.7.tgz"
   integrity sha512-xbWoy/PFoxSWazIToT9Sif+jJTlrMcndIsaOKvTA6u7QEo7ilkRZpjew18/W3c7nm8fXdUDXh02VXTbZ0pGDNw==
   dependencies:
     "@babel/types" "^7.16.7"
 
-"@babel/helper-validator-identifier@^7.15.7":
-  version "7.15.7"
-  resolved "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.15.7.tgz"
-  integrity sha512-K4JvCtQqad9OY2+yTU8w+E82ywk/fe+ELNlt1G8z3bVGlZfn/hOcQQsUhGhW/N+tb3fxK800wLtKOE/aM0m72w==
-
-"@babel/helper-validator-identifier@^7.16.7":
-  version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-validator-identifier/-/helper-validator-identifier-7.16.7.tgz#e8c602438c4a8195751243da9031d1607d247cad"
-  integrity sha512-hsEnFemeiW4D08A5gUAZxLBTXpZ39P+a+DGDsHw1yxqyQ/jzFEnxf5uTEGp+3bzAbNOxU1paTgYS4ECU/IgfDw==
+"@babel/helper-string-parser@^7.27.1":
+  version "7.27.1"
+  resolved "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.27.1.tgz"
+  integrity sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA==
 
-"@babel/helper-validator-option@^7.14.5":
-  version "7.14.5"
-  resolved "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.14.5.tgz"
-  integrity sha512-OX8D5eeX4XwcroVW45NMvoYaIuFI+GQpA2a8Gi+X/U/cDUIRsV37qQfF905F0htTRCREQIB4KqPeaveRJUl3Ow==
+"@babel/helper-validator-identifier@^7.16.7", "@babel/helper-validator-identifier@^7.27.1":
+  version "7.27.1"
+  resolved "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.27.1.tgz"
+  integrity sha512-D2hP9eA+Sqx1kBZgzxZh0y1trbuU+JoDkiEwqhQ36nodYqJwyEIhPSdMNd7lOm/4io72luTPWH20Yda0xOuUow==
 
 "@babel/helper-validator-option@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/helper-validator-option/-/helper-validator-option-7.16.7.tgz#b203ce62ce5fe153899b617c08957de860de4d23"
+  resolved "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.16.7.tgz"
   integrity sha512-TRtenOuRUVo9oIQGPC5G9DgK4743cdxvtOw0weQNpZXaS16SCBi5MNjZF8vba3ETURjZpTbVn7Vvcf2eAwFozQ==
 
 "@babel/helper-wrap-function@^7.16.8":
   version "7.16.8"
-  resolved "https://registry.yarnpkg.com/@babel/helper-wrap-function/-/helper-wrap-function-7.16.8.tgz#58afda087c4cd235de92f7ceedebca2c41274200"
+  resolved "https://registry.npmjs.org/@babel/helper-wrap-function/-/helper-wrap-function-7.16.8.tgz"
   integrity sha512-8RpyRVIAW1RcDDGTA+GpPAwV22wXCfKOoM9bet6TLkGIFTkRQSkH1nMQ5Yet4MpoXe1ZwHPVtNasc2w0uZMqnw==
   dependencies:
     "@babel/helper-function-name" "^7.16.7"
@@ -362,52 +264,30 @@
     "@babel/types" "^7.16.8"
 
 "@babel/helpers@^7.17.8":
-  version "7.17.8"
-  resolved "https://registry.yarnpkg.com/@babel/helpers/-/helpers-7.17.8.tgz#288450be8c6ac7e4e44df37bcc53d345e07bc106"
-  integrity sha512-QcL86FGxpfSJwGtAvv4iG93UL6bmqBdmoVY0CMCU2g+oD2ezQse3PT5Pa+jiD6LJndBQi0EDlpzOWNlLuhz5gw==
-  dependencies:
-    "@babel/template" "^7.16.7"
-    "@babel/traverse" "^7.17.3"
-    "@babel/types" "^7.17.0"
-
-"@babel/highlight@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/highlight/-/highlight-7.16.0.tgz"
-  integrity sha512-t8MH41kUQylBtu2+4IQA3atqevA2lRgqA2wyVB/YiWmsDSuylZZuXOUy9ric30hfzauEFfdsuk/eXTRrGrfd0g==
+  version "7.28.4"
+  resolved "https://registry.npmjs.org/@babel/helpers/-/helpers-7.28.4.tgz"
+  integrity sha512-HFN59MmQXGHVyYadKLVumYsA9dBFun/ldYxipEjzA4196jpLZd8UjEEBLkbEkvfYreDqJhZxYAWFPtrfhNpj4w==
   dependencies:
-    "@babel/helper-validator-identifier" "^7.15.7"
-    chalk "^2.0.0"
-    js-tokens "^4.0.0"
+    "@babel/template" "^7.27.2"
+    "@babel/types" "^7.28.4"
 
-"@babel/highlight@^7.16.7":
-  version "7.16.10"
-  resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.16.10.tgz#744f2eb81579d6eea753c227b0f570ad785aba88"
-  integrity sha512-5FnTQLSLswEj6IkgVw5KusNUUFY9ZGqe/TRFnP/BKYHYgfh7tc+C7mwiy95/yNP7Dh9x580Vv8r7u7ZfTBFxdw==
+"@babel/parser@^7.17.8", "@babel/parser@^7.27.2", "@babel/parser@^7.28.3", "@babel/parser@^7.28.4":
+  version "7.28.4"
+  resolved "https://registry.npmjs.org/@babel/parser/-/parser-7.28.4.tgz"
+  integrity sha512-yZbBqeM6TkpP9du/I2pUZnJsRMGGvOuIrhjzC1AwHwW+6he4mni6Bp/m8ijn0iOuZuPI2BfkCoSRunpyjnrQKg==
   dependencies:
-    "@babel/helper-validator-identifier" "^7.16.7"
-    chalk "^2.0.0"
-    js-tokens "^4.0.0"
-
-"@babel/parser@^7.16.0", "@babel/parser@^7.16.3":
-  version "7.16.4"
-  resolved "https://registry.npmjs.org/@babel/parser/-/parser-7.16.4.tgz"
-  integrity sha512-6V0qdPUaiVHH3RtZeLIsc+6pDhbYzHR8ogA8w+f+Wc77DuXto19g2QUwveINoS34Uw+W8/hQDGJCx+i4n7xcng==
-
-"@babel/parser@^7.16.7", "@babel/parser@^7.17.3", "@babel/parser@^7.17.8":
-  version "7.17.8"
-  resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.17.8.tgz#2817fb9d885dd8132ea0f8eb615a6388cca1c240"
-  integrity sha512-BoHhDJrJXqcg+ZL16Xv39H9n+AqJ4pcDrQBGZN+wHxIysrLZ3/ECwCBUch/1zUNhnsXULcONU3Ei5Hmkfk6kiQ==
+    "@babel/types" "^7.28.4"
 
 "@babel/plugin-bugfix-safari-id-destructuring-collision-in-function-expression@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-bugfix-safari-id-destructuring-collision-in-function-expression/-/plugin-bugfix-safari-id-destructuring-collision-in-function-expression-7.16.7.tgz#4eda6d6c2a0aa79c70fa7b6da67763dfe2141050"
+  resolved "https://registry.npmjs.org/@babel/plugin-bugfix-safari-id-destructuring-collision-in-function-expression/-/plugin-bugfix-safari-id-destructuring-collision-in-function-expression-7.16.7.tgz"
   integrity sha512-anv/DObl7waiGEnC24O9zqL0pSuI9hljihqiDuFHC8d7/bjr/4RLGPWuc8rYOff/QPzbEPSkzG8wGG9aDuhHRg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining/-/plugin-bugfix-v8-spread-parameters-in-optional-chaining-7.16.7.tgz#cc001234dfc139ac45f6bcf801866198c8c72ff9"
+  resolved "https://registry.npmjs.org/@babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining/-/plugin-bugfix-v8-spread-parameters-in-optional-chaining-7.16.7.tgz"
   integrity sha512-di8vUHRdf+4aJ7ltXhaDbPoszdkh59AQtJM5soLsuHpQJdFQZOA4uGj0V2u/CZ8bJ/u8ULDL5yq6FO/bCXnKHw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -416,7 +296,7 @@
 
 "@babel/plugin-proposal-async-generator-functions@^7.16.8":
   version "7.16.8"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-async-generator-functions/-/plugin-proposal-async-generator-functions-7.16.8.tgz#3bdd1ebbe620804ea9416706cd67d60787504bc8"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-async-generator-functions/-/plugin-proposal-async-generator-functions-7.16.8.tgz"
   integrity sha512-71YHIvMuiuqWJQkebWJtdhQTfd4Q4mF76q2IX37uZPkG9+olBxsX+rH1vkhFto4UeJZ9dPY2s+mDvhDm1u2BGQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -425,7 +305,7 @@
 
 "@babel/plugin-proposal-class-properties@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-class-properties/-/plugin-proposal-class-properties-7.16.7.tgz#925cad7b3b1a2fcea7e59ecc8eb5954f961f91b0"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-class-properties/-/plugin-proposal-class-properties-7.16.7.tgz"
   integrity sha512-IobU0Xme31ewjYOShSIqd/ZGM/r/cuOz2z0MDbNrhF5FW+ZVgi0f2lyeoj9KFPDOAqsYxmLWZte1WOwlvY9aww==
   dependencies:
     "@babel/helper-create-class-features-plugin" "^7.16.7"
@@ -433,7 +313,7 @@
 
 "@babel/plugin-proposal-class-static-block@^7.16.7":
   version "7.17.6"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-class-static-block/-/plugin-proposal-class-static-block-7.17.6.tgz#164e8fd25f0d80fa48c5a4d1438a6629325ad83c"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-class-static-block/-/plugin-proposal-class-static-block-7.17.6.tgz"
   integrity sha512-X/tididvL2zbs7jZCeeRJ8167U/+Ac135AM6jCAx6gYXDUviZV5Ku9UDvWS2NCuWlFjIRXklYhwo6HhAC7ETnA==
   dependencies:
     "@babel/helper-create-class-features-plugin" "^7.17.6"
@@ -442,7 +322,7 @@
 
 "@babel/plugin-proposal-dynamic-import@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-dynamic-import/-/plugin-proposal-dynamic-import-7.16.7.tgz#c19c897eaa46b27634a00fee9fb7d829158704b2"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-dynamic-import/-/plugin-proposal-dynamic-import-7.16.7.tgz"
   integrity sha512-I8SW9Ho3/8DRSdmDdH3gORdyUuYnk1m4cMxUAdu5oy4n3OfN8flDEH+d60iG7dUfi0KkYwSvoalHzzdRzpWHTg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -450,7 +330,7 @@
 
 "@babel/plugin-proposal-export-namespace-from@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-export-namespace-from/-/plugin-proposal-export-namespace-from-7.16.7.tgz#09de09df18445a5786a305681423ae63507a6163"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-export-namespace-from/-/plugin-proposal-export-namespace-from-7.16.7.tgz"
   integrity sha512-ZxdtqDXLRGBL64ocZcs7ovt71L3jhC1RGSyR996svrCi3PYqHNkb3SwPJCs8RIzD86s+WPpt2S73+EHCGO+NUA==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -458,7 +338,7 @@
 
 "@babel/plugin-proposal-json-strings@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-json-strings/-/plugin-proposal-json-strings-7.16.7.tgz#9732cb1d17d9a2626a08c5be25186c195b6fa6e8"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-json-strings/-/plugin-proposal-json-strings-7.16.7.tgz"
   integrity sha512-lNZ3EEggsGY78JavgbHsK9u5P3pQaW7k4axlgFLYkMd7UBsiNahCITShLjNQschPyjtO6dADrL24757IdhBrsQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -466,7 +346,7 @@
 
 "@babel/plugin-proposal-logical-assignment-operators@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-logical-assignment-operators/-/plugin-proposal-logical-assignment-operators-7.16.7.tgz#be23c0ba74deec1922e639832904be0bea73cdea"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-logical-assignment-operators/-/plugin-proposal-logical-assignment-operators-7.16.7.tgz"
   integrity sha512-K3XzyZJGQCr00+EtYtrDjmwX7o7PLK6U9bi1nCwkQioRFVUv6dJoxbQjtWVtP+bCPy82bONBKG8NPyQ4+i6yjg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -474,7 +354,7 @@
 
 "@babel/plugin-proposal-nullish-coalescing-operator@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-nullish-coalescing-operator/-/plugin-proposal-nullish-coalescing-operator-7.16.7.tgz#141fc20b6857e59459d430c850a0011e36561d99"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-nullish-coalescing-operator/-/plugin-proposal-nullish-coalescing-operator-7.16.7.tgz"
   integrity sha512-aUOrYU3EVtjf62jQrCj63pYZ7k6vns2h/DQvHPWGmsJRYzWXZ6/AsfgpiRy6XiuIDADhJzP2Q9MwSMKauBQ+UQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -482,7 +362,7 @@
 
 "@babel/plugin-proposal-numeric-separator@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-numeric-separator/-/plugin-proposal-numeric-separator-7.16.7.tgz#d6b69f4af63fb38b6ca2558442a7fb191236eba9"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-numeric-separator/-/plugin-proposal-numeric-separator-7.16.7.tgz"
   integrity sha512-vQgPMknOIgiuVqbokToyXbkY/OmmjAzr/0lhSIbG/KmnzXPGwW/AdhdKpi+O4X/VkWiWjnkKOBiqJrTaC98VKw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -490,7 +370,7 @@
 
 "@babel/plugin-proposal-object-rest-spread@^7.16.7":
   version "7.17.3"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-object-rest-spread/-/plugin-proposal-object-rest-spread-7.17.3.tgz#d9eb649a54628a51701aef7e0ea3d17e2b9dd390"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-object-rest-spread/-/plugin-proposal-object-rest-spread-7.17.3.tgz"
   integrity sha512-yuL5iQA/TbZn+RGAfxQXfi7CNLmKi1f8zInn4IgobuCWcAb7i+zj4TYzQ9l8cEzVyJ89PDGuqxK1xZpUDISesw==
   dependencies:
     "@babel/compat-data" "^7.17.0"
@@ -501,7 +381,7 @@
 
 "@babel/plugin-proposal-optional-catch-binding@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-optional-catch-binding/-/plugin-proposal-optional-catch-binding-7.16.7.tgz#c623a430674ffc4ab732fd0a0ae7722b67cb74cf"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-optional-catch-binding/-/plugin-proposal-optional-catch-binding-7.16.7.tgz"
   integrity sha512-eMOH/L4OvWSZAE1VkHbr1vckLG1WUcHGJSLqqQwl2GaUqG6QjddvrOaTUMNYiv77H5IKPMZ9U9P7EaHwvAShfA==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -509,7 +389,7 @@
 
 "@babel/plugin-proposal-optional-chaining@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-optional-chaining/-/plugin-proposal-optional-chaining-7.16.7.tgz#7cd629564724816c0e8a969535551f943c64c39a"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-optional-chaining/-/plugin-proposal-optional-chaining-7.16.7.tgz"
   integrity sha512-eC3xy+ZrUcBtP7x+sq62Q/HYd674pPTb/77XZMb5wbDPGWIdUbSr4Agr052+zaUPSb+gGRnjxXfKFvx5iMJ+DA==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -518,7 +398,7 @@
 
 "@babel/plugin-proposal-private-methods@^7.16.11":
   version "7.16.11"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-private-methods/-/plugin-proposal-private-methods-7.16.11.tgz#e8df108288555ff259f4527dbe84813aac3a1c50"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-private-methods/-/plugin-proposal-private-methods-7.16.11.tgz"
   integrity sha512-F/2uAkPlXDr8+BHpZvo19w3hLFKge+k75XUprE6jaqKxjGkSYcK+4c+bup5PdW/7W/Rpjwql7FTVEDW+fRAQsw==
   dependencies:
     "@babel/helper-create-class-features-plugin" "^7.16.10"
@@ -526,7 +406,7 @@
 
 "@babel/plugin-proposal-private-property-in-object@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-private-property-in-object/-/plugin-proposal-private-property-in-object-7.16.7.tgz#b0b8cef543c2c3d57e59e2c611994861d46a3fce"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-private-property-in-object/-/plugin-proposal-private-property-in-object-7.16.7.tgz"
   integrity sha512-rMQkjcOFbm+ufe3bTZLyOfsOUOxyvLXZJCTARhJr+8UMSoZmqTe1K1BgkFcrW37rAchWg57yI69ORxiWvUINuQ==
   dependencies:
     "@babel/helper-annotate-as-pure" "^7.16.7"
@@ -534,22 +414,14 @@
     "@babel/helper-plugin-utils" "^7.16.7"
     "@babel/plugin-syntax-private-property-in-object" "^7.14.5"
 
-"@babel/plugin-proposal-unicode-property-regex@^7.16.7":
+"@babel/plugin-proposal-unicode-property-regex@^7.16.7", "@babel/plugin-proposal-unicode-property-regex@^7.4.4":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-proposal-unicode-property-regex/-/plugin-proposal-unicode-property-regex-7.16.7.tgz#635d18eb10c6214210ffc5ff4932552de08188a2"
+  resolved "https://registry.npmjs.org/@babel/plugin-proposal-unicode-property-regex/-/plugin-proposal-unicode-property-regex-7.16.7.tgz"
   integrity sha512-QRK0YI/40VLhNVGIjRNAAQkEHws0cswSdFFjpFyt943YmJIU1da9uW63Iu6NFV6CxTZW5eTDCrwZUstBWgp/Rg==
   dependencies:
     "@babel/helper-create-regexp-features-plugin" "^7.16.7"
     "@babel/helper-plugin-utils" "^7.16.7"
 
-"@babel/plugin-proposal-unicode-property-regex@^7.4.4":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/plugin-proposal-unicode-property-regex/-/plugin-proposal-unicode-property-regex-7.16.0.tgz"
-  integrity sha512-ti7IdM54NXv29cA4+bNNKEMS4jLMCbJgl+Drv+FgYy0erJLAxNAIXcNjNjrRZEcWq0xJHsNVwQezskMFpF8N9g==
-  dependencies:
-    "@babel/helper-create-regexp-features-plugin" "^7.16.0"
-    "@babel/helper-plugin-utils" "^7.14.5"
-
 "@babel/plugin-syntax-async-generators@^7.8.4":
   version "7.8.4"
   resolved "https://registry.npmjs.org/@babel/plugin-syntax-async-generators/-/plugin-syntax-async-generators-7.8.4.tgz"
@@ -594,7 +466,7 @@
 
 "@babel/plugin-syntax-jsx@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.16.7.tgz#50b6571d13f764266a113d77c82b4a6508bbe665"
+  resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.16.7.tgz"
   integrity sha512-Esxmk7YjA8QysKeT3VhTXvF6y77f/a91SIs4pWb4H2eWGQkCKFgQaG6hdoEVZtGsrAcb2K5BW66XsOErD4WU3Q==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -657,14 +529,14 @@
 
 "@babel/plugin-transform-arrow-functions@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-arrow-functions/-/plugin-transform-arrow-functions-7.16.7.tgz#44125e653d94b98db76369de9c396dc14bef4154"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-arrow-functions/-/plugin-transform-arrow-functions-7.16.7.tgz"
   integrity sha512-9ffkFFMbvzTvv+7dTp/66xvZAWASuPD5Tl9LK3Z9vhOmANo6j94rik+5YMBt4CwHVMWLWpMsriIc2zsa3WW3xQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-async-to-generator@^7.16.8":
   version "7.16.8"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-async-to-generator/-/plugin-transform-async-to-generator-7.16.8.tgz#b83dff4b970cf41f1b819f8b49cc0cfbaa53a808"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-async-to-generator/-/plugin-transform-async-to-generator-7.16.8.tgz"
   integrity sha512-MtmUmTJQHCnyJVrScNzNlofQJ3dLFuobYn3mwOTKHnSCMtbNsqvF71GQmJfFjdrXSsAA7iysFmYWw4bXZ20hOg==
   dependencies:
     "@babel/helper-module-imports" "^7.16.7"
@@ -673,21 +545,21 @@
 
 "@babel/plugin-transform-block-scoped-functions@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-block-scoped-functions/-/plugin-transform-block-scoped-functions-7.16.7.tgz#4d0d57d9632ef6062cdf354bb717102ee042a620"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-block-scoped-functions/-/plugin-transform-block-scoped-functions-7.16.7.tgz"
   integrity sha512-JUuzlzmF40Z9cXyytcbZEZKckgrQzChbQJw/5PuEHYeqzCsvebDx0K0jWnIIVcmmDOAVctCgnYs0pMcrYj2zJg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-block-scoping@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-block-scoping/-/plugin-transform-block-scoping-7.16.7.tgz#f50664ab99ddeaee5bc681b8f3a6ea9d72ab4f87"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-block-scoping/-/plugin-transform-block-scoping-7.16.7.tgz"
   integrity sha512-ObZev2nxVAYA4bhyusELdo9hb3H+A56bxH3FZMbEImZFiEDYVHXQSJ1hQKFlDnlt8G9bBrCZ5ZpURZUrV4G5qQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-classes@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-classes/-/plugin-transform-classes-7.16.7.tgz#8f4b9562850cd973de3b498f1218796eb181ce00"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-classes/-/plugin-transform-classes-7.16.7.tgz"
   integrity sha512-WY7og38SFAGYRe64BrjKf8OrE6ulEHtr5jEYaZMwox9KebgqPi67Zqz8K53EKk1fFEJgm96r32rkKZ3qA2nCWQ==
   dependencies:
     "@babel/helper-annotate-as-pure" "^7.16.7"
@@ -701,44 +573,36 @@
 
 "@babel/plugin-transform-computed-properties@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-computed-properties/-/plugin-transform-computed-properties-7.16.7.tgz#66dee12e46f61d2aae7a73710f591eb3df616470"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-computed-properties/-/plugin-transform-computed-properties-7.16.7.tgz"
   integrity sha512-gN72G9bcmenVILj//sv1zLNaPyYcOzUho2lIJBMh/iakJ9ygCo/hEF9cpGb61SCMEDxbbyBoVQxrt+bWKu5KGw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-destructuring@^7.16.7":
   version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-destructuring/-/plugin-transform-destructuring-7.17.7.tgz#49dc2675a7afa9a5e4c6bdee636061136c3408d1"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-destructuring/-/plugin-transform-destructuring-7.17.7.tgz"
   integrity sha512-XVh0r5yq9sLR4vZ6eVZe8FKfIcSgaTBxVBRSYokRj2qksf6QerYnTxz9/GTuKTH/n/HwLP7t6gtlybHetJ/6hQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
-"@babel/plugin-transform-dotall-regex@^7.16.7":
+"@babel/plugin-transform-dotall-regex@^7.16.7", "@babel/plugin-transform-dotall-regex@^7.4.4":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-dotall-regex/-/plugin-transform-dotall-regex-7.16.7.tgz#6b2d67686fab15fb6a7fd4bd895d5982cfc81241"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-dotall-regex/-/plugin-transform-dotall-regex-7.16.7.tgz"
   integrity sha512-Lyttaao2SjZF6Pf4vk1dVKv8YypMpomAbygW+mU5cYP3S5cWTfCJjG8xV6CFdzGFlfWK81IjL9viiTvpb6G7gQ==
   dependencies:
     "@babel/helper-create-regexp-features-plugin" "^7.16.7"
     "@babel/helper-plugin-utils" "^7.16.7"
 
-"@babel/plugin-transform-dotall-regex@^7.4.4":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/plugin-transform-dotall-regex/-/plugin-transform-dotall-regex-7.16.0.tgz"
-  integrity sha512-FXlDZfQeLILfJlC6I1qyEwcHK5UpRCFkaoVyA1nk9A1L1Yu583YO4un2KsLBsu3IJb4CUbctZks8tD9xPQubLw==
-  dependencies:
-    "@babel/helper-create-regexp-features-plugin" "^7.16.0"
-    "@babel/helper-plugin-utils" "^7.14.5"
-
 "@babel/plugin-transform-duplicate-keys@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-duplicate-keys/-/plugin-transform-duplicate-keys-7.16.7.tgz#2207e9ca8f82a0d36a5a67b6536e7ef8b08823c9"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-duplicate-keys/-/plugin-transform-duplicate-keys-7.16.7.tgz"
   integrity sha512-03DvpbRfvWIXyK0/6QiR1KMTWeT6OcQ7tbhjrXyFS02kjuX/mu5Bvnh5SDSWHxyawit2g5aWhKwI86EE7GUnTw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-exponentiation-operator@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-exponentiation-operator/-/plugin-transform-exponentiation-operator-7.16.7.tgz#efa9862ef97e9e9e5f653f6ddc7b665e8536fe9b"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-exponentiation-operator/-/plugin-transform-exponentiation-operator-7.16.7.tgz"
   integrity sha512-8UYLSlyLgRixQvlYH3J2ekXFHDFLQutdy7FfFAMm3CPZ6q9wHCwnUyiXpQCe3gVVnQlHc5nsuiEVziteRNTXEA==
   dependencies:
     "@babel/helper-builder-binary-assignment-operator-visitor" "^7.16.7"
@@ -746,14 +610,14 @@
 
 "@babel/plugin-transform-for-of@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-for-of/-/plugin-transform-for-of-7.16.7.tgz#649d639d4617dff502a9a158c479b3b556728d8c"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-for-of/-/plugin-transform-for-of-7.16.7.tgz"
   integrity sha512-/QZm9W92Ptpw7sjI9Nx1mbcsWz33+l8kuMIQnDwgQBG5s3fAfQvkRjQ7NqXhtNcKOnPkdICmUHyCaWW06HCsqg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-function-name@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-function-name/-/plugin-transform-function-name-7.16.7.tgz#5ab34375c64d61d083d7d2f05c38d90b97ec65cf"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-function-name/-/plugin-transform-function-name-7.16.7.tgz"
   integrity sha512-SU/C68YVwTRxqWj5kgsbKINakGag0KTgq9f2iZEXdStoAbOzLHEBRYzImmA6yFo8YZhJVflvXmIHUO7GWHmxxA==
   dependencies:
     "@babel/helper-compilation-targets" "^7.16.7"
@@ -762,21 +626,21 @@
 
 "@babel/plugin-transform-literals@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-literals/-/plugin-transform-literals-7.16.7.tgz#254c9618c5ff749e87cb0c0cef1a0a050c0bdab1"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-literals/-/plugin-transform-literals-7.16.7.tgz"
   integrity sha512-6tH8RTpTWI0s2sV6uq3e/C9wPo4PTqqZps4uF0kzQ9/xPLFQtipynvmT1g/dOfEJ+0EQsHhkQ/zyRId8J2b8zQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-member-expression-literals@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-member-expression-literals/-/plugin-transform-member-expression-literals-7.16.7.tgz#6e5dcf906ef8a098e630149d14c867dd28f92384"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-member-expression-literals/-/plugin-transform-member-expression-literals-7.16.7.tgz"
   integrity sha512-mBruRMbktKQwbxaJof32LT9KLy2f3gH+27a5XSuXo6h7R3vqltl0PgZ80C8ZMKw98Bf8bqt6BEVi3svOh2PzMw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-modules-amd@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-modules-amd/-/plugin-transform-modules-amd-7.16.7.tgz#b28d323016a7daaae8609781d1f8c9da42b13186"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-modules-amd/-/plugin-transform-modules-amd-7.16.7.tgz"
   integrity sha512-KaaEtgBL7FKYwjJ/teH63oAmE3lP34N3kshz8mm4VMAw7U3PxjVwwUmxEFksbgsNUaO3wId9R2AVQYSEGRa2+g==
   dependencies:
     "@babel/helper-module-transforms" "^7.16.7"
@@ -785,7 +649,7 @@
 
 "@babel/plugin-transform-modules-commonjs@^7.16.8":
   version "7.17.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-modules-commonjs/-/plugin-transform-modules-commonjs-7.17.7.tgz#d86b217c8e45bb5f2dbc11eefc8eab62cf980d19"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-modules-commonjs/-/plugin-transform-modules-commonjs-7.17.7.tgz"
   integrity sha512-ITPmR2V7MqioMJyrxUo2onHNC3e+MvfFiFIR0RP21d3PtlVb6sfzoxNKiphSZUOM9hEIdzCcZe83ieX3yoqjUA==
   dependencies:
     "@babel/helper-module-transforms" "^7.17.7"
@@ -795,7 +659,7 @@
 
 "@babel/plugin-transform-modules-systemjs@^7.16.7":
   version "7.17.8"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-modules-systemjs/-/plugin-transform-modules-systemjs-7.17.8.tgz#81fd834024fae14ea78fbe34168b042f38703859"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-modules-systemjs/-/plugin-transform-modules-systemjs-7.17.8.tgz"
   integrity sha512-39reIkMTUVagzgA5x88zDYXPCMT6lcaRKs1+S9K6NKBPErbgO/w/kP8GlNQTC87b412ZTlmNgr3k2JrWgHH+Bw==
   dependencies:
     "@babel/helper-hoist-variables" "^7.16.7"
@@ -806,7 +670,7 @@
 
 "@babel/plugin-transform-modules-umd@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-modules-umd/-/plugin-transform-modules-umd-7.16.7.tgz#23dad479fa585283dbd22215bff12719171e7618"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-modules-umd/-/plugin-transform-modules-umd-7.16.7.tgz"
   integrity sha512-EMh7uolsC8O4xhudF2F6wedbSHm1HHZ0C6aJ7K67zcDNidMzVcxWdGr+htW9n21klm+bOn+Rx4CBsAntZd3rEQ==
   dependencies:
     "@babel/helper-module-transforms" "^7.16.7"
@@ -814,21 +678,21 @@
 
 "@babel/plugin-transform-named-capturing-groups-regex@^7.16.8":
   version "7.16.8"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-named-capturing-groups-regex/-/plugin-transform-named-capturing-groups-regex-7.16.8.tgz#7f860e0e40d844a02c9dcf9d84965e7dfd666252"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-named-capturing-groups-regex/-/plugin-transform-named-capturing-groups-regex-7.16.8.tgz"
   integrity sha512-j3Jw+n5PvpmhRR+mrgIh04puSANCk/T/UA3m3P1MjJkhlK906+ApHhDIqBQDdOgL/r1UYpz4GNclTXxyZrYGSw==
   dependencies:
     "@babel/helper-create-regexp-features-plugin" "^7.16.7"
 
 "@babel/plugin-transform-new-target@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-new-target/-/plugin-transform-new-target-7.16.7.tgz#9967d89a5c243818e0800fdad89db22c5f514244"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-new-target/-/plugin-transform-new-target-7.16.7.tgz"
   integrity sha512-xiLDzWNMfKoGOpc6t3U+etCE2yRnn3SM09BXqWPIZOBpL2gvVrBWUKnsJx0K/ADi5F5YC5f8APFfWrz25TdlGg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-object-super@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-object-super/-/plugin-transform-object-super-7.16.7.tgz#ac359cf8d32cf4354d27a46867999490b6c32a94"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-object-super/-/plugin-transform-object-super-7.16.7.tgz"
   integrity sha512-14J1feiQVWaGvRxj2WjyMuXS2jsBkgB3MdSN5HuC2G5nRspa5RK9COcs82Pwy5BuGcjb+fYaUj94mYcOj7rCvw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -836,35 +700,35 @@
 
 "@babel/plugin-transform-parameters@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-parameters/-/plugin-transform-parameters-7.16.7.tgz#a1721f55b99b736511cb7e0152f61f17688f331f"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-parameters/-/plugin-transform-parameters-7.16.7.tgz"
   integrity sha512-AT3MufQ7zZEhU2hwOA11axBnExW0Lszu4RL/tAlUJBuNoRak+wehQW8h6KcXOcgjY42fHtDxswuMhMjFEuv/aw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-property-literals@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-property-literals/-/plugin-transform-property-literals-7.16.7.tgz#2dadac85155436f22c696c4827730e0fe1057a55"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-property-literals/-/plugin-transform-property-literals-7.16.7.tgz"
   integrity sha512-z4FGr9NMGdoIl1RqavCqGG+ZuYjfZ/hkCIeuH6Do7tXmSm0ls11nYVSJqFEUOSJbDab5wC6lRE/w6YjVcr6Hqw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-react-display-name@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-react-display-name/-/plugin-transform-react-display-name-7.16.7.tgz#7b6d40d232f4c0f550ea348593db3b21e2404340"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-react-display-name/-/plugin-transform-react-display-name-7.16.7.tgz"
   integrity sha512-qgIg8BcZgd0G/Cz916D5+9kqX0c7nPZyXaP8R2tLNN5tkyIZdG5fEwBrxwplzSnjC1jvQmyMNVwUCZPcbGY7Pg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-react-jsx-development@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-react-jsx-development/-/plugin-transform-react-jsx-development-7.16.7.tgz#43a00724a3ed2557ed3f276a01a929e6686ac7b8"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-development/-/plugin-transform-react-jsx-development-7.16.7.tgz"
   integrity sha512-RMvQWvpla+xy6MlBpPlrKZCMRs2AGiHOGHY3xRwl0pEeim348dDyxeH4xBsMPbIMhujeq7ihE702eM2Ew0Wo+A==
   dependencies:
     "@babel/plugin-transform-react-jsx" "^7.16.7"
 
 "@babel/plugin-transform-react-jsx@^7.16.7":
   version "7.17.3"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-react-jsx/-/plugin-transform-react-jsx-7.17.3.tgz#eac1565da176ccb1a715dae0b4609858808008c1"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-react-jsx/-/plugin-transform-react-jsx-7.17.3.tgz"
   integrity sha512-9tjBm4O07f7mzKSIlEmPdiE6ub7kfIe6Cd+w+oQebpATfTQMAgW+YOuWxogbKVTulA+MEO7byMeIUtQ1z+z+ZQ==
   dependencies:
     "@babel/helper-annotate-as-pure" "^7.16.7"
@@ -875,7 +739,7 @@
 
 "@babel/plugin-transform-react-pure-annotations@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-react-pure-annotations/-/plugin-transform-react-pure-annotations-7.16.7.tgz#232bfd2f12eb551d6d7d01d13fe3f86b45eb9c67"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-react-pure-annotations/-/plugin-transform-react-pure-annotations-7.16.7.tgz"
   integrity sha512-hs71ToC97k3QWxswh2ElzMFABXHvGiJ01IB1TbYQDGeWRKWz/MPUTh5jGExdHvosYKpnJW5Pm3S4+TA3FyX+GA==
   dependencies:
     "@babel/helper-annotate-as-pure" "^7.16.7"
@@ -883,28 +747,28 @@
 
 "@babel/plugin-transform-regenerator@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-regenerator/-/plugin-transform-regenerator-7.16.7.tgz#9e7576dc476cb89ccc5096fff7af659243b4adeb"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-regenerator/-/plugin-transform-regenerator-7.16.7.tgz"
   integrity sha512-mF7jOgGYCkSJagJ6XCujSQg+6xC1M77/03K2oBmVJWoFGNUtnVJO4WHKJk3dnPC8HCcj4xBQP1Egm8DWh3Pb3Q==
   dependencies:
     regenerator-transform "^0.14.2"
 
 "@babel/plugin-transform-reserved-words@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-reserved-words/-/plugin-transform-reserved-words-7.16.7.tgz#1d798e078f7c5958eec952059c460b220a63f586"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-reserved-words/-/plugin-transform-reserved-words-7.16.7.tgz"
   integrity sha512-KQzzDnZ9hWQBjwi5lpY5v9shmm6IVG0U9pB18zvMu2i4H90xpT4gmqwPYsn8rObiadYe2M0gmgsiOIF5A/2rtg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-shorthand-properties@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-shorthand-properties/-/plugin-transform-shorthand-properties-7.16.7.tgz#e8549ae4afcf8382f711794c0c7b6b934c5fbd2a"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-shorthand-properties/-/plugin-transform-shorthand-properties-7.16.7.tgz"
   integrity sha512-hah2+FEnoRoATdIb05IOXf+4GzXYTq75TVhIn1PewihbpyrNWUt2JbudKQOETWw6QpLe+AIUpJ5MVLYTQbeeUg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-spread@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-spread/-/plugin-transform-spread-7.16.7.tgz#a303e2122f9f12e0105daeedd0f30fb197d8ff44"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-spread/-/plugin-transform-spread-7.16.7.tgz"
   integrity sha512-+pjJpgAngb53L0iaA5gU/1MLXJIfXcYepLgXB3esVRf4fqmj8f2cxM3/FKaHsZms08hFQJkFccEWuIpm429TXg==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -912,35 +776,35 @@
 
 "@babel/plugin-transform-sticky-regex@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-sticky-regex/-/plugin-transform-sticky-regex-7.16.7.tgz#c84741d4f4a38072b9a1e2e3fd56d359552e8660"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-sticky-regex/-/plugin-transform-sticky-regex-7.16.7.tgz"
   integrity sha512-NJa0Bd/87QV5NZZzTuZG5BPJjLYadeSZ9fO6oOUoL4iQx+9EEuw/eEM92SrsT19Yc2jgB1u1hsjqDtH02c3Drw==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-template-literals@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-template-literals/-/plugin-transform-template-literals-7.16.7.tgz#f3d1c45d28967c8e80f53666fc9c3e50618217ab"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-template-literals/-/plugin-transform-template-literals-7.16.7.tgz"
   integrity sha512-VwbkDDUeenlIjmfNeDX/V0aWrQH2QiVyJtwymVQSzItFDTpxfyJh3EVaQiS0rIN/CqbLGr0VcGmuwyTdZtdIsA==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-typeof-symbol@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-typeof-symbol/-/plugin-transform-typeof-symbol-7.16.7.tgz#9cdbe622582c21368bd482b660ba87d5545d4f7e"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-typeof-symbol/-/plugin-transform-typeof-symbol-7.16.7.tgz"
   integrity sha512-p2rOixCKRJzpg9JB4gjnG4gjWkWa89ZoYUnl9snJ1cWIcTH/hvxZqfO+WjG6T8DRBpctEol5jw1O5rA8gkCokQ==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-unicode-escapes@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-unicode-escapes/-/plugin-transform-unicode-escapes-7.16.7.tgz#da8717de7b3287a2c6d659750c964f302b31ece3"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-unicode-escapes/-/plugin-transform-unicode-escapes-7.16.7.tgz"
   integrity sha512-TAV5IGahIz3yZ9/Hfv35TV2xEm+kaBDaZQCn2S/hG9/CZ0DktxJv9eKfPc7yYCvOYR4JGx1h8C+jcSOvgaaI/Q==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
 
 "@babel/plugin-transform-unicode-regex@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/plugin-transform-unicode-regex/-/plugin-transform-unicode-regex-7.16.7.tgz#0f7aa4a501198976e25e82702574c34cfebe9ef2"
+  resolved "https://registry.npmjs.org/@babel/plugin-transform-unicode-regex/-/plugin-transform-unicode-regex-7.16.7.tgz"
   integrity sha512-oC5tYYKw56HO75KZVLQ+R/Nl3Hro9kf8iG0hXoaHP7tjAyCpvqBiSNe6vGrZni1Z6MggmUOC6A7VP7AVmw225Q==
   dependencies:
     "@babel/helper-create-regexp-features-plugin" "^7.16.7"
@@ -948,7 +812,7 @@
 
 "@babel/preset-env@^7.16.11":
   version "7.16.11"
-  resolved "https://registry.yarnpkg.com/@babel/preset-env/-/preset-env-7.16.11.tgz#5dd88fd885fae36f88fd7c8342475c9f0abe2982"
+  resolved "https://registry.npmjs.org/@babel/preset-env/-/preset-env-7.16.11.tgz"
   integrity sha512-qcmWG8R7ZW6WBRPZK//y+E3Cli151B20W1Rv7ln27vuPaXU/8TKms6jFdiJtF7UDTxcrb7mZd88tAeK9LjdT8g==
   dependencies:
     "@babel/compat-data" "^7.16.8"
@@ -1039,7 +903,7 @@
 
 "@babel/preset-react@^7.16.7":
   version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/preset-react/-/preset-react-7.16.7.tgz#4c18150491edc69c183ff818f9f2aecbe5d93852"
+  resolved "https://registry.npmjs.org/@babel/preset-react/-/preset-react-7.16.7.tgz"
   integrity sha512-fWpyI8UM/HE6DfPBzD8LnhQ/OcH8AgTaqcqP2nGOXEUV+VKBR5JRN9hCk9ai+zQQ57vtm9oWeXguBCPNUjytgA==
   dependencies:
     "@babel/helper-plugin-utils" "^7.16.7"
@@ -1050,80 +914,43 @@
     "@babel/plugin-transform-react-pure-annotations" "^7.16.7"
 
 "@babel/runtime@^7.14.8", "@babel/runtime@^7.8.4":
-  version "7.16.3"
-  resolved "https://registry.npmjs.org/@babel/runtime/-/runtime-7.16.3.tgz"
-  integrity sha512-WBwekcqacdY2e9AF/Q7WLFUWmdJGJTkbjqTjoMDgXkVZ3ZRUvOPsLb5KdwISoQVsbP+DQzVZW4Zhci0DvpbNTQ==
-  dependencies:
-    regenerator-runtime "^0.13.4"
-
-"@babel/template@^7.16.0":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/template/-/template-7.16.0.tgz"
-  integrity sha512-MnZdpFD/ZdYhXwiunMqqgyZyucaYsbL0IrjoGjaVhGilz+x8YB++kRfygSOIj1yOtWKPlx7NBp+9I1RQSgsd5A==
-  dependencies:
-    "@babel/code-frame" "^7.16.0"
-    "@babel/parser" "^7.16.0"
-    "@babel/types" "^7.16.0"
-
-"@babel/template@^7.16.7":
-  version "7.16.7"
-  resolved "https://registry.yarnpkg.com/@babel/template/-/template-7.16.7.tgz#8d126c8701fde4d66b264b3eba3d96f07666d155"
-  integrity sha512-I8j/x8kHUrbYRTUxXrrMbfCa7jxkE7tZre39x3kjr9hvI82cK1FfqLygotcWN5kdPGWcLdWMHpSBavse5tWw3w==
-  dependencies:
-    "@babel/code-frame" "^7.16.7"
-    "@babel/parser" "^7.16.7"
-    "@babel/types" "^7.16.7"
-
-"@babel/traverse@^7.13.0":
-  version "7.16.3"
-  resolved "https://registry.npmjs.org/@babel/traverse/-/traverse-7.16.3.tgz"
-  integrity sha512-eolumr1vVMjqevCpwVO99yN/LoGL0EyHiLO5I043aYQvwOJ9eR5UsZSClHVCzfhBduMAsSzgA/6AyqPjNayJag==
-  dependencies:
-    "@babel/code-frame" "^7.16.0"
-    "@babel/generator" "^7.16.0"
-    "@babel/helper-function-name" "^7.16.0"
-    "@babel/helper-hoist-variables" "^7.16.0"
-    "@babel/helper-split-export-declaration" "^7.16.0"
-    "@babel/parser" "^7.16.3"
-    "@babel/types" "^7.16.0"
-    debug "^4.1.0"
-    globals "^11.1.0"
-
-"@babel/traverse@^7.16.7", "@babel/traverse@^7.16.8", "@babel/traverse@^7.17.3":
-  version "7.17.3"
-  resolved "https://registry.yarnpkg.com/@babel/traverse/-/traverse-7.17.3.tgz#0ae0f15b27d9a92ba1f2263358ea7c4e7db47b57"
-  integrity sha512-5irClVky7TxRWIRtxlh2WPUUOLhcPN06AGgaQSB8AEwuyEBgJVuJ5imdHm5zxk8w0QS5T+tDfnDxAlhWjpb7cw==
-  dependencies:
-    "@babel/code-frame" "^7.16.7"
-    "@babel/generator" "^7.17.3"
-    "@babel/helper-environment-visitor" "^7.16.7"
-    "@babel/helper-function-name" "^7.16.7"
-    "@babel/helper-hoist-variables" "^7.16.7"
-    "@babel/helper-split-export-declaration" "^7.16.7"
-    "@babel/parser" "^7.17.3"
-    "@babel/types" "^7.17.0"
-    debug "^4.1.0"
-    globals "^11.1.0"
-
-"@babel/types@^7.16.0", "@babel/types@^7.4.4":
-  version "7.16.0"
-  resolved "https://registry.npmjs.org/@babel/types/-/types-7.16.0.tgz"
-  integrity sha512-PJgg/k3SdLsGb3hhisFvtLOw5ts113klrpLuIPtCJIU+BB24fqq6lf8RWqKJEjzqXR9AEH1rIb5XTqwBHB+kQg==
-  dependencies:
-    "@babel/helper-validator-identifier" "^7.15.7"
-    to-fast-properties "^2.0.0"
-
-"@babel/types@^7.16.7", "@babel/types@^7.16.8", "@babel/types@^7.17.0":
-  version "7.17.0"
-  resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.17.0.tgz#a826e368bccb6b3d84acd76acad5c0d87342390b"
-  integrity sha512-TmKSNO4D5rzhL5bjWFcVHHLETzfQ/AmbKpKPOSjlP0WoHZ6L911fgoOKY4Alp/emzG4cHJdyN49zpgkbXFEHHw==
-  dependencies:
-    "@babel/helper-validator-identifier" "^7.16.7"
-    to-fast-properties "^2.0.0"
+  version "7.28.4"
+  resolved "https://registry.npmjs.org/@babel/runtime/-/runtime-7.28.4.tgz"
+  integrity sha512-Q/N6JNWvIvPnLDvjlE1OUBLPQHH6l3CltCEsHIujp45zQUSSh8K+gHnaEX45yAT1nyngnINhvWtzN+Nb9D8RAQ==
+
+"@babel/template@^7.16.7", "@babel/template@^7.27.2":
+  version "7.27.2"
+  resolved "https://registry.npmjs.org/@babel/template/-/template-7.27.2.tgz"
+  integrity sha512-LPDZ85aEJyYSd18/DkjNh4/y1ntkE5KwUHWTiqgRxruuZL2F1yuHligVHLvcHY2vMHXttKFpJn6LwfI7cw7ODw==
+  dependencies:
+    "@babel/code-frame" "^7.27.1"
+    "@babel/parser" "^7.27.2"
+    "@babel/types" "^7.27.1"
+
+"@babel/traverse@^7.13.0", "@babel/traverse@^7.16.7", "@babel/traverse@^7.16.8", "@babel/traverse@^7.17.3":
+  version "7.28.4"
+  resolved "https://registry.npmjs.org/@babel/traverse/-/traverse-7.28.4.tgz"
+  integrity sha512-YEzuboP2qvQavAcjgQNVgsvHIDv6ZpwXvcvjmyySP2DIMuByS/6ioU5G9pYrWHM6T2YDfc7xga9iNzYOs12CFQ==
+  dependencies:
+    "@babel/code-frame" "^7.27.1"
+    "@babel/generator" "^7.28.3"
+    "@babel/helper-globals" "^7.28.0"
+    "@babel/parser" "^7.28.4"
+    "@babel/template" "^7.27.2"
+    "@babel/types" "^7.28.4"
+    debug "^4.3.1"
+
+"@babel/types@^7.16.0", "@babel/types@^7.16.7", "@babel/types@^7.16.8", "@babel/types@^7.17.0", "@babel/types@^7.27.1", "@babel/types@^7.28.2", "@babel/types@^7.28.4", "@babel/types@^7.4.4":
+  version "7.28.4"
+  resolved "https://registry.npmjs.org/@babel/types/-/types-7.28.4.tgz"
+  integrity sha512-bkFqkLhh3pMBUQQkpVgWDWq/lqzc2678eUyDlTBhRqhCHFguYYGM0Efga7tYk4TogG/3x0EEl66/OQ+WGbWB/Q==
+  dependencies:
+    "@babel/helper-string-parser" "^7.27.1"
+    "@babel/helper-validator-identifier" "^7.27.1"
 
 "@csstools/postcss-color-function@^1.0.3":
   version "1.0.3"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-color-function/-/postcss-color-function-1.0.3.tgz#251c961a852c99e9aabdbbdbefd50e9a96e8a9ff"
+  resolved "https://registry.npmjs.org/@csstools/postcss-color-function/-/postcss-color-function-1.0.3.tgz"
   integrity sha512-J26I69pT2B3MYiLY/uzCGKVJyMYVg9TCpXkWsRlt+Yfq+nELUEm72QXIMYXs4xA9cJA4Oqs2EylrfokKl3mJEQ==
   dependencies:
     "@csstools/postcss-progressive-custom-properties" "^1.1.0"
@@ -1131,21 +958,21 @@
 
 "@csstools/postcss-font-format-keywords@^1.0.0":
   version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-font-format-keywords/-/postcss-font-format-keywords-1.0.0.tgz#7e7df948a83a0dfb7eb150a96e2390ac642356a1"
+  resolved "https://registry.npmjs.org/@csstools/postcss-font-format-keywords/-/postcss-font-format-keywords-1.0.0.tgz"
   integrity sha512-oO0cZt8do8FdVBX8INftvIA4lUrKUSCcWUf9IwH9IPWOgKT22oAZFXeHLoDK7nhB2SmkNycp5brxfNMRLIhd6Q==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 "@csstools/postcss-hwb-function@^1.0.0":
   version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-hwb-function/-/postcss-hwb-function-1.0.0.tgz#d6785c1c5ba8152d1d392c66f3a6a446c6034f6d"
+  resolved "https://registry.npmjs.org/@csstools/postcss-hwb-function/-/postcss-hwb-function-1.0.0.tgz"
   integrity sha512-VSTd7hGjmde4rTj1rR30sokY3ONJph1reCBTUXqeW1fKwETPy1x4t/XIeaaqbMbC5Xg4SM/lyXZ2S8NELT2TaA==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 "@csstools/postcss-ic-unit@^1.0.0":
   version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-ic-unit/-/postcss-ic-unit-1.0.0.tgz#f484db59fc94f35a21b6d680d23b0ec69b286b7f"
+  resolved "https://registry.npmjs.org/@csstools/postcss-ic-unit/-/postcss-ic-unit-1.0.0.tgz"
   integrity sha512-i4yps1mBp2ijrx7E96RXrQXQQHm6F4ym1TOD0D69/sjDjZvQ22tqiEvaNw7pFZTUO5b9vWRHzbHzP9+UKuw+bA==
   dependencies:
     "@csstools/postcss-progressive-custom-properties" "^1.1.0"
@@ -1153,21 +980,21 @@
 
 "@csstools/postcss-is-pseudo-class@^2.0.1":
   version "2.0.1"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-is-pseudo-class/-/postcss-is-pseudo-class-2.0.1.tgz#472fff2cf434bdf832f7145b2a5491587e790c9e"
+  resolved "https://registry.npmjs.org/@csstools/postcss-is-pseudo-class/-/postcss-is-pseudo-class-2.0.1.tgz"
   integrity sha512-Og5RrTzwFhrKoA79c3MLkfrIBYmwuf/X83s+JQtz/Dkk/MpsaKtqHV1OOzYkogQ+tj3oYp5Mq39XotBXNqVc3Q==
   dependencies:
     postcss-selector-parser "^6.0.9"
 
 "@csstools/postcss-normalize-display-values@^1.0.0":
   version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-normalize-display-values/-/postcss-normalize-display-values-1.0.0.tgz#ce698f688c28517447aedf15a9037987e3d2dc97"
+  resolved "https://registry.npmjs.org/@csstools/postcss-normalize-display-values/-/postcss-normalize-display-values-1.0.0.tgz"
   integrity sha512-bX+nx5V8XTJEmGtpWTO6kywdS725t71YSLlxWt78XoHUbELWgoCXeOFymRJmL3SU1TLlKSIi7v52EWqe60vJTQ==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 "@csstools/postcss-oklab-function@^1.0.2":
   version "1.0.2"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-oklab-function/-/postcss-oklab-function-1.0.2.tgz#87cd646e9450347a5721e405b4f7cc35157b7866"
+  resolved "https://registry.npmjs.org/@csstools/postcss-oklab-function/-/postcss-oklab-function-1.0.2.tgz"
   integrity sha512-QwhWesEkMlp4narAwUi6pgc6kcooh8cC7zfxa9LSQNYXqzcdNUtNBzbGc5nuyAVreb7uf5Ox4qH1vYT3GA1wOg==
   dependencies:
     "@csstools/postcss-progressive-custom-properties" "^1.1.0"
@@ -1175,7 +1002,7 @@
 
 "@csstools/postcss-progressive-custom-properties@^1.1.0", "@csstools/postcss-progressive-custom-properties@^1.3.0":
   version "1.3.0"
-  resolved "https://registry.yarnpkg.com/@csstools/postcss-progressive-custom-properties/-/postcss-progressive-custom-properties-1.3.0.tgz#542292558384361776b45c85226b9a3a34f276fa"
+  resolved "https://registry.npmjs.org/@csstools/postcss-progressive-custom-properties/-/postcss-progressive-custom-properties-1.3.0.tgz"
   integrity sha512-ASA9W1aIy5ygskZYuWams4BzafD12ULvSypmaLJT2jvQ8G0M3I8PRQhC0h7mG0Z3LI05+agZjqSR9+K9yaQQjA==
   dependencies:
     postcss-value-parser "^4.2.0"
@@ -1187,19 +1014,19 @@
 
 "@floating-ui/core@^0.7.2":
   version "0.7.2"
-  resolved "https://registry.yarnpkg.com/@floating-ui/core/-/core-0.7.2.tgz#f7af9613d080dc29360e77c970965b79b524d45a"
+  resolved "https://registry.npmjs.org/@floating-ui/core/-/core-0.7.2.tgz"
   integrity sha512-FRVAkSNU/vGXLIsgbggcs70GkXKEOXgBBbNpYPNHSaKsCAMMd00NrjbtKTesxkdv9xm9N3+XiDlcFGY6WnatBg==
 
 "@floating-ui/dom@^0.5.1":
   version "0.5.2"
-  resolved "https://registry.yarnpkg.com/@floating-ui/dom/-/dom-0.5.2.tgz#908f3febbfc0d6696d70921616ec194fe07af183"
+  resolved "https://registry.npmjs.org/@floating-ui/dom/-/dom-0.5.2.tgz"
   integrity sha512-z1DnEa7F3d8Fm/eXSbii8UEGpcjZGkQaYYUI0WpEVgD3vBfebDW8j/3ysusxonuMexoigA+A3b/fYH7sEqiwyg==
   dependencies:
     "@floating-ui/core" "^0.7.2"
 
 "@floating-ui/react-dom-interactions@^0.6.3":
   version "0.6.3"
-  resolved "https://registry.yarnpkg.com/@floating-ui/react-dom-interactions/-/react-dom-interactions-0.6.3.tgz#895c52cb06bf5ea73c00f1074c75b0535e0046bc"
+  resolved "https://registry.npmjs.org/@floating-ui/react-dom-interactions/-/react-dom-interactions-0.6.3.tgz"
   integrity sha512-xvbGEtBtA7JaEngnHQjROArv2onRp3oJIpb4+bEN5EGJf0hBYDY0vD8vFGPz/5TQwN++hb6icOB1QwdOnffMzw==
   dependencies:
     "@floating-ui/react-dom" "^0.7.1"
@@ -1208,50 +1035,95 @@
 
 "@floating-ui/react-dom@^0.7.1":
   version "0.7.1"
-  resolved "https://registry.yarnpkg.com/@floating-ui/react-dom/-/react-dom-0.7.1.tgz#e0eb57cb05c7762d4c904bfbae73148684578d66"
+  resolved "https://registry.npmjs.org/@floating-ui/react-dom/-/react-dom-0.7.1.tgz"
   integrity sha512-Dd7e8AupUjzcjeGf1g3EItf/QRtEWKF5GGyEs5WA5n3zlHvEgZ4XrZM6ANhUnzgE3pUQAaXkcXLnibgFp1YBRw==
   dependencies:
     "@floating-ui/dom" "^0.5.1"
     use-isomorphic-layout-effect "^1.1.1"
 
-"@jridgewell/resolve-uri@^3.0.3":
-  version "3.0.5"
-  resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.0.5.tgz#68eb521368db76d040a6315cdb24bf2483037b9c"
-  integrity sha512-VPeQ7+wH0itvQxnG+lIzWgkysKIr3L9sslimFW55rHMdGu/qCQ5z5h9zq4gI8uBtqkpHhsF4Z/OwExufUCThew==
+"@jridgewell/gen-mapping@^0.3.12", "@jridgewell/gen-mapping@^0.3.5":
+  version "0.3.13"
+  resolved "https://registry.npmjs.org/@jridgewell/gen-mapping/-/gen-mapping-0.3.13.tgz"
+  integrity sha512-2kkt/7niJ6MgEPxF0bYdQ6etZaA+fQvDcLKckhy1yIQOzaoKjBBjSj63/aLVjYE3qhRt5dvM+uUyfCg6UKCBbA==
+  dependencies:
+    "@jridgewell/sourcemap-codec" "^1.5.0"
+    "@jridgewell/trace-mapping" "^0.3.24"
 
-"@jridgewell/sourcemap-codec@^1.4.10":
-  version "1.4.11"
-  resolved "https://registry.yarnpkg.com/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.11.tgz#771a1d8d744eeb71b6adb35808e1a6c7b9b8c8ec"
-  integrity sha512-Fg32GrJo61m+VqYSdRSjRXMjQ06j8YIYfcTqndLYVAaHmroZHLJZCydsWBOTDqXS2v+mjxohBWEMfg97GXmYQg==
+"@jridgewell/resolve-uri@^3.1.0":
+  version "3.1.2"
+  resolved "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz"
+  integrity sha512-bRISgCIjP20/tbWSPWMEi54QVPRZExkuD9lJL+UIxUKtwVJA8wW1Trb1jMs1RFXo1CBTNZ/5hpC9QvmKWdopKw==
 
-"@jridgewell/trace-mapping@^0.3.0":
-  version "0.3.4"
-  resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.4.tgz#f6a0832dffd5b8a6aaa633b7d9f8e8e94c83a0c3"
-  integrity sha512-vFv9ttIedivx0ux3QSjhgtCVjPZd5l46ZOMDSCwnH1yUO2e964gO8LZGyv2QkqcgR6TnBU1v+1IFqmeoG+0UJQ==
+"@jridgewell/source-map@^0.3.3":
+  version "0.3.11"
+  resolved "https://registry.npmjs.org/@jridgewell/source-map/-/source-map-0.3.11.tgz"
+  integrity sha512-ZMp1V8ZFcPG5dIWnQLr3NSI1MiCU7UETdS/A0G8V/XWHvJv3ZsFqutJn1Y5RPmAPX6F3BiE397OqveU/9NCuIA==
   dependencies:
-    "@jridgewell/resolve-uri" "^3.0.3"
-    "@jridgewell/sourcemap-codec" "^1.4.10"
+    "@jridgewell/gen-mapping" "^0.3.5"
+    "@jridgewell/trace-mapping" "^0.3.25"
+
+"@jridgewell/sourcemap-codec@^1.4.14", "@jridgewell/sourcemap-codec@^1.5.0":
+  version "1.5.5"
+  resolved "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.5.5.tgz"
+  integrity sha512-cYQ9310grqxueWbl+WuIUIaiUaDcj7WOq5fVhEljNVgRfOUhY9fy2zTvfoqWsnebh8Sl70VScFbICvJnLKB0Og==
 
-"@nodelib/fs.scandir@2.1.5":
-  version "2.1.5"
-  resolved "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz"
-  integrity sha512-vq24Bq3ym5HEQm2NKCr3yXDwjc7vTsEThRDnkp2DK9p1uqLR+DHurm/NOTo0KG7HYHU7eppKZj3MyqYuMBf62g==
+"@jridgewell/trace-mapping@^0.3.0", "@jridgewell/trace-mapping@^0.3.24", "@jridgewell/trace-mapping@^0.3.25", "@jridgewell/trace-mapping@^0.3.28":
+  version "0.3.31"
+  resolved "https://registry.npmjs.org/@jridgewell/trace-mapping/-/trace-mapping-0.3.31.tgz"
+  integrity sha512-zzNR+SdQSDJzc8joaeP8QQoCQr8NuYx2dIIytl1QeBEZHJ9uW6hebsrYgbz8hJwUQao3TWCMtmfV8Nu1twOLAw==
   dependencies:
-    "@nodelib/fs.stat" "2.0.5"
-    run-parallel "^1.1.9"
+    "@jridgewell/resolve-uri" "^3.1.0"
+    "@jridgewell/sourcemap-codec" "^1.4.14"
 
-"@nodelib/fs.stat@2.0.5", "@nodelib/fs.stat@^2.0.2":
-  version "2.0.5"
-  resolved "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz"
-  integrity sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A==
+"@jsonjoy.com/base64@^1.1.2":
+  version "1.1.2"
+  resolved "https://registry.npmjs.org/@jsonjoy.com/base64/-/base64-1.1.2.tgz"
+  integrity sha512-q6XAnWQDIMA3+FTiOYajoYqySkO+JSat0ytXGSuRdq9uXE7o92gzuQwQM14xaCRlBLGq3v5miDGC4vkVTn54xA==
+
+"@jsonjoy.com/buffers@^1.0.0", "@jsonjoy.com/buffers@^1.2.0":
+  version "1.2.0"
+  resolved "https://registry.npmjs.org/@jsonjoy.com/buffers/-/buffers-1.2.0.tgz"
+  integrity sha512-6RX+W5a+ZUY/c/7J5s5jK9UinLfJo5oWKh84fb4X0yK2q4WXEWUWZWuEMjvCb1YNUQhEAhUfr5scEGOH7jC4YQ==
+
+"@jsonjoy.com/codegen@^1.0.0":
+  version "1.0.0"
+  resolved "https://registry.npmjs.org/@jsonjoy.com/codegen/-/codegen-1.0.0.tgz"
+  integrity sha512-E8Oy+08cmCf0EK/NMxpaJZmOxPqM+6iSe2S4nlSBrPZOORoDJILxtbSUEDKQyTamm/BVAhIGllOBNU79/dwf0g==
+
+"@jsonjoy.com/json-pack@^1.11.0":
+  version "1.21.0"
+  resolved "https://registry.npmjs.org/@jsonjoy.com/json-pack/-/json-pack-1.21.0.tgz"
+  integrity sha512-+AKG+R2cfZMShzrF2uQw34v3zbeDYUqnQ+jg7ORic3BGtfw9p/+N6RJbq/kkV8JmYZaINknaEQ2m0/f693ZPpg==
+  dependencies:
+    "@jsonjoy.com/base64" "^1.1.2"
+    "@jsonjoy.com/buffers" "^1.2.0"
+    "@jsonjoy.com/codegen" "^1.0.0"
+    "@jsonjoy.com/json-pointer" "^1.0.2"
+    "@jsonjoy.com/util" "^1.9.0"
+    hyperdyperid "^1.2.0"
+    thingies "^2.5.0"
+    tree-dump "^1.1.0"
+
+"@jsonjoy.com/json-pointer@^1.0.2":
+  version "1.0.2"
+  resolved "https://registry.npmjs.org/@jsonjoy.com/json-pointer/-/json-pointer-1.0.2.tgz"
+  integrity sha512-Fsn6wM2zlDzY1U+v4Nc8bo3bVqgfNTGcn6dMgs6FjrEnt4ZCe60o6ByKRjOGlI2gow0aE/Q41QOigdTqkyK5fg==
+  dependencies:
+    "@jsonjoy.com/codegen" "^1.0.0"
+    "@jsonjoy.com/util" "^1.9.0"
 
-"@nodelib/fs.walk@^1.2.3":
-  version "1.2.8"
-  resolved "https://registry.npmjs.org/@nodelib/fs.walk/-/fs.walk-1.2.8.tgz"
-  integrity sha512-oGB+UxlgWcgQkgwo8GcEGwemoTFt3FIO9ababBmaGwXIoBKZ+GTy0pP185beGg7Llih/NSHSV2XAs1lnznocSg==
+"@jsonjoy.com/util@^1.9.0":
+  version "1.9.0"
+  resolved "https://registry.npmjs.org/@jsonjoy.com/util/-/util-1.9.0.tgz"
+  integrity sha512-pLuQo+VPRnN8hfPqUTLTHk126wuYdXVxE6aDmjSeV4NCAgyxWbiOIeNJVtID3h1Vzpoi9m4jXezf73I6LgabgQ==
   dependencies:
-    "@nodelib/fs.scandir" "2.1.5"
-    fastq "^1.6.0"
+    "@jsonjoy.com/buffers" "^1.0.0"
+    "@jsonjoy.com/codegen" "^1.0.0"
+
+"@leichtgewicht/ip-codec@^2.0.1":
+  version "2.0.5"
+  resolved "https://registry.npmjs.org/@leichtgewicht/ip-codec/-/ip-codec-2.0.5.tgz"
+  integrity sha512-Vo+PSpZG2/fmgmiNzYK9qWRh8h/CHrwD0mo1h1DzL4yzHNSfWYujGTYsWGreD000gcgmZ7K4Ys6Tx9TxtsKdDw==
 
 "@trysound/sax@0.2.0":
   version "0.2.0"
@@ -1266,17 +1138,17 @@
     "@types/connect" "*"
     "@types/node" "*"
 
-"@types/bonjour@^3.5.9":
-  version "3.5.10"
-  resolved "https://registry.npmjs.org/@types/bonjour/-/bonjour-3.5.10.tgz"
-  integrity sha512-p7ienRMiS41Nu2/igbJxxLDWrSZ0WxM8UQgCeO9KhoVF7cOVFkrKsiDr1EsJIla8vV3oEEjGcz11jc5yimhzZw==
+"@types/bonjour@^3.5.13":
+  version "3.5.13"
+  resolved "https://registry.npmjs.org/@types/bonjour/-/bonjour-3.5.13.tgz"
+  integrity sha512-z9fJ5Im06zvUL548KvYNecEVlA7cVDkGUi6kZusb04mpyEFKCIZJvloCcmpmLaIahDpOQGHaHmG6imtPMmPXGQ==
   dependencies:
     "@types/node" "*"
 
-"@types/connect-history-api-fallback@^1.3.5":
-  version "1.3.5"
-  resolved "https://registry.npmjs.org/@types/connect-history-api-fallback/-/connect-history-api-fallback-1.3.5.tgz"
-  integrity sha512-h8QJa8xSb1WD4fpKBDcATDNGXghFj6/3GRWG6dhmRcu0RX1Ubasur2Uvx5aeEwlf0MwblEC2bMzzMQntxnw/Cw==
+"@types/connect-history-api-fallback@^1.5.4":
+  version "1.5.4"
+  resolved "https://registry.npmjs.org/@types/connect-history-api-fallback/-/connect-history-api-fallback-1.5.4.tgz"
+  integrity sha512-n6Cr2xS1h4uAulPRdlw6Jl6s1oG8KrVilPN2yUITEs+K48EzMJJ3W1xy8K5eWuFvjp3R74AOIGSmp2UfBJ8HFw==
   dependencies:
     "@types/express-serve-static-core" "*"
     "@types/node" "*"
@@ -1288,68 +1160,76 @@
   dependencies:
     "@types/node" "*"
 
-"@types/eslint-scope@^3.7.3":
-  version "3.7.3"
-  resolved "https://registry.yarnpkg.com/@types/eslint-scope/-/eslint-scope-3.7.3.tgz#125b88504b61e3c8bc6f870882003253005c3224"
-  integrity sha512-PB3ldyrcnAicT35TWPs5IcwKD8S333HMaa2VVv4+wdvebJkjWuW/xESoB8IwRcog8HYVYamb1g/R31Qv5Bx03g==
+"@types/eslint-scope@^3.7.7":
+  version "3.7.7"
+  resolved "https://registry.npmjs.org/@types/eslint-scope/-/eslint-scope-3.7.7.tgz"
+  integrity sha512-MzMFlSLBqNF2gcHWO0G1vP/YQyfvrxZ0bF+u7mzUdZ1/xK4A4sru+nraZz5i3iEIk1l1uyicaDVTB4QbbEkAYg==
   dependencies:
     "@types/eslint" "*"
     "@types/estree" "*"
 
 "@types/eslint@*":
-  version "8.2.1"
-  resolved "https://registry.npmjs.org/@types/eslint/-/eslint-8.2.1.tgz"
-  integrity sha512-UP9rzNn/XyGwb5RQ2fok+DzcIRIYwc16qTXse5+Smsy8MOIccCChT15KAwnsgQx4PzJkaMq4myFyZ4CL5TjhIQ==
+  version "9.6.1"
+  resolved "https://registry.npmjs.org/@types/eslint/-/eslint-9.6.1.tgz"
+  integrity sha512-FXx2pKgId/WyYo2jXw63kk7/+TY7u7AziEJxJAnSFzHlqTAS3Ync6SvgYAN/k4/PQpnnVuzoMuVnByKK2qp0ag==
   dependencies:
     "@types/estree" "*"
     "@types/json-schema" "*"
 
-"@types/estree@*":
-  version "0.0.50"
-  resolved "https://registry.npmjs.org/@types/estree/-/estree-0.0.50.tgz"
-  integrity sha512-C6N5s2ZFtuZRj54k2/zyRhNDjJwwcViAM3Nbm8zjBpbqAdZ00mr0CFxvSKeO8Y/e03WVFLpQMdHYVfUd6SB+Hw==
+"@types/estree@*", "@types/estree@^1.0.8":
+  version "1.0.8"
+  resolved "https://registry.npmjs.org/@types/estree/-/estree-1.0.8.tgz"
+  integrity sha512-dWHzHa2WqEXI/O1E9OjrocMTKJl2mSrEolh1Iomrv6U+JuNwaHXsXx9bLu5gG7BUWFIN0skIQJQ/L1rIex4X6w==
 
-"@types/estree@^0.0.51":
-  version "0.0.51"
-  resolved "https://registry.yarnpkg.com/@types/estree/-/estree-0.0.51.tgz#cfd70924a25a3fd32b218e5e420e6897e1ac4f40"
-  integrity sha512-CuPgU6f3eT/XgKKPqKd/gLZV1Xmvf1a2R5POBOGQa6uv82xpls89HU5zKeVoyR8XzHd1RGNOlQlvUe3CFkjWNQ==
-
-"@types/express-serve-static-core@*", "@types/express-serve-static-core@^4.17.18":
-  version "4.17.28"
-  resolved "https://registry.npmjs.org/@types/express-serve-static-core/-/express-serve-static-core-4.17.28.tgz"
-  integrity sha512-P1BJAEAW3E2DJUlkgq4tOL3RyMunoWXqbSCygWo5ZIWTjUgN1YnaXWW4VWl/oc8vs/XoYibEGBKP0uZyF4AHig==
+"@types/express-serve-static-core@*", "@types/express-serve-static-core@^4.17.21", "@types/express-serve-static-core@^4.17.33":
+  version "4.19.7"
+  resolved "https://registry.npmjs.org/@types/express-serve-static-core/-/express-serve-static-core-4.19.7.tgz"
+  integrity sha512-FvPtiIf1LfhzsaIXhv/PHan/2FeQBbtBDtfX2QfvPxdUelMDEckK08SM6nqo1MIZY3RUlfA+HV8+hFUSio78qg==
   dependencies:
     "@types/node" "*"
     "@types/qs" "*"
     "@types/range-parser" "*"
+    "@types/send" "*"
 
-"@types/express@*", "@types/express@^4.17.13":
-  version "4.17.13"
-  resolved "https://registry.npmjs.org/@types/express/-/express-4.17.13.tgz"
-  integrity sha512-6bSZTPaTIACxn48l50SR+axgrqm6qXFIxrdAKaG6PaJk3+zuUr35hBlgT7vOmJcum+OEaIBLtHV/qloEAFITeA==
+"@types/express@*", "@types/express@^4.17.13", "@types/express@^4.17.21":
+  version "4.17.23"
+  resolved "https://registry.npmjs.org/@types/express/-/express-4.17.23.tgz"
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-    "@types/express-serve-static-core" "^4.17.18"
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     "@types/qs" "*"
     "@types/serve-static" "*"
 
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+  resolved "https://registry.npmjs.org/@types/node-forge/-/node-forge-1.3.14.tgz"
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   resolved "https://registry.npmjs.org/@types/node/-/node-16.11.12.tgz"
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+  resolved "https://registry.npmjs.org/@types/send/-/send-0.17.5.tgz"
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+  dependencies:
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   dependencies:
-    "@types/mime" "^1"
+    "@types/http-errors" "*"
     "@types/node" "*"
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-    "@webassemblyjs/helper-wasm-bytecode" "1.11.1"
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+    "@webassemblyjs/helper-wasm-bytecode" "1.13.2"
 
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-    "@webassemblyjs/helper-api-error" "1.11.1"
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-    "@webassemblyjs/wasm-gen" "1.11.1"
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+    "@webassemblyjs/utf8" "1.13.2"
+
+"@webassemblyjs/wasm-opt@1.14.1":
+  version "1.14.1"
+  resolved "https://registry.npmjs.org/@webassemblyjs/wasm-opt/-/wasm-opt-1.14.1.tgz"
+  integrity sha512-PTcKLUNvBqnY2U6E5bdOQcSM+oVP/PmrDY9NzowJjislEjwP/C4an2303MCVS2Mg9d3AJpIGdUFIQQWbPds0Sw==
+  dependencies:
+    "@webassemblyjs/ast" "1.14.1"
+    "@webassemblyjs/helper-buffer" "1.14.1"
+    "@webassemblyjs/wasm-gen" "1.14.1"
+    "@webassemblyjs/wasm-parser" "1.14.1"
+
+"@webassemblyjs/wasm-parser@^1.14.1", "@webassemblyjs/wasm-parser@1.14.1":
+  version "1.14.1"
+  resolved "https://registry.npmjs.org/@webassemblyjs/wasm-parser/-/wasm-parser-1.14.1.tgz"
+  integrity sha512-JLBl+KZ0R5qB7mCnud/yyX08jWFw5MsoalJ1pQ4EdFlgj9VdXKGuENGsiCIjegI1W7p91rUlcB/LB5yRJKNTcQ==
+  dependencies:
+    "@webassemblyjs/ast" "1.14.1"
+    "@webassemblyjs/helper-api-error" "1.13.2"
+    "@webassemblyjs/helper-wasm-bytecode" "1.13.2"
+    "@webassemblyjs/ieee754" "1.13.2"
+    "@webassemblyjs/leb128" "1.13.2"
+    "@webassemblyjs/utf8" "1.13.2"
+
+"@webassemblyjs/wast-printer@1.14.1":
+  version "1.14.1"
+  resolved "https://registry.npmjs.org/@webassemblyjs/wast-printer/-/wast-printer-1.14.1.tgz"
+  integrity sha512-kPSSXE6De1XOR820C90RIo2ogvZG+c3KiHzqUoO/F34Y2shGzesfqv7o57xrxovZJH/MetF5UjroJ/R/3isoiw==
+  dependencies:
+    "@webassemblyjs/ast" "1.14.1"
     "@xtuc/long" "4.2.2"
 
 "@webpack-cli/configtest@^1.1.1":
   version "1.1.1"
-  resolved "https://registry.yarnpkg.com/@webpack-cli/configtest/-/configtest-1.1.1.tgz#9f53b1b7946a6efc2a749095a4f450e2932e8356"
+  resolved "https://registry.npmjs.org/@webpack-cli/configtest/-/configtest-1.1.1.tgz"
   integrity sha512-1FBc1f9G4P/AxMqIgfZgeOTuRnwZMten8E7zap5zgpPInnCrP8D4Q81+4CWIch8i/Nf7nXjP0v6CjjbHOrXhKg==
 
 "@webpack-cli/info@^1.4.1":
   version "1.4.1"
-  resolved "https://registry.yarnpkg.com/@webpack-cli/info/-/info-1.4.1.tgz#2360ea1710cbbb97ff156a3f0f24556e0fc1ebea"
+  resolved "https://registry.npmjs.org/@webpack-cli/info/-/info-1.4.1.tgz"
   integrity sha512-PKVGmazEq3oAo46Q63tpMr4HipI3OPfP7LiNOEJg963RMgT0rqheag28NCML0o3GIzA3DmxP1ZIAv9oTX1CUIA==
   dependencies:
     envinfo "^7.7.3"
 
 "@webpack-cli/serve@^1.6.1":
   version "1.6.1"
-  resolved "https://registry.yarnpkg.com/@webpack-cli/serve/-/serve-1.6.1.tgz#0de2875ac31b46b6c5bb1ae0a7d7f0ba5678dffe"
+  resolved "https://registry.npmjs.org/@webpack-cli/serve/-/serve-1.6.1.tgz"
   integrity sha512-gNGTiTrjEVQ0OcVnzsRSqTxaBSr+dmTfm+qJsCDluky8uhdLWep7Gcr62QsAKHTMxjCS/8nEITsmFAhfIx+QSw==
 
 "@xtuc/ieee754@^1.2.0":
@@ -1552,31 +1441,23 @@
   resolved "https://registry.npmjs.org/@xtuc/long/-/long-4.2.2.tgz"
   integrity sha512-NuHqBY1PB/D8xU6s/thBgOAiAP7HOYDQ32+BFZILJ8ivkUkAHQnWfn6WhL79Owj1qmUnoN/YPhktdIoucipkAQ==
 
-accepts@~1.3.4, accepts@~1.3.5, accepts@~1.3.7:
-  version "1.3.7"
-  resolved "https://registry.npmjs.org/accepts/-/accepts-1.3.7.tgz"
-  integrity sha512-Il80Qs2WjYlJIBNzNkK6KYqlVMTbZLXgHx2oT0pU/fjRHyEp+PEfEPY0R3WCwAGVOtauxh1hOxNgIf5bv7dQpA==
+accepts@~1.3.4, accepts@~1.3.8:
+  version "1.3.8"
+  resolved "https://registry.npmjs.org/accepts/-/accepts-1.3.8.tgz"
+  integrity sha512-PYAthTa2m2VKxuvSD3DPC/Gy+U+sOA1LAuT8mkmRuvw+NACSaeXEQ+NHcVF7rONl6qcaxV3Uuemwawk+7+SJLw==
   dependencies:
-    mime-types "~2.1.24"
-    negotiator "0.6.2"
+    mime-types "~2.1.34"
+    negotiator "0.6.3"
 
-acorn-import-assertions@^1.7.6:
-  version "1.8.0"
-  resolved "https://registry.npmjs.org/acorn-import-assertions/-/acorn-import-assertions-1.8.0.tgz"
-  integrity sha512-m7VZ3jwz4eK6A4Vtt8Ew1/mNbP24u0FhdyfA7fSvnJR6LMdfOYnmuIrrJAgrYfYJ10F/otaHTtrtrtmHdMNzEw==
-
-acorn@^8.4.1:
-  version "8.6.0"
-  resolved "https://registry.npmjs.org/acorn/-/acorn-8.6.0.tgz"
-  integrity sha512-U1riIR+lBSNi3IbxtaHOIKdH8sLFv3NYfNv8sg7ZsNhcfl4HF2++BfqqrNAxoCLQW1iiylOj76ecnaUxz+z9yw==
+acorn-import-phases@^1.0.3:
+  version "1.0.4"
+  resolved "https://registry.npmjs.org/acorn-import-phases/-/acorn-import-phases-1.0.4.tgz"
+  integrity sha512-wKmbr/DDiIXzEOiWrTTUcDm24kQ2vGfZQvM2fwg2vXqR5uW6aapr7ObPtj1th32b9u90/Pf4AItvdTh42fBmVQ==
 
-aggregate-error@^3.0.0:
-  version "3.1.0"
-  resolved "https://registry.npmjs.org/aggregate-error/-/aggregate-error-3.1.0.tgz"
-  integrity sha512-4I7Td01quW/RpocfNayFdFVk1qSuoh0E7JrbRJ16nH01HhKFQ88INq9Sd+nd72zqRySlr9BmDA8xlEJ6vJMrYA==
-  dependencies:
-    clean-stack "^2.0.0"
-    indent-string "^4.0.0"
+acorn@^8.14.0, acorn@^8.15.0:
+  version "8.15.0"
+  resolved "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz"
+  integrity sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==
 
 ajv-formats@^2.1.1:
   version "2.1.1"
@@ -1597,7 +1478,14 @@ ajv-keywords@^5.0.0:
   dependencies:
     fast-deep-equal "^3.1.3"
 
-ajv@^6.12.4, ajv@^6.12.5:
+ajv-keywords@^5.1.0:
+  version "5.1.0"
+  resolved "https://registry.npmjs.org/ajv-keywords/-/ajv-keywords-5.1.0.tgz"
+  integrity sha512-YCS/JNFAUyr5vAuhk1DWm1CBxRHW9LbJ2ozWeemrIqpbsqKjHVxYPyi5GC0rjZIT5JxJ3virVTS8wk4i/Z+krw==
+  dependencies:
+    fast-deep-equal "^3.1.3"
+
+ajv@^6.12.4, ajv@^6.9.1:
   version "6.12.6"
   resolved "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz"
   integrity sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==
@@ -1607,7 +1495,17 @@ ajv@^6.12.4, ajv@^6.12.5:
     json-schema-traverse "^0.4.1"
     uri-js "^4.2.2"
 
-ajv@^8.0.0, ajv@^8.8.0:
+ajv@^8.0.0:
+  version "8.8.2"
+  resolved "https://registry.npmjs.org/ajv/-/ajv-8.8.2.tgz"
+  integrity sha512-x9VuX+R/jcFj1DHo/fCp99esgGDWiHENrKxaCENuCxpoMCmAt/COCGVDwA7kleEpEzJjDnvh3yGoOuLu0Dtllw==
+  dependencies:
+    fast-deep-equal "^3.1.1"
+    json-schema-traverse "^1.0.0"
+    require-from-string "^2.0.2"
+    uri-js "^4.2.2"
+
+ajv@^8.8.0, ajv@^8.8.2:
   version "8.8.2"
   resolved "https://registry.npmjs.org/ajv/-/ajv-8.8.2.tgz"
   integrity sha512-x9VuX+R/jcFj1DHo/fCp99esgGDWiHENrKxaCENuCxpoMCmAt/COCGVDwA7kleEpEzJjDnvh3yGoOuLu0Dtllw==
@@ -1617,39 +1515,37 @@ ajv@^8.0.0, ajv@^8.8.0:
     require-from-string "^2.0.2"
     uri-js "^4.2.2"
 
+ajv@^8.9.0:
+  version "8.17.1"
+  resolved "https://registry.npmjs.org/ajv/-/ajv-8.17.1.tgz"
+  integrity sha512-B/gBuNg5SiMTrPkC+A2+cW0RszwxYmn6VYxB/inlBStS5nx6xHIt/ehKRhIMhqusl7a8LjQoZnjCs5vhwxOQ1g==
+  dependencies:
+    fast-deep-equal "^3.1.3"
+    fast-uri "^3.0.1"
+    json-schema-traverse "^1.0.0"
+    require-from-string "^2.0.2"
+
 alphanum-sort@^1.0.2:
   version "1.0.2"
   resolved "https://registry.npmjs.org/alphanum-sort/-/alphanum-sort-1.0.2.tgz"
-  integrity sha1-l6ERlkmyEa0zaR2fn0hqjsn74KM=
+  integrity sha1-l6ERlkmyEa0zaR2fn0hqjsn74KM= sha512-0FcBfdcmaumGPQ0qPn7Q5qTgz/ooXgIyp1rf8ik5bGX8mpE2YHjC0P/eyQvxu1GURYQgq9ozf2mteQ5ZD9YiyQ==
 
 ansi-html-community@^0.0.8:
   version "0.0.8"
   resolved "https://registry.npmjs.org/ansi-html-community/-/ansi-html-community-0.0.8.tgz"
   integrity sha512-1APHAyr3+PCamwNw3bXCPp4HFLONZt/yIH0sZp0/469KWNTEy+qN5jQ3GVX6DMZ1UXAi34yVwtTeaG/HpBuuzw==
 
-ansi-regex@^6.0.1:
-  version "6.0.1"
-  resolved "https://registry.npmjs.org/ansi-regex/-/ansi-regex-6.0.1.tgz"
-  integrity sha512-n5M855fKb2SsfMIiFFoVrABHJC8QtHwVx+mHWP3QcEqBHYienj5dHSgjbxtC0WEZXYt4wcD6zrQElDPhFuZgfA==
-
-ansi-styles@^3.2.1:
-  version "3.2.1"
-  resolved "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz"
-  integrity sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==
-  dependencies:
-    color-convert "^1.9.0"
-
 anymatch@~3.1.2:
-  version "3.1.2"
-  resolved "https://registry.npmjs.org/anymatch/-/anymatch-3.1.2.tgz"
-  integrity sha512-P43ePfOAIupkguHUycrc4qJ9kz8ZiuOUijaETwX7THt0Y/GNK7v0aa8rY816xWjZ7rJdA5XdMcpVFTKMq+RvWg==
+  version "3.1.3"
+  resolved "https://registry.npmjs.org/anymatch/-/anymatch-3.1.3.tgz"
+  integrity sha512-KMReFUr0B4t+D+OBkjR3KYqvocp2XaSzO55UcB6mgQMd3KbcE+mWTyvVV7D/zsdEbNnV6acZUutkiHQXvTr1Rw==
   dependencies:
     normalize-path "^3.0.0"
     picomatch "^2.0.4"
 
 aria-hidden@^1.1.3:
   version "1.1.3"
-  resolved "https://registry.yarnpkg.com/aria-hidden/-/aria-hidden-1.1.3.tgz#bb48de18dc84787a3c6eee113709c473c64ec254"
+  resolved "https://registry.npmjs.org/aria-hidden/-/aria-hidden-1.1.3.tgz"
   integrity sha512-RhVWFtKH5BiGMycI72q2RAFMLQi8JP9bLuQXgR5a8Znp7P5KOIADSJeyfI8PCVxLEp067B2HbP5JIiI/PXIZeA==
   dependencies:
     tslib "^1.0.0"
@@ -1657,28 +1553,11 @@ aria-hidden@^1.1.3:
 array-flatten@1.1.1:
   version "1.1.1"
   resolved "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz"
-  integrity sha1-ml9pkFGx5wczKPKgCJaLZOopVdI=
-
-array-flatten@^2.1.0:
-  version "2.1.2"
-  resolved "https://registry.npmjs.org/array-flatten/-/array-flatten-2.1.2.tgz"
-  integrity sha512-hNfzcOV8W4NdualtqBFPyVO+54DSJuZGY9qT4pRroB6S9e3iiido2ISIC5h9R2sPJ8H3FHCIiEnsv1lPXO3KtQ==
-
-array-union@^2.1.0:
-  version "2.1.0"
-  resolved "https://registry.npmjs.org/array-union/-/array-union-2.1.0.tgz"
-  integrity sha512-HGyxoOTYUyCM6stUe6EJgnd4EoewAI7zMdfqO+kGjnlZmBDz/cR5pf8r/cR4Wq60sL/p0IkcjUEEPwS3GFrIyw==
-
-async@^2.6.2:
-  version "2.6.3"
-  resolved "https://registry.npmjs.org/async/-/async-2.6.3.tgz"
-  integrity sha512-zflvls11DCy+dQWzTW2dzuilv8Z5X/pjfmZOWba6TNIVDm+2UDaJmXSOXlasHKfNBs8oo3M0aT50fDEWfKZjXg==
-  dependencies:
-    lodash "^4.17.14"
+  integrity sha1-ml9pkFGx5wczKPKgCJaLZOopVdI= sha512-PCVAQswWemu6UdxsDFFX/+gVeYqKAod3D3UVm91jHwynguOwAvYPhx8nNlM++NqRcK6CxxpUafjmhIdKiHibqg==
 
 autoprefixer@^10.4.4:
   version "10.4.4"
-  resolved "https://registry.yarnpkg.com/autoprefixer/-/autoprefixer-10.4.4.tgz#3e85a245b32da876a893d3ac2ea19f01e7ea5a1e"
+  resolved "https://registry.npmjs.org/autoprefixer/-/autoprefixer-10.4.4.tgz"
   integrity sha512-Tm8JxsB286VweiZ5F0anmbyGiNI3v3wGv3mz9W+cxEDYB/6jbnj6GM9H9mK3wIL8ftgl+C07Lcwb8PG5PCCPzA==
   dependencies:
     browserslist "^4.20.2"
@@ -1690,7 +1569,7 @@ autoprefixer@^10.4.4:
 
 babel-loader@^8.2.4:
   version "8.2.4"
-  resolved "https://registry.yarnpkg.com/babel-loader/-/babel-loader-8.2.4.tgz#95f5023c791b2e9e2ca6f67b0984f39c82ff384b"
+  resolved "https://registry.npmjs.org/babel-loader/-/babel-loader-8.2.4.tgz"
   integrity sha512-8dytA3gcvPPPv4Grjhnt8b5IIiTcq/zeXOPk4iTYI0SVXcsmuGg7JtBRDp8S9X+gJfhQ8ektjXZlDu1Bb33U8A==
   dependencies:
     find-cache-dir "^3.3.1"
@@ -1716,7 +1595,7 @@ babel-plugin-polyfill-corejs2@^0.3.0:
 
 babel-plugin-polyfill-corejs3@^0.5.0:
   version "0.5.2"
-  resolved "https://registry.yarnpkg.com/babel-plugin-polyfill-corejs3/-/babel-plugin-polyfill-corejs3-0.5.2.tgz#aabe4b2fa04a6e038b688c5e55d44e78cd3a5f72"
+  resolved "https://registry.npmjs.org/babel-plugin-polyfill-corejs3/-/babel-plugin-polyfill-corejs3-0.5.2.tgz"
   integrity sha512-G3uJih0XWiID451fpeFaYGVuxHEjzKTHtc9uGFEjR6hHrvNzeS/PX+LLLcetJcytsB5m4j+K3o/EpXJNb/5IEQ==
   dependencies:
     "@babel/helper-define-polyfill-provider" "^0.3.1"
@@ -1734,10 +1613,15 @@ balanced-match@^1.0.0:
   resolved "https://registry.npmjs.org/balanced-match/-/balanced-match-1.0.2.tgz"
   integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
 
+baseline-browser-mapping@^2.8.9:
+  version "2.8.17"
+  resolved "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.8.17.tgz"
+  integrity sha512-j5zJcx6golJYTG6c05LUZ3Z8Gi+M62zRT/ycz4Xq4iCOdpcxwg7ngEYD4KA0eWZC7U17qh/Smq8bYbACJ0ipBA==
+
 batch@0.6.1:
   version "0.6.1"
   resolved "https://registry.npmjs.org/batch/-/batch-0.6.1.tgz"
-  integrity sha1-3DQxT05nkxgJP8dgJyUl+UvyXBY=
+  integrity sha1-3DQxT05nkxgJP8dgJyUl+UvyXBY= sha512-x+VAiMRL6UPkx+kudNvxTl6hB2XNNCG2r+7wixVfIYwu/2HKRXimwQyaumLjMveWvT2Hkd/cAJw+QBMfJ/EKVw==
 
 big.js@^5.2.2:
   version "5.2.2"
@@ -1745,112 +1629,85 @@ big.js@^5.2.2:
   integrity sha512-vyL2OymJxmarO8gxMr0mhChsO9QGwhynfuu4+MHTAW6czfq9humCB7rKpUjDd9YUiDPU4mzpyupFSvOClAwbmQ==
 
 binary-extensions@^2.0.0:
-  version "2.2.0"
-  resolved "https://registry.npmjs.org/binary-extensions/-/binary-extensions-2.2.0.tgz"
-  integrity sha512-jDctJ/IVQbZoJykoeHbhXpOlNBqGNcwXJKJog42E5HDPUwQTSdjCHdihjj0DlnheQ7blbT6dHOafNAiS8ooQKA==
+  version "2.3.0"
+  resolved "https://registry.npmjs.org/binary-extensions/-/binary-extensions-2.3.0.tgz"
+  integrity sha512-Ceh+7ox5qe7LJuLHoY0feh3pHuUDHAcRUeyL2VYghZwfpkNIy/+8Ocg0a3UuSoYzavmylwuLWQOf3hl0jjMMIw==
 
-body-parser@1.19.0:
-  version "1.19.0"
-  resolved "https://registry.npmjs.org/body-parser/-/body-parser-1.19.0.tgz"
-  integrity sha512-dhEPs72UPbDnAQJ9ZKMNTP6ptJaionhP5cBb541nXPlW60Jepo9RV/a4fX4XWW9CuFNK22krhrj1+rgzifNCsw==
+body-parser@1.20.3:
+  version "1.20.3"
+  resolved "https://registry.npmjs.org/body-parser/-/body-parser-1.20.3.tgz"
+  integrity sha512-7rAxByjUMqQ3/bHJy7D6OGXvx/MMc4IqBn/X0fcM1QUcAItpZrBEYhWGem+tzXH90c+G01ypMcYJBO9Y30203g==
   dependencies:
-    bytes "3.1.0"
-    content-type "~1.0.4"
+    bytes "3.1.2"
+    content-type "~1.0.5"
     debug "2.6.9"
-    depd "~1.1.2"
-    http-errors "1.7.2"
+    depd "2.0.0"
+    destroy "1.2.0"
+    http-errors "2.0.0"
     iconv-lite "0.4.24"
-    on-finished "~2.3.0"
-    qs "6.7.0"
-    raw-body "2.4.0"
-    type-is "~1.6.17"
-
-bonjour@^3.5.0:
-  version "3.5.0"
-  resolved "https://registry.npmjs.org/bonjour/-/bonjour-3.5.0.tgz"
-  integrity sha1-jokKGD2O6aI5OzhExpGkK897yfU=
-  dependencies:
-    array-flatten "^2.1.0"
-    deep-equal "^1.0.1"
-    dns-equal "^1.0.0"
-    dns-txt "^2.0.2"
-    multicast-dns "^6.0.1"
-    multicast-dns-service-types "^1.1.0"
+    on-finished "2.4.1"
+    qs "6.13.0"
+    raw-body "2.5.2"
+    type-is "~1.6.18"
+    unpipe "1.0.0"
+
+bonjour-service@^1.2.1:
+  version "1.3.0"
+  resolved "https://registry.npmjs.org/bonjour-service/-/bonjour-service-1.3.0.tgz"
+  integrity sha512-3YuAUiSkWykd+2Azjgyxei8OWf8thdn8AITIog2M4UICzoqfjlqr64WIjEXZllf/W6vK1goqleSR6brGomxQqA==
+  dependencies:
+    fast-deep-equal "^3.1.3"
+    multicast-dns "^7.2.5"
 
 boolbase@^1.0.0:
   version "1.0.0"
   resolved "https://registry.npmjs.org/boolbase/-/boolbase-1.0.0.tgz"
-  integrity sha1-aN/1++YMUes3cl6p4+0xDcwed24=
+  integrity sha1-aN/1++YMUes3cl6p4+0xDcwed24= sha512-JZOSA7Mo9sNGB8+UjSgzdLtokWAky1zbztM3WRLCbZ70/3cTANmQmOdR7y2g+J0e2WXywy1yS468tY+IruqEww==
 
-brace-expansion@^1.1.7:
-  version "1.1.11"
-  resolved "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.11.tgz"
-  integrity sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA==
-  dependencies:
-    balanced-match "^1.0.0"
-    concat-map "0.0.1"
-
-braces@^3.0.1, braces@~3.0.2:
-  version "3.0.2"
-  resolved "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz"
-  integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==
-  dependencies:
-    fill-range "^7.0.1"
-
-browserslist@^4.0.0, browserslist@^4.14.5, browserslist@^4.16.0, browserslist@^4.16.6, browserslist@^4.17.5:
-  version "4.18.1"
-  resolved "https://registry.npmjs.org/browserslist/-/browserslist-4.18.1.tgz"
-  integrity sha512-8ScCzdpPwR2wQh8IT82CA2VgDwjHyqMovPBZSNH54+tm4Jk2pCuv90gmAdH6J84OCRWi0b4gMe6O6XPXuJnjgQ==
-  dependencies:
-    caniuse-lite "^1.0.30001280"
-    electron-to-chromium "^1.3.896"
-    escalade "^3.1.1"
-    node-releases "^2.0.1"
-    picocolors "^1.0.0"
-
-browserslist@^4.19.1:
-  version "4.19.1"
-  resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.19.1.tgz#4ac0435b35ab655896c31d53018b6dd5e9e4c9a3"
-  integrity sha512-u2tbbG5PdKRTUoctO3NBD8FQ5HdPh1ZXPHzp1rwaa5jTc+RV9/+RlWiAIKmjRPQF+xbGM9Kklj5bZQFa2s/38A==
+braces@^3.0.3, braces@~3.0.2:
+  version "3.0.3"
+  resolved "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz"
+  integrity sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==
   dependencies:
-    caniuse-lite "^1.0.30001286"
-    electron-to-chromium "^1.4.17"
-    escalade "^3.1.1"
-    node-releases "^2.0.1"
-    picocolors "^1.0.0"
+    fill-range "^7.1.1"
 
-browserslist@^4.20.2:
-  version "4.20.2"
-  resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.20.2.tgz#567b41508757ecd904dab4d1c646c612cd3d4f88"
-  integrity sha512-CQOBCqp/9pDvDbx3xfMi+86pr4KXIf2FDkTTdeuYw8OxS9t898LA1Khq57gtufFILXpfgsSx5woNgsBgvGjpsA==
+browserslist@^4.0.0, browserslist@^4.16.0, browserslist@^4.16.6, browserslist@^4.17.5, browserslist@^4.20.2, browserslist@^4.26.3, "browserslist@>= 4.21.0":
+  version "4.26.3"
+  resolved "https://registry.npmjs.org/browserslist/-/browserslist-4.26.3.tgz"
+  integrity sha512-lAUU+02RFBuCKQPj/P6NgjlbCnLBMp4UtgTx7vNHd3XSIJF87s9a5rA3aH2yw3GS9DqZAUbOtZdCCiZeVRqt0w==
   dependencies:
-    caniuse-lite "^1.0.30001317"
-    electron-to-chromium "^1.4.84"
-    escalade "^3.1.1"
-    node-releases "^2.0.2"
-    picocolors "^1.0.0"
+    baseline-browser-mapping "^2.8.9"
+    caniuse-lite "^1.0.30001746"
+    electron-to-chromium "^1.5.227"
+    node-releases "^2.0.21"
+    update-browserslist-db "^1.1.3"
 
 buffer-from@^1.0.0:
   version "1.1.2"
   resolved "https://registry.npmjs.org/buffer-from/-/buffer-from-1.1.2.tgz"
   integrity sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==
 
-buffer-indexof@^1.0.0:
-  version "1.1.1"
-  resolved "https://registry.npmjs.org/buffer-indexof/-/buffer-indexof-1.1.1.tgz"
-  integrity sha512-4/rOEg86jivtPTeOUUT61jJO1Ya1TrR/OkqCSZDyq84WJh3LuuiphBYJN+fm5xufIk4XAFcEwte/8WzC8If/1g==
+bundle-name@^4.1.0:
+  version "4.1.0"
+  resolved "https://registry.npmjs.org/bundle-name/-/bundle-name-4.1.0.tgz"
+  integrity sha512-tjwM5exMg6BGRI+kNmTntNsvdZS1X8BFYS6tnJ2hdH0kVxM6/eVZ2xy+FqStSWvYmtfFMDLIxurorHwDKfDz5Q==
+  dependencies:
+    run-applescript "^7.0.0"
 
-bytes@3.0.0:
-  version "3.0.0"
-  resolved "https://registry.npmjs.org/bytes/-/bytes-3.0.0.tgz"
-  integrity sha1-0ygVQE1olpn4Wk6k+odV3ROpYEg=
+bytes@3.1.2:
+  version "3.1.2"
+  resolved "https://registry.npmjs.org/bytes/-/bytes-3.1.2.tgz"
+  integrity sha512-/Nf7TyzTx6S3yRJObOAV7956r8cr2+Oj8AC5dt8wSP3BQAoeX58NoHyCU8P8zGkNXStjTSi6fzO6F0pBdcYbEg==
 
-bytes@3.1.0:
-  version "3.1.0"
-  resolved "https://registry.npmjs.org/bytes/-/bytes-3.1.0.tgz"
-  integrity sha512-zauLjrfCG+xvoyaqLoV8bLVXXNGC4JqlxFCutSDWA6fJrTo2ZuvLYTqZ7aHBLZSMOopbzwv8f+wZcVzfVTI2Dg==
+call-bind-apply-helpers@^1.0.1, call-bind-apply-helpers@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.npmjs.org/call-bind-apply-helpers/-/call-bind-apply-helpers-1.0.2.tgz"
+  integrity sha512-Sp1ablJ0ivDkSzjcaJdxEunN5/XvksFJ2sMBFfq6x0ryhQV/2b/KwFe21cMpmHtPOSij8K99/wSfoEuTObmuMQ==
+  dependencies:
+    es-errors "^1.3.0"
+    function-bind "^1.1.2"
 
-call-bind@^1.0.0, call-bind@^1.0.2:
+call-bind@^1.0.0:
   version "1.0.2"
   resolved "https://registry.npmjs.org/call-bind/-/call-bind-1.0.2.tgz"
   integrity sha512-7O+FbCihrB5WGbFYesctwmTKae6rOiIzmz1icreWJ+0aA7LJfuqhEso2T9ncpcFtzMQtzXf2QGGueWJGTYsqrA==
@@ -1858,6 +1715,14 @@ call-bind@^1.0.0, call-bind@^1.0.2:
     function-bind "^1.1.1"
     get-intrinsic "^1.0.2"
 
+call-bound@^1.0.2:
+  version "1.0.4"
+  resolved "https://registry.npmjs.org/call-bound/-/call-bound-1.0.4.tgz"
+  integrity sha512-+ys997U96po4Kx/ABpBCqhA9EuxJaQWDQg7295H4hBphv3IZg0boBKuwYpt4YXp6MZ5AmZQnU/tyMTlRpaSejg==
+  dependencies:
+    call-bind-apply-helpers "^1.0.2"
+    get-intrinsic "^1.3.0"
+
 callsites@^3.0.0:
   version "3.1.0"
   resolved "https://registry.npmjs.org/callsites/-/callsites-3.1.0.tgz"
@@ -1873,34 +1738,15 @@ caniuse-api@^3.0.0:
     lodash.memoize "^4.1.2"
     lodash.uniq "^4.5.0"
 
-caniuse-lite@^1.0.0, caniuse-lite@^1.0.30001280:
-  version "1.0.30001285"
-  resolved "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001285.tgz"
-  integrity sha512-KAOkuUtcQ901MtmvxfKD+ODHH9YVDYnBt+TGYSz2KIfnq22CiArbUxXPN9067gNbgMlnNYRSwho8OPXZPALB9Q==
-
-caniuse-lite@^1.0.30001286:
-  version "1.0.30001301"
-  resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001301.tgz#ebc9086026534cab0dab99425d9c3b4425e5f450"
-  integrity sha512-csfD/GpHMqgEL3V3uIgosvh+SVIQvCh43SNu9HRbP1lnxkKm1kjDG4f32PP571JplkLjfS+mg2p1gxR7MYrrIA==
-
-caniuse-lite@^1.0.30001317:
-  version "1.0.30001323"
-  resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001323.tgz#a451ff80dec7033016843f532efda18f02eec011"
-  integrity sha512-e4BF2RlCVELKx8+RmklSEIVub1TWrmdhvA5kEUueummz1XyySW0DVk+3x9HyhU9MuWTa2BhqLgEuEmUwASAdCA==
+caniuse-lite@^1.0.0, caniuse-lite@^1.0.30001317, caniuse-lite@^1.0.30001746:
+  version "1.0.30001751"
+  resolved "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001751.tgz"
+  integrity sha512-A0QJhug0Ly64Ii3eIqHu5X51ebln3k4yTUkY1j8drqpWHVreg/VLijN48cZ1bYPiqOQuqpkIKnzr/Ul8V+p6Cw==
 
-chalk@^2.0.0:
-  version "2.4.2"
-  resolved "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz"
-  integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
-  dependencies:
-    ansi-styles "^3.2.1"
-    escape-string-regexp "^1.0.5"
-    supports-color "^5.3.0"
-
-chokidar@^3.5.3:
-  version "3.5.3"
-  resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.3.tgz#1cf37c8707b932bd1af1ae22c0432e2acd1903bd"
-  integrity sha512-Dr3sfKRP6oTcjf2JmUmFJfeVMvXBdegxB0iVQ5eb2V10uFJUCAS8OByZdVAyVb8xXNz3GjjTgj9kLWsZTqE6kw==
+chokidar@^3.6.0:
+  version "3.6.0"
+  resolved "https://registry.npmjs.org/chokidar/-/chokidar-3.6.0.tgz"
+  integrity sha512-7VT13fmjotKpGipCW9JEQAusEPE+Ei8nl6/g4FBAmIm0GOOLMua9NDDo/DWp0ZAxCr3cPq5ZpBqmPAQgDda2Pw==
   dependencies:
     anymatch "~3.1.2"
     braces "~3.0.2"
@@ -1922,11 +1768,6 @@ classnames@^2.3.1:
   resolved "https://registry.npmjs.org/classnames/-/classnames-2.3.1.tgz"
   integrity sha512-OlQdbZ7gLfGarSqxesMesDa5uz7KFbID8Kpq/SxIoNGDqY8lSYs0D+hhtBXhcdB3rcbXArFr7vlHheLk1voeNA==
 
-clean-stack@^2.0.0:
-  version "2.2.0"
-  resolved "https://registry.npmjs.org/clean-stack/-/clean-stack-2.2.0.tgz"
-  integrity sha512-4diC9HaTE+KRAMWhDhrGOECgWZxoevMc5TlkObMqNSsVU62PYzXZ/SMTjzyGAFF1YusgxGcSWTEXBhp0CPwQ1A==
-
 clone-deep@^4.0.1:
   version "4.0.1"
   resolved "https://registry.npmjs.org/clone-deep/-/clone-deep-4.0.1.tgz"
@@ -1936,18 +1777,6 @@ clone-deep@^4.0.1:
     kind-of "^6.0.2"
     shallow-clone "^3.0.0"
 
-color-convert@^1.9.0:
-  version "1.9.3"
-  resolved "https://registry.npmjs.org/color-convert/-/color-convert-1.9.3.tgz"
-  integrity sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==
-  dependencies:
-    color-name "1.1.3"
-
-color-name@1.1.3:
-  version "1.1.3"
-  resolved "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz"
-  integrity sha1-p9BVi9icQveV3UIyj3QIMcpTvCU=
-
 colord@^2.9.1:
   version "2.9.1"
   resolved "https://registry.npmjs.org/colord/-/colord-2.9.1.tgz"
@@ -1971,9 +1800,9 @@ commander@^7.0.0, commander@^7.2.0:
 commondir@^1.0.1:
   version "1.0.1"
   resolved "https://registry.npmjs.org/commondir/-/commondir-1.0.1.tgz"
-  integrity sha1-3dgA2gxmEnOTzKWVDqloo6rxJTs=
+  integrity sha1-3dgA2gxmEnOTzKWVDqloo6rxJTs= sha512-W9pAhw0ja1Edb5GVdIF1mjZw/ASI0AlShXM83UUGe2DVr5TdAPEA1OA8m/g8zWp9x6On7gqufY+FatDbC3MDQg==
 
-compressible@~2.0.16:
+compressible@~2.0.18:
   version "2.0.18"
   resolved "https://registry.npmjs.org/compressible/-/compressible-2.0.18.tgz"
   integrity sha512-AF3r7P5dWxL8MxyITRMlORQNaOA2IkAFaTr4k7BUumjPtRpGDTZpl0Pb1XCO6JeDCBdp126Cgs9sMxqSjgYyRg==
@@ -1981,16 +1810,16 @@ compressible@~2.0.16:
     mime-db ">= 1.43.0 < 2"
 
 compression@^1.7.4:
-  version "1.7.4"
-  resolved "https://registry.npmjs.org/compression/-/compression-1.7.4.tgz"
-  integrity sha512-jaSIDzP9pZVS4ZfQ+TzvtiWhdpFhE2RDHz8QJkpX9SIpLq88VueF5jJw6t+6CUQcAoA6t+x89MLrWAqpfDE8iQ==
+  version "1.8.1"
+  resolved "https://registry.npmjs.org/compression/-/compression-1.8.1.tgz"
+  integrity sha512-9mAqGPHLakhCLeNyxPkK4xVo746zQ/czLH1Ky+vkitMnWfWZps8r0qXuwhwizagCRttsL4lfG4pIOvaWLpAP0w==
   dependencies:
-    accepts "~1.3.5"
-    bytes "3.0.0"
-    compressible "~2.0.16"
+    bytes "3.1.2"
+    compressible "~2.0.18"
     debug "2.6.9"
-    on-headers "~1.0.2"
-    safe-buffer "5.1.2"
+    negotiator "~0.6.4"
+    on-headers "~1.1.0"
+    safe-buffer "5.2.1"
     vary "~1.1.2"
 
 compute-scroll-into-view@^1.0.17:
@@ -1998,27 +1827,22 @@ compute-scroll-into-view@^1.0.17:
   resolved "https://registry.npmjs.org/compute-scroll-into-view/-/compute-scroll-into-view-1.0.17.tgz"
   integrity sha512-j4dx+Fb0URmzbwwMUrhqWM2BEWHdFGx+qZ9qqASHRPqvTYdqvWnHg0H1hIbcyLnvgnoNAVMlwkepyqM3DaIFUg==
 
-concat-map@0.0.1:
-  version "0.0.1"
-  resolved "https://registry.npmjs.org/concat-map/-/concat-map-0.0.1.tgz"
-  integrity sha1-2Klr13/Wjfd5OnMDajug1UBdR3s=
-
-connect-history-api-fallback@^1.6.0:
-  version "1.6.0"
-  resolved "https://registry.npmjs.org/connect-history-api-fallback/-/connect-history-api-fallback-1.6.0.tgz"
-  integrity sha512-e54B99q/OUoH64zYYRf3HBP5z24G38h5D3qXu23JGRoigpX5Ss4r9ZnDk3g0Z8uQC2x2lPaJ+UlWBc1ZWBWdLg==
+connect-history-api-fallback@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.npmjs.org/connect-history-api-fallback/-/connect-history-api-fallback-2.0.0.tgz"
+  integrity sha512-U73+6lQFmfiNPrYbXqr6kZ1i1wiRqXnp2nhMsINseWXO8lDau0LGEffJ8kQi4EjLZympVgRdvqjAgiZ1tgzDDA==
 
-content-disposition@0.5.3:
-  version "0.5.3"
-  resolved "https://registry.npmjs.org/content-disposition/-/content-disposition-0.5.3.tgz"
-  integrity sha512-ExO0774ikEObIAEV9kDo50o+79VCUdEB6n6lzKgGwupcVeRlhrj3qGAfwq8G6uBJjkqLrhT0qEYFcWng8z1z0g==
+content-disposition@0.5.4:
+  version "0.5.4"
+  resolved "https://registry.npmjs.org/content-disposition/-/content-disposition-0.5.4.tgz"
+  integrity sha512-FveZTNuGw04cxlAiWbzi6zTAL/lhehaWbTtgluJh4/E95DqMwTmha3KZN1aAWA8cFIhHzMZUvLevkw5Rqk+tSQ==
   dependencies:
-    safe-buffer "5.1.2"
+    safe-buffer "5.2.1"
 
-content-type@~1.0.4:
-  version "1.0.4"
-  resolved "https://registry.npmjs.org/content-type/-/content-type-1.0.4.tgz"
-  integrity sha512-hIP3EEPs8tB9AT1L+NUqtwOAps4mk2Zob89MWXMHjHWg9milF/j4osnnQLXBCBFBk/tvIG/tUc9mOUJiPBhPXA==
+content-type@~1.0.4, content-type@~1.0.5:
+  version "1.0.5"
+  resolved "https://registry.npmjs.org/content-type/-/content-type-1.0.5.tgz"
+  integrity sha512-nTjqfcBFEipKdXCv4YDQWCfmcLZKm81ldF0pAopTvyrFGVbcR6P/VAAd5G7N+0tTr8QqiU0tFadD6FK4NtJwOA==
 
 convert-source-map@^1.7.0:
   version "1.8.0"
@@ -2030,20 +1854,19 @@ convert-source-map@^1.7.0:
 cookie-signature@1.0.6:
   version "1.0.6"
   resolved "https://registry.npmjs.org/cookie-signature/-/cookie-signature-1.0.6.tgz"
-  integrity sha1-4wOogrNCzD7oylE6eZmXNNqzriw=
+  integrity sha1-4wOogrNCzD7oylE6eZmXNNqzriw= sha512-QADzlaHc8icV8I7vbaJXJwod9HWYp8uCqf1xa4OfNu1T7JVxQIrUgOWtHdNDtPiywmFbiS12VjotIXLrKM3orQ==
 
-cookie@0.4.0:
-  version "0.4.0"
-  resolved "https://registry.npmjs.org/cookie/-/cookie-0.4.0.tgz"
-  integrity sha512-+Hp8fLp57wnUSt0tY0tHEXh4voZRDnoIrZPqlo3DPiI4y9lwg/jqx+1Om94/W6ZaPDOUbnjOt/99w66zk+l1Xg==
+cookie@0.7.1:
+  version "0.7.1"
+  resolved "https://registry.npmjs.org/cookie/-/cookie-0.7.1.tgz"
+  integrity sha512-6DnInpx7SJ2AK3+CTUE/ZM0vWTUboZCegxhC2xiIydHR9jNuTAASBrfEpHhiGOZw/nX51bHt6YQl8jsGo4y/0w==
 
 core-js-compat@^3.20.2, core-js-compat@^3.21.0:
-  version "3.21.1"
-  resolved "https://registry.yarnpkg.com/core-js-compat/-/core-js-compat-3.21.1.tgz#cac369f67c8d134ff8f9bd1623e3bc2c42068c82"
-  integrity sha512-gbgX5AUvMb8gwxC7FLVWYT7Kkgu/y7+h/h1X43yJkNqhlK2fuYyQimqvKGNZFAY6CKii/GFKJ2cp/1/42TN36g==
+  version "3.46.0"
+  resolved "https://registry.npmjs.org/core-js-compat/-/core-js-compat-3.46.0.tgz"
+  integrity sha512-p9hObIIEENxSV8xIu+V68JjSeARg6UVMG5mR+JEUguG3sI6MsiS1njz2jHmyJDvA+8jX/sytkBHup6kxhM9law==
   dependencies:
-    browserslist "^4.19.1"
-    semver "7.0.0"
+    browserslist "^4.26.3"
 
 core-util-is@~1.0.0:
   version "1.0.3"
@@ -2062,9 +1885,9 @@ cosmiconfig@^7.0.0:
     yaml "^1.10.0"
 
 cross-spawn@^7.0.3:
-  version "7.0.3"
-  resolved "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.3.tgz"
-  integrity sha512-iRDPJKUPVEND7dHPO8rkbOnPpyDygcDFtWjpeWNCgy8WP2rXcxXL8TskReQl6OrB2G7+UJrags1q15Fudc7G6w==
+  version "7.0.6"
+  resolved "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.6.tgz"
+  integrity sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA==
   dependencies:
     path-key "^3.1.0"
     shebang-command "^2.0.0"
@@ -2072,13 +1895,13 @@ cross-spawn@^7.0.3:
 
 css-blank-pseudo@^3.0.3:
   version "3.0.3"
-  resolved "https://registry.yarnpkg.com/css-blank-pseudo/-/css-blank-pseudo-3.0.3.tgz#36523b01c12a25d812df343a32c322d2a2324561"
+  resolved "https://registry.npmjs.org/css-blank-pseudo/-/css-blank-pseudo-3.0.3.tgz"
   integrity sha512-VS90XWtsHGqoM0t4KpH053c4ehxZ2E6HtGI7x68YFV0pTo/QmkV/YFA+NnlvK8guxZVNWGQhVNJGC39Q8XF4OQ==
   dependencies:
     postcss-selector-parser "^6.0.9"
 
 css-declaration-sorter@^6.0.3:
-  version "6.1.2"
+  version "6.1.3"
   resolved "https://registry.npmjs.org/css-declaration-sorter/-/css-declaration-sorter-6.1.3.tgz"
   integrity sha512-SvjQjNRZgh4ULK1LDJ2AduPKUKxIqmtU7ZAyi47BTV+M90Qvxr9AB6lKlLbDUfXqI9IQeYA8LbAsCZPpJEV3aA==
   dependencies:
@@ -2086,14 +1909,14 @@ css-declaration-sorter@^6.0.3:
 
 css-has-pseudo@^3.0.4:
   version "3.0.4"
-  resolved "https://registry.yarnpkg.com/css-has-pseudo/-/css-has-pseudo-3.0.4.tgz#57f6be91ca242d5c9020ee3e51bbb5b89fc7af73"
+  resolved "https://registry.npmjs.org/css-has-pseudo/-/css-has-pseudo-3.0.4.tgz"
   integrity sha512-Vse0xpR1K9MNlp2j5w1pgWIJtm1a8qS0JwS9goFYcImjlHEmywP9VUF05aGBXzGpDJF86QXk4L0ypBmwPhGArw==
   dependencies:
     postcss-selector-parser "^6.0.9"
 
 css-loader@^6.7.1:
   version "6.7.1"
-  resolved "https://registry.yarnpkg.com/css-loader/-/css-loader-6.7.1.tgz#e98106f154f6e1baf3fc3bc455cb9981c1d5fd2e"
+  resolved "https://registry.npmjs.org/css-loader/-/css-loader-6.7.1.tgz"
   integrity sha512-yB5CNFa14MbPJcomwNh3wLThtkZgcNyI2bNMRt8iE5Z8Vwl7f8vQXFAzn2HDOJvtDq2NTZBUGMSUNNyrv3/+cw==
   dependencies:
     icss-utils "^5.1.0"
@@ -2107,7 +1930,7 @@ css-loader@^6.7.1:
 
 css-minimizer-webpack-plugin@^3.4.1:
   version "3.4.1"
-  resolved "https://registry.yarnpkg.com/css-minimizer-webpack-plugin/-/css-minimizer-webpack-plugin-3.4.1.tgz#ab78f781ced9181992fe7b6e4f3422e76429878f"
+  resolved "https://registry.npmjs.org/css-minimizer-webpack-plugin/-/css-minimizer-webpack-plugin-3.4.1.tgz"
   integrity sha512-1u6D71zeIfgngN2XNRJefc/hY7Ybsxd74Jm4qngIXyUEk7fss3VUzuHxLAq/R8NAba4QU9OUSaMZlbpRc7bM4Q==
   dependencies:
     cssnano "^5.0.6"
@@ -2119,7 +1942,7 @@ css-minimizer-webpack-plugin@^3.4.1:
 
 css-prefers-color-scheme@^6.0.3:
   version "6.0.3"
-  resolved "https://registry.yarnpkg.com/css-prefers-color-scheme/-/css-prefers-color-scheme-6.0.3.tgz#ca8a22e5992c10a5b9d315155e7caee625903349"
+  resolved "https://registry.npmjs.org/css-prefers-color-scheme/-/css-prefers-color-scheme-6.0.3.tgz"
   integrity sha512-4BqMbZksRkJQx2zAjrokiGMd07RqOa2IxIrrN10lyBe9xhn9DEvjUK79J6jkeiv9D9hQFXKb6g1jwU62jziJZA==
 
 css-select@^4.1.3:
@@ -2148,7 +1971,7 @@ css-what@^5.0.0:
 
 cssdb@^6.5.0:
   version "6.5.0"
-  resolved "https://registry.yarnpkg.com/cssdb/-/cssdb-6.5.0.tgz#61264b71f29c834f09b59cb3e5b43c8226590122"
+  resolved "https://registry.npmjs.org/cssdb/-/cssdb-6.5.0.tgz"
   integrity sha512-Rh7AAopF2ckPXe/VBcoUS9JrCZNSyc60+KpgE6X25vpVxA32TmiqvExjkfhwP4wGSb6Xe8Z/JIyGqwgx/zZYFA==
 
 cssesc@^3.0.0:
@@ -2213,50 +2036,37 @@ csso@^4.2.0:
   dependencies:
     css-tree "^1.1.2"
 
-debug@2.6.9:
-  version "2.6.9"
-  resolved "https://registry.npmjs.org/debug/-/debug-2.6.9.tgz"
-  integrity sha512-bC7ElrdJaJnPbAP+1EotYvqZsb3ecl5wi6Bfi6BJTUcNowp6cvspg0jXznRTKDjm/E7AdgFBVeAPVMNcKGsHMA==
-  dependencies:
-    ms "2.0.0"
-
-debug@^3.1.1:
-  version "3.2.7"
-  resolved "https://registry.npmjs.org/debug/-/debug-3.2.7.tgz"
-  integrity sha512-CFjzYYAi4ThfiQvizrFQevTTXHtnCqWfe7x1AhgEscTz6ZbLbfoLRLPugTQyBth6f8ZERVUSyWHFD/7Wu4t1XQ==
-  dependencies:
-    ms "^2.1.1"
-
-debug@^4.1.0, debug@^4.1.1:
+debug@^4.1.0, debug@^4.1.1, debug@^4.3.1:
   version "4.3.3"
   resolved "https://registry.npmjs.org/debug/-/debug-4.3.3.tgz"
   integrity sha512-/zxw5+vh1Tfv+4Qn7a5nsbcJKPaSvCDhojn6FEl9vupwK2VCSDtEiEtqr8DFtzYFOdz63LBkxec7DYuc2jon6Q==
   dependencies:
     ms "2.1.2"
 
-deep-equal@^1.0.1:
-  version "1.1.1"
-  resolved "https://registry.npmjs.org/deep-equal/-/deep-equal-1.1.1.tgz"
-  integrity sha512-yd9c5AdiqVcR+JjcwUQb9DkhJc8ngNr0MahEBGvDiJw8puWab2yZlh+nkasOnZP+EGTAP6rRp2JzJhJZzvNF8g==
+debug@2.6.9:
+  version "2.6.9"
+  resolved "https://registry.npmjs.org/debug/-/debug-2.6.9.tgz"
+  integrity sha512-bC7ElrdJaJnPbAP+1EotYvqZsb3ecl5wi6Bfi6BJTUcNowp6cvspg0jXznRTKDjm/E7AdgFBVeAPVMNcKGsHMA==
   dependencies:
-    is-arguments "^1.0.4"
-    is-date-object "^1.0.1"
-    is-regex "^1.0.4"
-    object-is "^1.0.1"
-    object-keys "^1.1.1"
-    regexp.prototype.flags "^1.2.0"
+    ms "2.0.0"
 
-default-gateway@^6.0.3:
-  version "6.0.3"
-  resolved "https://registry.npmjs.org/default-gateway/-/default-gateway-6.0.3.tgz"
-  integrity sha512-fwSOJsbbNzZ/CUFpqFBqYfYNLj1NbMPm8MMCIzHjC83iSJRBEGmDUxU+WP661BaBQImeC2yHwXtz+P/O9o+XEg==
+default-browser-id@^5.0.0:
+  version "5.0.0"
+  resolved "https://registry.npmjs.org/default-browser-id/-/default-browser-id-5.0.0.tgz"
+  integrity sha512-A6p/pu/6fyBcA1TRz/GqWYPViplrftcW2gZC9q79ngNCKAeR/X3gcEdXQHl4KNXV+3wgIJ1CPkJQ3IHM6lcsyA==
+
+default-browser@^5.2.1:
+  version "5.2.1"
+  resolved "https://registry.npmjs.org/default-browser/-/default-browser-5.2.1.tgz"
+  integrity sha512-WY/3TUME0x3KPYdRRxEJJvXRHV4PyPoUsxtZa78lwItwRQRHhd2U9xOscaT/YTf8uCXIAjeJOFBVEh/7FtD8Xg==
   dependencies:
-    execa "^5.0.0"
+    bundle-name "^4.1.0"
+    default-browser-id "^5.0.0"
 
-define-lazy-prop@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.npmjs.org/define-lazy-prop/-/define-lazy-prop-2.0.0.tgz"
-  integrity sha512-Ds09qNh8yw3khSjiJjiUInaGX9xlqZDY7JVryGxdxV7NPeuqQfplOpQ66yJFZut3jLa5zOwkXw1g9EI2uKh4Og==
+define-lazy-prop@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.npmjs.org/define-lazy-prop/-/define-lazy-prop-3.0.0.tgz"
+  integrity sha512-N+MeXYoqr3pOgn8xfyRPREN7gHakLYjhsHhWGT3fWAiL4IkAt0iDw14QiiEm2bE30c5XX5q0FtAA3CK5f9/BUg==
 
 define-properties@^1.1.3:
   version "1.1.3"
@@ -2265,61 +2075,32 @@ define-properties@^1.1.3:
   dependencies:
     object-keys "^1.0.12"
 
-del@^6.0.0:
-  version "6.0.0"
-  resolved "https://registry.npmjs.org/del/-/del-6.0.0.tgz"
-  integrity sha512-1shh9DQ23L16oXSZKB2JxpL7iMy2E0S9d517ptA1P8iw0alkPtQcrKH7ru31rYtKwF499HkTu+DRzq3TCKDFRQ==
-  dependencies:
-    globby "^11.0.1"
-    graceful-fs "^4.2.4"
-    is-glob "^4.0.1"
-    is-path-cwd "^2.2.0"
-    is-path-inside "^3.0.2"
-    p-map "^4.0.0"
-    rimraf "^3.0.2"
-    slash "^3.0.0"
-
 depd@~1.1.2:
   version "1.1.2"
   resolved "https://registry.npmjs.org/depd/-/depd-1.1.2.tgz"
-  integrity sha1-m81S4UwJd2PnSbJ0xDRu0uVgtak=
+  integrity sha1-m81S4UwJd2PnSbJ0xDRu0uVgtak= sha512-7emPTl6Dpo6JRXOXjLRxck+FlLRX5847cLKEn00PLAgc3g2hTZZgr+e4c2v6QpSmLeFP3n5yUo7ft6avBK/5jQ==
 
-destroy@~1.0.4:
-  version "1.0.4"
-  resolved "https://registry.npmjs.org/destroy/-/destroy-1.0.4.tgz"
-  integrity sha1-l4hXRCxEdJ5CBmE+N5RiBYJqvYA=
+depd@2.0.0:
+  version "2.0.0"
+  resolved "https://registry.npmjs.org/depd/-/depd-2.0.0.tgz"
+  integrity sha512-g7nH6P6dyDioJogAAGprGpCtVImJhpPk/roCzdb3fIh61/s/nPsfR6onyMwkCAR/OlC3yBC0lESvUoQEAssIrw==
+
+destroy@1.2.0:
+  version "1.2.0"
+  resolved "https://registry.npmjs.org/destroy/-/destroy-1.2.0.tgz"
+  integrity sha512-2sJGJTaXIIaR1w4iJSNoN0hnMY7Gpc/n8D4qSCJw8QqFWXf7cuAgnEHxBpweaVcPevC2l3KpjYCx3NypQQgaJg==
 
 detect-node@^2.0.4:
   version "2.1.0"
   resolved "https://registry.npmjs.org/detect-node/-/detect-node-2.1.0.tgz"
   integrity sha512-T0NIuQpnTvFDATNuHN5roPwSBG83rFsuO+MXXH9/3N1eFbn4wcPjttvjMLEPWJ0RGUYgQE7cGgS3tNxbqCGM7g==
 
-dir-glob@^3.0.1:
-  version "3.0.1"
-  resolved "https://registry.npmjs.org/dir-glob/-/dir-glob-3.0.1.tgz"
-  integrity sha512-WkrWp9GR4KXfKGYzOLmTuGVi1UWFfws377n9cc55/tb6DuqyF6pcQ5AbiHEshaDpY9v6oaSr2XCDidGmMwdzIA==
-  dependencies:
-    path-type "^4.0.0"
-
-dns-equal@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.npmjs.org/dns-equal/-/dns-equal-1.0.0.tgz"
-  integrity sha1-s55/HabrCnW6nBcySzR1PEfgZU0=
-
-dns-packet@^1.3.1:
-  version "1.3.4"
-  resolved "https://registry.npmjs.org/dns-packet/-/dns-packet-1.3.4.tgz"
-  integrity sha512-BQ6F4vycLXBvdrJZ6S3gZewt6rcrks9KBgM9vrhW+knGRqc8uEdT7fuCwloc7nny5xNoMJ17HGH0R/6fpo8ECA==
-  dependencies:
-    ip "^1.1.0"
-    safe-buffer "^5.0.1"
-
-dns-txt@^2.0.2:
-  version "2.0.2"
-  resolved "https://registry.npmjs.org/dns-txt/-/dns-txt-2.0.2.tgz"
-  integrity sha1-uR2Ab10nGI5Ks+fRB9iBocxGQrY=
+dns-packet@^5.2.2:
+  version "5.6.1"
+  resolved "https://registry.npmjs.org/dns-packet/-/dns-packet-5.6.1.tgz"
+  integrity sha512-l4gcSouhcgIKRvyy99RNVOgxXiicE+2jZoNmaNmZ6JXiGajBOJAesk1OBlJuM5k2c+eudGdLxDqXuPCKIj6kpw==
   dependencies:
-    buffer-indexof "^1.0.0"
+    "@leichtgewicht/ip-codec" "^2.0.1"
 
 dom-serializer@^1.0.1:
   version "1.3.2"
@@ -2362,25 +2143,24 @@ downshift@^6.1.7:
     react-is "^17.0.2"
     tslib "^2.3.0"
 
+dunder-proto@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.npmjs.org/dunder-proto/-/dunder-proto-1.0.1.tgz"
+  integrity sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A==
+  dependencies:
+    call-bind-apply-helpers "^1.0.1"
+    es-errors "^1.3.0"
+    gopd "^1.2.0"
+
 ee-first@1.1.1:
   version "1.1.1"
   resolved "https://registry.npmjs.org/ee-first/-/ee-first-1.1.1.tgz"
-  integrity sha1-WQxhFWsK4vTwJVcyoViyZrxWsh0=
+  integrity sha1-WQxhFWsK4vTwJVcyoViyZrxWsh0= sha512-WMwm9LhRUo+WUaRN+vRuETqG89IgZphVSNkdFgeb6sS/E4OrDIN7t48CAewSHXc6C8lefD8KKfr5vY61brQlow==
 
-electron-to-chromium@^1.3.896:
-  version "1.4.13"
-  resolved "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.4.13.tgz"
-  integrity sha512-ih5tIhzEuf78pBY70FXLo+Pw73R5MPPPcXb4CGBMJaCQt/qo/IGIesKXmswpemVCKSE2Bulr5FslUv7gAWJoOw==
-
-electron-to-chromium@^1.4.17:
-  version "1.4.51"
-  resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.4.51.tgz#a432f5a5d983ace79278a33057300cf949627e63"
-  integrity sha512-JNEmcYl3mk1tGQmy0EvL5eik/CKSBuzAyGP0QFdG6LIgxQe3II0BL1m2zKc2MZMf3uGqHWE1TFddJML0RpjSHQ==
-
-electron-to-chromium@^1.4.84:
-  version "1.4.103"
-  resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.4.103.tgz#abfe376a4d70fa1e1b4b353b95df5d6dfd05da3a"
-  integrity sha512-c/uKWR1Z/W30Wy/sx3dkZoj4BijbXX85QKWu9jJfjho3LBAXNEGAEW3oWiGb+dotA6C6BzCTxL2/aLes7jlUeg==
+electron-to-chromium@^1.5.227:
+  version "1.5.237"
+  resolved "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.237.tgz"
+  integrity sha512-icUt1NvfhGLar5lSWH3tHNzablaA5js3HVHacQimfP8ViEBOQv+L7DKEuHdbTZ0SKCO1ogTJTIL1Gwk9S6Qvcg==
 
 emojis-list@^3.0.0:
   version "3.0.0"
@@ -2390,12 +2170,17 @@ emojis-list@^3.0.0:
 encodeurl@~1.0.2:
   version "1.0.2"
   resolved "https://registry.npmjs.org/encodeurl/-/encodeurl-1.0.2.tgz"
-  integrity sha1-rT/0yG7C0CkyL1oCw6mmBslbP1k=
+  integrity sha1-rT/0yG7C0CkyL1oCw6mmBslbP1k= sha512-TPJXq8JqFaVYm2CWmPvnP2Iyo4ZSM7/QKcSmuMLDObfpH5fi7RUGmd/rTDf+rut/saiDiQEeVTNgAmJEdAOx0w==
 
-enhanced-resolve@^5.9.2:
-  version "5.9.2"
-  resolved "https://registry.yarnpkg.com/enhanced-resolve/-/enhanced-resolve-5.9.2.tgz#0224dcd6a43389ebfb2d55efee517e5466772dd9"
-  integrity sha512-GIm3fQfwLJ8YZx2smuHpBKkXC1yOk+OBEmKckVyL0i/ea8mqDEykK3ld5dgH1QYPNyT/lIllxV2LULnxCHaHkA==
+encodeurl@~2.0.0:
+  version "2.0.0"
+  resolved "https://registry.npmjs.org/encodeurl/-/encodeurl-2.0.0.tgz"
+  integrity sha512-Q0n9HRi4m6JuGIV1eFlmvJB7ZEVxu93IrMyiMsGC0lrMJMWzRgx6WGquyfQgZVb31vhGgXnfmPNNXmxnOkRBrg==
+
+enhanced-resolve@^5.17.3:
+  version "5.18.3"
+  resolved "https://registry.npmjs.org/enhanced-resolve/-/enhanced-resolve-5.18.3.tgz"
+  integrity sha512-d4lC8xfavMeBjzGr2vECC3fsGXziXZQyJxD868h2M/mBI3PwAuODxAkLkq5HYuvrPYcUtiLzsTo8U3PgX3Ocww==
   dependencies:
     graceful-fs "^4.2.4"
     tapable "^2.2.0"
@@ -2417,25 +2202,37 @@ error-ex@^1.3.1:
   dependencies:
     is-arrayish "^0.2.1"
 
-es-module-lexer@^0.9.0:
-  version "0.9.3"
-  resolved "https://registry.npmjs.org/es-module-lexer/-/es-module-lexer-0.9.3.tgz"
-  integrity sha512-1HQ2M2sPtxwnvOvT1ZClHyQDiggdNjURWpY2we6aMKCQiUVxTmVs2UYPLIrD84sS+kMdUwfBSylbJPwNnBrnHQ==
+es-define-property@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.1.tgz"
+  integrity sha512-e3nRfgfUZ4rNGL232gUgX06QNyyez04KdjFrF+LTRoOXmrOgFKDg4BCdsjW8EnT69eqdYGmRpJwiPVYNrCaW3g==
+
+es-errors@^1.3.0:
+  version "1.3.0"
+  resolved "https://registry.npmjs.org/es-errors/-/es-errors-1.3.0.tgz"
+  integrity sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==
 
-escalade@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.npmjs.org/escalade/-/escalade-3.1.1.tgz"
-  integrity sha512-k0er2gUkLf8O0zKJiAhmkTnJlTvINGv7ygDNPbeIsX/TJjGJZHuh9B2UxbsaEkmlEo9MfhrSzmhIlhRlI2GXnw==
+es-module-lexer@^1.2.1:
+  version "1.7.0"
+  resolved "https://registry.npmjs.org/es-module-lexer/-/es-module-lexer-1.7.0.tgz"
+  integrity sha512-jEQoCwk8hyb2AZziIOLhDqpm5+2ww5uIE6lkO/6jcOCusfk6LhMHpXXfBLXTZ7Ydyt0j4VoUQv6uGNYbdW+kBA==
+
+es-object-atoms@^1.0.0, es-object-atoms@^1.1.1:
+  version "1.1.1"
+  resolved "https://registry.npmjs.org/es-object-atoms/-/es-object-atoms-1.1.1.tgz"
+  integrity sha512-FGgH2h8zKNim9ljj7dankFPcICIK9Cp5bm+c2gQSYePhpaG5+esrLODihIorn+Pe6FGJzWhXQotPv73jTaldXA==
+  dependencies:
+    es-errors "^1.3.0"
+
+escalade@^3.2.0:
+  version "3.2.0"
+  resolved "https://registry.npmjs.org/escalade/-/escalade-3.2.0.tgz"
+  integrity sha512-WUj2qlxaQtO4g6Pq5c29GTcWGDyd8itL8zTlipgECz3JesAiiOKotd8JU6otB3PACgG6xkJUyVhboMS+bje/jA==
 
 escape-html@~1.0.3:
   version "1.0.3"
   resolved "https://registry.npmjs.org/escape-html/-/escape-html-1.0.3.tgz"
-  integrity sha1-Aljq5NPQwJdN4cFpGI7wBR0dGYg=
-
-escape-string-regexp@^1.0.5:
-  version "1.0.5"
-  resolved "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz"
-  integrity sha1-G2HAViGQqN/2rjuyzwIAyhMLhtQ=
+  integrity sha1-Aljq5NPQwJdN4cFpGI7wBR0dGYg= sha512-NiSupZ4OeuGwr68lGIeym/ksIZMJodUGOSCZ/FSnTxcrekbvqrgdUxlJOMpijaKZVjAJrWrGs/6Jy8OMuyj9ow==
 
 eslint-scope@5.1.1:
   version "5.1.1"
@@ -2470,7 +2267,7 @@ esutils@^2.0.2:
 etag@~1.8.1:
   version "1.8.1"
   resolved "https://registry.npmjs.org/etag/-/etag-1.8.1.tgz"
-  integrity sha1-Qa4u62XvpiJorr/qg6x9eSmbCIc=
+  integrity sha1-Qa4u62XvpiJorr/qg6x9eSmbCIc= sha512-aIL5Fx7mawVa300al2BnEE4iNvo1qETxLrPI/o05L7z6go7fCw1J6EQmbK4FmJ2AS7kgVF/KEZWufBfdClMcPg==
 
 eventemitter3@^4.0.0:
   version "4.0.7"
@@ -2497,38 +2294,39 @@ execa@^5.0.0:
     signal-exit "^3.0.3"
     strip-final-newline "^2.0.0"
 
-express@^4.17.1:
-  version "4.17.1"
-  resolved "https://registry.npmjs.org/express/-/express-4.17.1.tgz"
-  integrity sha512-mHJ9O79RqluphRrcw2X/GTh3k9tVv8YcoyY4Kkh4WDMUYKRZUq0h1o0w2rrrxBqM7VoeUVqgb27xlEMXTnYt4g==
+express@^4.21.2:
+  version "4.21.2"
+  resolved "https://registry.npmjs.org/express/-/express-4.21.2.tgz"
+  integrity sha512-28HqgMZAmih1Czt9ny7qr6ek2qddF4FclbMzwhCREB6OFfH+rXAnuNCwo1/wFvrtbgsQDb4kSbX9de9lFbrXnA==
   dependencies:
-    accepts "~1.3.7"
+    accepts "~1.3.8"
     array-flatten "1.1.1"
-    body-parser "1.19.0"
-    content-disposition "0.5.3"
+    body-parser "1.20.3"
+    content-disposition "0.5.4"
     content-type "~1.0.4"
-    cookie "0.4.0"
+    cookie "0.7.1"
     cookie-signature "1.0.6"
     debug "2.6.9"
-    depd "~1.1.2"
-    encodeurl "~1.0.2"
+    depd "2.0.0"
+    encodeurl "~2.0.0"
     escape-html "~1.0.3"
     etag "~1.8.1"
-    finalhandler "~1.1.2"
+    finalhandler "1.3.1"
     fresh "0.5.2"
-    merge-descriptors "1.0.1"
+    http-errors "2.0.0"
+    merge-descriptors "1.0.3"
     methods "~1.1.2"
-    on-finished "~2.3.0"
+    on-finished "2.4.1"
     parseurl "~1.3.3"
-    path-to-regexp "0.1.7"
-    proxy-addr "~2.0.5"
-    qs "6.7.0"
+    path-to-regexp "0.1.12"
+    proxy-addr "~2.0.7"
+    qs "6.13.0"
     range-parser "~1.2.1"
-    safe-buffer "5.1.2"
-    send "0.17.1"
-    serve-static "1.14.1"
-    setprototypeof "1.1.1"
-    statuses "~1.5.0"
+    safe-buffer "5.2.1"
+    send "0.19.0"
+    serve-static "1.16.2"
+    setprototypeof "1.2.0"
+    statuses "2.0.1"
     type-is "~1.6.18"
     utils-merge "1.0.1"
     vary "~1.1.2"
@@ -2538,34 +2336,21 @@ fast-deep-equal@^3.1.1, fast-deep-equal@^3.1.3:
   resolved "https://registry.npmjs.org/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz"
   integrity sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q==
 
-fast-glob@^3.1.1:
-  version "3.2.7"
-  resolved "https://registry.npmjs.org/fast-glob/-/fast-glob-3.2.7.tgz"
-  integrity sha512-rYGMRwip6lUMvYD3BTScMwT1HtAs2d71SMv66Vrxs0IekGZEjhM0pcMfjQPnknBt2zeCwQMEupiN02ZP4DiT1Q==
-  dependencies:
-    "@nodelib/fs.stat" "^2.0.2"
-    "@nodelib/fs.walk" "^1.2.3"
-    glob-parent "^5.1.2"
-    merge2 "^1.3.0"
-    micromatch "^4.0.4"
-
 fast-json-stable-stringify@^2.0.0:
   version "2.1.0"
   resolved "https://registry.npmjs.org/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz"
   integrity sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw==
 
+fast-uri@^3.0.1:
+  version "3.1.0"
+  resolved "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.0.tgz"
+  integrity sha512-iPeeDKJSWf4IEOasVVrknXpaBV0IApz/gp7S2bb7Z4Lljbl2MGJRqInZiUrQwV16cpzw/D3S5j5Julj/gT52AA==
+
 fastest-levenshtein@^1.0.12:
   version "1.0.12"
   resolved "https://registry.npmjs.org/fastest-levenshtein/-/fastest-levenshtein-1.0.12.tgz"
   integrity sha512-On2N+BpYJ15xIC974QNVuYGMOlEVt4s0EOI3wwMqOmK1fdDY+FN/zltPV8vosq4ad4c/gJ1KHScUn/6AWIgiow==
 
-fastq@^1.6.0:
-  version "1.13.0"
-  resolved "https://registry.npmjs.org/fastq/-/fastq-1.13.0.tgz"
-  integrity sha512-YpkpUnK8od0o1hmeSc7UUs/eB/vIPWJYjKck2QKIzAf71Vm1AAQ3EbuZB3g2JIy+pg+ERD0vqI79KyZiB2e2Nw==
-  dependencies:
-    reusify "^1.0.4"
-
 faye-websocket@^0.11.3:
   version "0.11.4"
   resolved "https://registry.npmjs.org/faye-websocket/-/faye-websocket-0.11.4.tgz"
@@ -2573,24 +2358,24 @@ faye-websocket@^0.11.3:
   dependencies:
     websocket-driver ">=0.5.1"
 
-fill-range@^7.0.1:
-  version "7.0.1"
-  resolved "https://registry.npmjs.org/fill-range/-/fill-range-7.0.1.tgz"
-  integrity sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==
+fill-range@^7.1.1:
+  version "7.1.1"
+  resolved "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz"
+  integrity sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==
   dependencies:
     to-regex-range "^5.0.1"
 
-finalhandler@~1.1.2:
-  version "1.1.2"
-  resolved "https://registry.npmjs.org/finalhandler/-/finalhandler-1.1.2.tgz"
-  integrity sha512-aAWcW57uxVNrQZqFXjITpW3sIUQmHGG3qSb9mUah9MgMC4NeWhNOlNjXEYq3HjRAvL6arUviZGGJsBg6z0zsWA==
+finalhandler@1.3.1:
+  version "1.3.1"
+  resolved "https://registry.npmjs.org/finalhandler/-/finalhandler-1.3.1.tgz"
+  integrity sha512-6BN9trH7bp3qvnrRyzsBz+g3lZxTNZTbVO2EV1CS0WIcDbawYVdYvGflME/9QP0h0pYlCDBCTjYa9nZzMDpyxQ==
   dependencies:
     debug "2.6.9"
-    encodeurl "~1.0.2"
+    encodeurl "~2.0.0"
     escape-html "~1.0.3"
-    on-finished "~2.3.0"
+    on-finished "2.4.1"
     parseurl "~1.3.3"
-    statuses "~1.5.0"
+    statuses "2.0.1"
     unpipe "~1.0.0"
 
 find-cache-dir@^3.3.1:
@@ -2611,9 +2396,9 @@ find-up@^4.0.0:
     path-exists "^4.0.0"
 
 follow-redirects@^1.0.0:
-  version "1.14.8"
-  resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.14.8.tgz#016996fb9a11a100566398b1c6839337d7bfa8fc"
-  integrity sha512-1x0S9UVJHsQprFcEC/qnNzBLcIxsjAV905f/UkQxbclCsoTWlacCNOpQa/anodLl2uaEKFhfWOvM2Qg77+15zA==
+  version "1.15.11"
+  resolved "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.11.tgz"
+  integrity sha512-deG2P0JfjrTxl50XGCDyfI97ZGVCxIpfKYmfyrQ54n5FO/0gfIES8C/Psl6kWVDolizcaaxZJnTS0QSMxvnsBQ==
 
 forwarded@0.2.0:
   version "0.2.0"
@@ -2622,130 +2407,104 @@ forwarded@0.2.0:
 
 fraction.js@^4.2.0:
   version "4.2.0"
-  resolved "https://registry.yarnpkg.com/fraction.js/-/fraction.js-4.2.0.tgz#448e5109a313a3527f5a3ab2119ec4cf0e0e2950"
+  resolved "https://registry.npmjs.org/fraction.js/-/fraction.js-4.2.0.tgz"
   integrity sha512-MhLuK+2gUcnZe8ZHlaaINnQLl0xRIGRfcGk2yl8xoQAfHrSsL3rYu6FCmBdkdbhc9EPlwyGHewaRsvwRMJtAlA==
 
 fresh@0.5.2:
   version "0.5.2"
   resolved "https://registry.npmjs.org/fresh/-/fresh-0.5.2.tgz"
-  integrity sha1-PYyt2Q2XZWn6g1qx+OSyOhBWBac=
-
-fs-monkey@1.0.3:
-  version "1.0.3"
-  resolved "https://registry.npmjs.org/fs-monkey/-/fs-monkey-1.0.3.tgz"
-  integrity sha512-cybjIfiiE+pTWicSCLFHSrXZ6EilF30oh91FDP9S2B051prEa7QWfrVTQm10/dDpswBDXZugPa1Ogu8Yh+HV0Q==
-
-fs.realpath@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.npmjs.org/fs.realpath/-/fs.realpath-1.0.0.tgz"
-  integrity sha1-FQStJSMVjKpA20onh8sBQRmU6k8=
+  integrity sha1-PYyt2Q2XZWn6g1qx+OSyOhBWBac= sha512-zJ2mQYM18rEFOudeV4GShTGIQ7RbzA7ozbU9I/XBpm7kqgMywgmylMwXHxZJmkVoYkna9d2pVXVXPdYTP9ej8Q==
 
 fsevents@~2.3.2:
-  version "2.3.2"
-  resolved "https://registry.npmjs.org/fsevents/-/fsevents-2.3.2.tgz"
-  integrity sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==
+  version "2.3.3"
+  resolved "https://registry.npmjs.org/fsevents/-/fsevents-2.3.3.tgz"
+  integrity sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw==
 
-function-bind@^1.1.1:
-  version "1.1.1"
-  resolved "https://registry.npmjs.org/function-bind/-/function-bind-1.1.1.tgz"
-  integrity sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==
+function-bind@^1.1.1, function-bind@^1.1.2:
+  version "1.1.2"
+  resolved "https://registry.npmjs.org/function-bind/-/function-bind-1.1.2.tgz"
+  integrity sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==
 
 gensync@^1.0.0-beta.2:
   version "1.0.0-beta.2"
   resolved "https://registry.npmjs.org/gensync/-/gensync-1.0.0-beta.2.tgz"
   integrity sha512-3hN7NaskYvMDLQY55gnW3NQ+mesEAepTqlg+VEbj7zzqEMBVNhzcGYYeqFo/TlYz6eQiFcp1HcsCZO+nGgS8zg==
 
-get-intrinsic@^1.0.2:
-  version "1.1.1"
-  resolved "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.1.1.tgz"
-  integrity sha512-kWZrnVM42QCiEA2Ig1bG8zjoIMOgxWwYCEeNdwY6Tv/cOSeGpcoX4pXHfKUxNKVoArnrEr2e9srnAxxGIraS9Q==
+get-intrinsic@^1.0.2, get-intrinsic@^1.2.5, get-intrinsic@^1.3.0:
+  version "1.3.0"
+  resolved "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.3.0.tgz"
+  integrity sha512-9fSjSaos/fRIVIp+xSJlE6lfwhES7LNtKaCBIamHsjr2na1BiABJPo0mOjjz8GJDURarmCPGqaiVg5mfjb98CQ==
+  dependencies:
+    call-bind-apply-helpers "^1.0.2"
+    es-define-property "^1.0.1"
+    es-errors "^1.3.0"
+    es-object-atoms "^1.1.1"
+    function-bind "^1.1.2"
+    get-proto "^1.0.1"
+    gopd "^1.2.0"
+    has-symbols "^1.1.0"
+    hasown "^2.0.2"
+    math-intrinsics "^1.1.0"
+
+get-proto@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.npmjs.org/get-proto/-/get-proto-1.0.1.tgz"
+  integrity sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g==
   dependencies:
-    function-bind "^1.1.1"
-    has "^1.0.3"
-    has-symbols "^1.0.1"
+    dunder-proto "^1.0.1"
+    es-object-atoms "^1.0.0"
 
 get-stream@^6.0.0:
   version "6.0.1"
   resolved "https://registry.npmjs.org/get-stream/-/get-stream-6.0.1.tgz"
   integrity sha512-ts6Wi+2j3jQjqi70w5AlN8DFnkSwC+MqmxEzdEALB2qXZYV3X/b1CTfgPLGJNMeAWxdPfU8FO1ms3NUfaHCPYg==
 
-glob-parent@^5.1.2, glob-parent@~5.1.2:
+glob-parent@~5.1.2:
   version "5.1.2"
   resolved "https://registry.npmjs.org/glob-parent/-/glob-parent-5.1.2.tgz"
   integrity sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==
   dependencies:
     is-glob "^4.0.1"
 
+glob-to-regex.js@^1.0.1:
+  version "1.2.0"
+  resolved "https://registry.npmjs.org/glob-to-regex.js/-/glob-to-regex.js-1.2.0.tgz"
+  integrity sha512-QMwlOQKU/IzqMUOAZWubUOT8Qft+Y0KQWnX9nK3ch0CJg0tTp4TvGZsTfudYKv2NzoQSyPcnA6TYeIQ3jGichQ==
+
 glob-to-regexp@^0.4.1:
   version "0.4.1"
   resolved "https://registry.npmjs.org/glob-to-regexp/-/glob-to-regexp-0.4.1.tgz"
   integrity sha512-lkX1HJXwyMcprw/5YUZc2s7DrpAiHB21/V+E1rHUrVNokkvB6bqMzT0VfV6/86ZNabt1k14YOIaT7nDvOX3Iiw==
 
-glob@^7.1.3:
-  version "7.2.0"
-  resolved "https://registry.npmjs.org/glob/-/glob-7.2.0.tgz"
-  integrity sha512-lmLf6gtyrPq8tTjSmrO94wBeQbFR3HbLHbuyD69wuyQkImp2hWqMGB47OX65FBkPffO641IP9jWa1z4ivqG26Q==
-  dependencies:
-    fs.realpath "^1.0.0"
-    inflight "^1.0.4"
-    inherits "2"
-    minimatch "^3.0.4"
-    once "^1.3.0"
-    path-is-absolute "^1.0.0"
-
 globals@^11.1.0:
   version "11.12.0"
   resolved "https://registry.npmjs.org/globals/-/globals-11.12.0.tgz"
   integrity sha512-WOBp/EEGUiIsJSp7wcv/y6MO+lV9UoncWqxuFfm8eBwzWNgyfBd6Gz+IeKQ9jCmyhoH99g15M3T+QaVHFjizVA==
 
-globby@^11.0.1:
-  version "11.0.4"
-  resolved "https://registry.npmjs.org/globby/-/globby-11.0.4.tgz"
-  integrity sha512-9O4MVG9ioZJ08ffbcyVYyLOJLk5JQ688pJ4eMGLpdWLHq/Wr1D9BlriLQyL0E+jbkuePVZXYFj47QM/v093wHg==
-  dependencies:
-    array-union "^2.1.0"
-    dir-glob "^3.0.1"
-    fast-glob "^3.1.1"
-    ignore "^5.1.4"
-    merge2 "^1.3.0"
-    slash "^3.0.0"
-
-graceful-fs@^4.1.2, graceful-fs@^4.2.4, graceful-fs@^4.2.6:
-  version "4.2.8"
-  resolved "https://registry.npmjs.org/graceful-fs/-/graceful-fs-4.2.8.tgz"
-  integrity sha512-qkIilPUYcNhJpd33n0GBXTB1MMPp14TxEsEs0pTrsSVucApsYzW5V+Q8Qxhik6KU3evy+qkAAowTByymK0avdg==
+gopd@^1.2.0:
+  version "1.2.0"
+  resolved "https://registry.npmjs.org/gopd/-/gopd-1.2.0.tgz"
+  integrity sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==
 
-graceful-fs@^4.2.9:
-  version "4.2.9"
-  resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.9.tgz#041b05df45755e587a24942279b9d113146e1c96"
-  integrity sha512-NtNxqUcXgpW2iMrfqSfR73Glt39K+BLwWsPs94yR63v45T0Wbej7eRmL5cWfwEgqXnmjQp3zaJTshdRW/qC2ZQ==
+graceful-fs@^4.1.2, graceful-fs@^4.2.11, graceful-fs@^4.2.4, graceful-fs@^4.2.6:
+  version "4.2.11"
+  resolved "https://registry.npmjs.org/graceful-fs/-/graceful-fs-4.2.11.tgz"
+  integrity sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==
 
 handle-thing@^2.0.0:
   version "2.0.1"
   resolved "https://registry.npmjs.org/handle-thing/-/handle-thing-2.0.1.tgz"
   integrity sha512-9Qn4yBxelxoh2Ow62nP+Ka/kMnOXRi8BXnRaUwezLNhqelnN49xKz4F/dPP8OYLxLxq6JDtZb2i9XznUQbNPTg==
 
-has-flag@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz"
-  integrity sha1-tdRU3CGZriJWmfNGfloH87lVuv0=
-
 has-flag@^4.0.0:
   version "4.0.0"
   resolved "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz"
   integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
 
-has-symbols@^1.0.1, has-symbols@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.npmjs.org/has-symbols/-/has-symbols-1.0.2.tgz"
-  integrity sha512-chXa79rL/UC2KlX17jo3vRGz0azaWEx5tGqZg5pO3NUyEJVB17dMruQlzCCOfUvElghKcm5194+BCRvi2Rv/Gw==
-
-has-tostringtag@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.npmjs.org/has-tostringtag/-/has-tostringtag-1.0.0.tgz"
-  integrity sha512-kFjcSNhnlGV1kyoGk7OXKSawH5JOb/LzUc5w9B02hOTO0dfFRjbHQKvg1d6cf3HbeUmtU9VbbV3qzZ2Teh97WQ==
-  dependencies:
-    has-symbols "^1.0.2"
+has-symbols@^1.0.1, has-symbols@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.npmjs.org/has-symbols/-/has-symbols-1.1.0.tgz"
+  integrity sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ==
 
 has@^1.0.3:
   version "1.0.3"
@@ -2754,58 +2513,60 @@ has@^1.0.3:
   dependencies:
     function-bind "^1.1.1"
 
+hasown@^2.0.2:
+  version "2.0.2"
+  resolved "https://registry.npmjs.org/hasown/-/hasown-2.0.2.tgz"
+  integrity sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==
+  dependencies:
+    function-bind "^1.1.2"
+
 hpack.js@^2.1.6:
   version "2.1.6"
   resolved "https://registry.npmjs.org/hpack.js/-/hpack.js-2.1.6.tgz"
-  integrity sha1-h3dMCUnlE/QuhFdbPEVoH63ioLI=
+  integrity sha1-h3dMCUnlE/QuhFdbPEVoH63ioLI= sha512-zJxVehUdMGIKsRaNt7apO2Gqp0BdqW5yaiGHXXmbpvxgBYVZnAql+BJb4RO5ad2MgpbZKn5G6nMnegrH1FcNYQ==
   dependencies:
     inherits "^2.0.1"
     obuf "^1.0.0"
     readable-stream "^2.0.1"
     wbuf "^1.1.0"
 
-html-entities@^2.3.2:
-  version "2.3.2"
-  resolved "https://registry.npmjs.org/html-entities/-/html-entities-2.3.2.tgz"
-  integrity sha512-c3Ab/url5ksaT0WyleslpBEthOzWhrjQbg75y7XUsfSzi3Dgzt0l8w5e7DylRn15MTlMMD58dTfzddNS2kcAjQ==
-
 http-deceiver@^1.2.7:
   version "1.2.7"
   resolved "https://registry.npmjs.org/http-deceiver/-/http-deceiver-1.2.7.tgz"
-  integrity sha1-+nFolEq5pRnTN8sL7HKE3D5yPYc=
-
-http-errors@1.7.2, http-errors@~1.7.2:
-  version "1.7.2"
-  resolved "https://registry.npmjs.org/http-errors/-/http-errors-1.7.2.tgz"
-  integrity sha512-uUQBt3H/cSIVfch6i1EuPNy/YsRSOUBXTVfZ+yR7Zjez3qjBz6i9+i4zjNaoqcoFVI4lQJ5plg63TvGfRSDCRg==
-  dependencies:
-    depd "~1.1.2"
-    inherits "2.0.3"
-    setprototypeof "1.1.1"
-    statuses ">= 1.5.0 < 2"
-    toidentifier "1.0.0"
+  integrity sha1-+nFolEq5pRnTN8sL7HKE3D5yPYc= sha512-LmpOGxTfbpgtGVxJrj5k7asXHCgNZp5nLfp+hWc8QQRqtb7fUy6kRY3BO1h9ddF6yIPYUARgxGOwB42DnxIaNw==
 
 http-errors@~1.6.2:
   version "1.6.3"
   resolved "https://registry.npmjs.org/http-errors/-/http-errors-1.6.3.tgz"
-  integrity sha1-i1VoC7S+KDoLW/TqLjhYC+HZMg0=
+  integrity sha1-i1VoC7S+KDoLW/TqLjhYC+HZMg0= sha512-lks+lVC8dgGyh97jxvxeYTWQFvh4uw4yC12gVl63Cg30sjPX4wuGcdkICVXDAESr6OJGjqGA8Iz5mkeN6zlD7A==
   dependencies:
     depd "~1.1.2"
     inherits "2.0.3"
     setprototypeof "1.1.0"
     statuses ">= 1.4.0 < 2"
 
+http-errors@2.0.0:
+  version "2.0.0"
+  resolved "https://registry.npmjs.org/http-errors/-/http-errors-2.0.0.tgz"
+  integrity sha512-FtwrG/euBzaEjYeRqOgly7G0qviiXoJWnvEH2Z1plBdXgbyjv34pHTSb9zoeHMyDy33+DWy5Wt9Wo+TURtOYSQ==
+  dependencies:
+    depd "2.0.0"
+    inherits "2.0.4"
+    setprototypeof "1.2.0"
+    statuses "2.0.1"
+    toidentifier "1.0.1"
+
 http-parser-js@>=0.5.1:
   version "0.5.5"
   resolved "https://registry.npmjs.org/http-parser-js/-/http-parser-js-0.5.5.tgz"
   integrity sha512-x+JVEkO2PoM8qqpbPbOL3cqHPwerep7OwzK7Ay+sMQjKzaKCqWvjoXm5tqMP9tXWWTnTzAjIhXg+J99XYuPhPA==
 
-http-proxy-middleware@^2.0.0:
-  version "2.0.1"
-  resolved "https://registry.npmjs.org/http-proxy-middleware/-/http-proxy-middleware-2.0.1.tgz"
-  integrity sha512-cfaXRVoZxSed/BmkA7SwBVNI9Kj7HFltaE5rqYOub5kWzWZ+gofV2koVN1j2rMW7pEfSSlCHGJ31xmuyFyfLOg==
+http-proxy-middleware@^2.0.9:
+  version "2.0.9"
+  resolved "https://registry.npmjs.org/http-proxy-middleware/-/http-proxy-middleware-2.0.9.tgz"
+  integrity sha512-c1IyJYLYppU574+YI7R4QyX2ystMtVXZwIdzazUIPIJsHuWNd+mho2j+bKoHftndicGj9yh+xjd+l0yj7VeT1Q==
   dependencies:
-    "@types/http-proxy" "^1.17.5"
+    "@types/http-proxy" "^1.17.8"
     http-proxy "^1.18.1"
     is-glob "^4.0.1"
     is-plain-obj "^3.0.0"
@@ -2825,6 +2586,11 @@ human-signals@^2.1.0:
   resolved "https://registry.npmjs.org/human-signals/-/human-signals-2.1.0.tgz"
   integrity sha512-B4FFZ6q/T2jhhksgkbEW3HBvWIfDW85snkQgawt07S7J5QXTk6BkNV+0yAeZrM5QpMAdYlocGoljn0sJ/WQkFw==
 
+hyperdyperid@^1.2.0:
+  version "1.2.0"
+  resolved "https://registry.npmjs.org/hyperdyperid/-/hyperdyperid-1.2.0.tgz"
+  integrity sha512-Y93lCzHYgGWdrJ66yIktxiaGULYc6oGiABxhcO5AufBeOyoIdZF7bIfLaOrbM0iGIOXQQgxxRrFEnb+Y6w1n4A==
+
 iconv-lite@0.4.24:
   version "0.4.24"
   resolved "https://registry.npmjs.org/iconv-lite/-/iconv-lite-0.4.24.tgz"
@@ -2837,11 +2603,6 @@ icss-utils@^5.0.0, icss-utils@^5.1.0:
   resolved "https://registry.npmjs.org/icss-utils/-/icss-utils-5.1.0.tgz"
   integrity sha512-soFhflCVWLfRNOPU3iv5Z9VUdT44xFRbzjLsEzSr5AQmgqPMTHdU3PMT1Cf1ssx8fLNJDA1juftYl+PUcv3MqA==
 
-ignore@^5.1.4:
-  version "5.1.9"
-  resolved "https://registry.npmjs.org/ignore/-/ignore-5.1.9.tgz"
-  integrity sha512-2zeMQpbKz5dhZ9IwL0gbxSW5w0NK/MSAMtNuhgIHEPmaU3vPdKPL0UdvUCXs5SS4JAwsBxysK5sFMW8ocFiVjQ==
-
 import-fresh@^3.2.1:
   version "3.3.0"
   resolved "https://registry.npmjs.org/import-fresh/-/import-fresh-3.3.0.tgz"
@@ -2858,61 +2619,40 @@ import-local@^3.0.2:
     pkg-dir "^4.2.0"
     resolve-cwd "^3.0.0"
 
-indent-string@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.npmjs.org/indent-string/-/indent-string-4.0.0.tgz"
-  integrity sha512-EdDDZu4A2OyIK7Lr/2zG+w5jmbuk1DVBnEwREQvBzspBJkCEbRa8GxU1lghYcaGJCnRWibjDXlq779X1/y5xwg==
-
-inflight@^1.0.4:
-  version "1.0.6"
-  resolved "https://registry.npmjs.org/inflight/-/inflight-1.0.6.tgz"
-  integrity sha1-Sb1jMdfQLQwJvJEKEHW6gWW1bfk=
-  dependencies:
-    once "^1.3.0"
-    wrappy "1"
+inherits@^2.0.1, inherits@^2.0.3, inherits@~2.0.3, inherits@2.0.4:
+  version "2.0.4"
+  resolved "https://registry.npmjs.org/inherits/-/inherits-2.0.4.tgz"
+  integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
 
-inherits@2, inherits@2.0.3, inherits@^2.0.1, inherits@^2.0.3, inherits@~2.0.3:
+inherits@2.0.3:
   version "2.0.3"
   resolved "https://registry.npmjs.org/inherits/-/inherits-2.0.3.tgz"
-  integrity sha1-Yzwsg+PaQqUC9SRmAiSA9CCCYd4=
+  integrity sha1-Yzwsg+PaQqUC9SRmAiSA9CCCYd4= sha512-x00IRNXNy63jwGkJmzPigoySHbaqpNuzKbBOmzK+g2OdZpQ9w+sxCN+VSB3ja7IAge2OP2qpfxTjeNcyjmW1uw==
 
 interpret@^2.2.0:
   version "2.2.0"
   resolved "https://registry.npmjs.org/interpret/-/interpret-2.2.0.tgz"
   integrity sha512-Ju0Bz/cEia55xDwUWEa8+olFpCiQoypjnQySseKtmjNrnps3P+xfpUmGr90T7yjlVJmOtybRvPXhKMbHr+fWnw==
 
-ip@^1.1.0:
-  version "1.1.5"
-  resolved "https://registry.npmjs.org/ip/-/ip-1.1.5.tgz"
-  integrity sha1-vd7XARQpCCjAoDnnLvJfWq7ENUo=
+ipaddr.js@^2.1.0:
+  version "2.2.0"
+  resolved "https://registry.npmjs.org/ipaddr.js/-/ipaddr.js-2.2.0.tgz"
+  integrity sha512-Ag3wB2o37wslZS19hZqorUnrnzSkpOVy+IiiDEiTqNubEYpYuHWIf6K4psgN2ZWKExS4xhVCrRVfb/wfW8fWJA==
 
 ipaddr.js@1.9.1:
   version "1.9.1"
   resolved "https://registry.npmjs.org/ipaddr.js/-/ipaddr.js-1.9.1.tgz"
   integrity sha512-0KI/607xoxSToH7GjN1FfSbLoU0+btTicjsQSWQlh/hZykN8KpmMf7uYwPW3R+akZ6R/w18ZlXSHBYXiYUPO3g==
 
-ipaddr.js@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.npmjs.org/ipaddr.js/-/ipaddr.js-2.0.1.tgz"
-  integrity sha512-1qTgH9NG+IIJ4yfKs2e6Pp1bZg8wbDbKHT21HrLIeYBTRLgMYKnMTPAuI3Lcs61nfx5h1xlXnbJtH1kX5/d/ng==
-
 is-absolute-url@^3.0.3:
   version "3.0.3"
   resolved "https://registry.npmjs.org/is-absolute-url/-/is-absolute-url-3.0.3.tgz"
   integrity sha512-opmNIX7uFnS96NtPmhWQgQx6/NYFgsUXYMllcfzwWKUMwfo8kku1TvE6hkNcH+Q1ts5cMVrsY7j0bxXQDciu9Q==
 
-is-arguments@^1.0.4:
-  version "1.1.1"
-  resolved "https://registry.npmjs.org/is-arguments/-/is-arguments-1.1.1.tgz"
-  integrity sha512-8Q7EARjzEnKpt/PCD7e1cgUS0a6X8u5tdSiMqXhojOdoV9TsMsiO+9VLC5vAmO8N7/GmXn7yjR8qnA6bVAEzfA==
-  dependencies:
-    call-bind "^1.0.2"
-    has-tostringtag "^1.0.0"
-
 is-arrayish@^0.2.1:
   version "0.2.1"
   resolved "https://registry.npmjs.org/is-arrayish/-/is-arrayish-0.2.1.tgz"
-  integrity sha1-d8mYQFJ6qOyxqLppe4BkWnqSap0=
+  integrity sha1-d8mYQFJ6qOyxqLppe4BkWnqSap0= sha512-zz06S8t0ozoDXMG+ube26zeCTNXcKIPJZJi8hBrF4idCLms4CG9QtK7qBl1boi5ODzFpjswb5JPmHCbMpjaYzg==
 
 is-binary-path@~2.1.0:
   version "2.1.0"
@@ -2928,22 +2668,15 @@ is-core-module@^2.2.0:
   dependencies:
     has "^1.0.3"
 
-is-date-object@^1.0.1:
-  version "1.0.5"
-  resolved "https://registry.npmjs.org/is-date-object/-/is-date-object-1.0.5.tgz"
-  integrity sha512-9YQaSxsAiSwcvS33MBk3wTCVnWK+HhF8VZR2jRxehM16QcVOdHqPn4VPHmRK4lSr38n9JriurInLcP90xsYNfQ==
-  dependencies:
-    has-tostringtag "^1.0.0"
-
-is-docker@^2.0.0, is-docker@^2.1.1:
-  version "2.2.1"
-  resolved "https://registry.npmjs.org/is-docker/-/is-docker-2.2.1.tgz"
-  integrity sha512-F+i2BKsFrH66iaUFc0woD8sLy8getkwTwtOBjvs56Cx4CgJDeKQeqfz8wAYiSb8JOprWhHH5p77PbmYCvvUuXQ==
+is-docker@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.npmjs.org/is-docker/-/is-docker-3.0.0.tgz"
+  integrity sha512-eljcgEDlEns/7AXFosB5K/2nCM4P7FQPkGc/DWLy5rmFEWvZayGrik1d9/QIY5nJ4f9YsVvBkA6kJpHn9rISdQ==
 
 is-extglob@^2.1.1:
   version "2.1.1"
   resolved "https://registry.npmjs.org/is-extglob/-/is-extglob-2.1.1.tgz"
-  integrity sha1-qIwCU1eR8C7TfHahueqXc8gz+MI=
+  integrity sha1-qIwCU1eR8C7TfHahueqXc8gz+MI= sha512-SbKbANkN603Vi4jEZv49LeVJMn4yGwsbzZworEoyEiutsN3nJYdbO36zfhGJ6QEDpOZIFkDtnq5JRxmvl3jsoQ==
 
 is-glob@^4.0.1, is-glob@~4.0.1:
   version "4.0.3"
@@ -2952,21 +2685,23 @@ is-glob@^4.0.1, is-glob@~4.0.1:
   dependencies:
     is-extglob "^2.1.1"
 
+is-inside-container@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.npmjs.org/is-inside-container/-/is-inside-container-1.0.0.tgz"
+  integrity sha512-KIYLCCJghfHZxqjYBE7rEy0OBuTd5xCHS7tHVgvCLkx7StIoaxwNW3hCALgEUjFfeRk+MG/Qxmp/vtETEF3tRA==
+  dependencies:
+    is-docker "^3.0.0"
+
+is-network-error@^1.0.0:
+  version "1.3.0"
+  resolved "https://registry.npmjs.org/is-network-error/-/is-network-error-1.3.0.tgz"
+  integrity sha512-6oIwpsgRfnDiyEDLMay/GqCl3HoAtH5+RUKW29gYkL0QA+ipzpDLA16yQs7/RHCSu+BwgbJaOUqa4A99qNVQVw==
+
 is-number@^7.0.0:
   version "7.0.0"
   resolved "https://registry.npmjs.org/is-number/-/is-number-7.0.0.tgz"
   integrity sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng==
 
-is-path-cwd@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.npmjs.org/is-path-cwd/-/is-path-cwd-2.2.0.tgz"
-  integrity sha512-w942bTcih8fdJPJmQHFzkS76NEP8Kzzvmw92cXsazb8intwLqPibPPdXf4ANdKV3rYMuuQYGIWtvz9JilB3NFQ==
-
-is-path-inside@^3.0.2:
-  version "3.0.3"
-  resolved "https://registry.npmjs.org/is-path-inside/-/is-path-inside-3.0.3.tgz"
-  integrity sha512-Fd4gABb+ycGAmKou8eMftCupSir5lRxqf4aD/vd0cD2qc4HL07OjCeuHMr8Ro4CoMaeCKDB0/ECBOVWjTwUvPQ==
-
 is-plain-obj@^3.0.0:
   version "3.0.0"
   resolved "https://registry.npmjs.org/is-plain-obj/-/is-plain-obj-3.0.0.tgz"
@@ -2979,14 +2714,6 @@ is-plain-object@^2.0.4:
   dependencies:
     isobject "^3.0.1"
 
-is-regex@^1.0.4:
-  version "1.1.4"
-  resolved "https://registry.npmjs.org/is-regex/-/is-regex-1.1.4.tgz"
-  integrity sha512-kvRdxDsxZjhzUX07ZnLydzS1TU/TJlTUHHY4YLL87e37oUA49DfkLqgy+VjFocowy29cKvcSiu+kIv728jTTVg==
-  dependencies:
-    call-bind "^1.0.2"
-    has-tostringtag "^1.0.0"
-
 is-resolvable@^1.1.0:
   version "1.1.0"
   resolved "https://registry.npmjs.org/is-resolvable/-/is-resolvable-1.1.0.tgz"
@@ -2997,40 +2724,31 @@ is-stream@^2.0.0:
   resolved "https://registry.npmjs.org/is-stream/-/is-stream-2.0.1.tgz"
   integrity sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg==
 
-is-wsl@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.npmjs.org/is-wsl/-/is-wsl-2.2.0.tgz"
-  integrity sha512-fKzAra0rGJUUBwGBgNkHZuToZcn+TtXHpeCgmkMJMMYx1sQDYaCSyjJBSCa2nH1DGm7s3n1oBnohoVTBaN7Lww==
+is-wsl@^3.1.0:
+  version "3.1.0"
+  resolved "https://registry.npmjs.org/is-wsl/-/is-wsl-3.1.0.tgz"
+  integrity sha512-UcVfVfaK4Sc4m7X3dUSoHoozQGBEFeDC+zVo06t98xe8CzHSZZBekNXH+tu0NalHolcJ/QAGqS46Hef7QXBIMw==
   dependencies:
-    is-docker "^2.0.0"
+    is-inside-container "^1.0.0"
 
 isarray@~1.0.0:
   version "1.0.0"
   resolved "https://registry.npmjs.org/isarray/-/isarray-1.0.0.tgz"
-  integrity sha1-u5NdSFgsuhaMBoNJV6VKPgcSTxE=
+  integrity sha1-u5NdSFgsuhaMBoNJV6VKPgcSTxE= sha512-VLghIWNM6ELQzo7zwmcg0NmTVyWKYjvIeM83yjp0wRDTmUnrM678fQbcKBo6n2CJEF0szoG//ytg+TKla89ALQ==
 
 isexe@^2.0.0:
   version "2.0.0"
   resolved "https://registry.npmjs.org/isexe/-/isexe-2.0.0.tgz"
-  integrity sha1-6PvzdNxVb/iUehDcsFctYz8s+hA=
+  integrity sha1-6PvzdNxVb/iUehDcsFctYz8s+hA= sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw==
 
 isobject@^3.0.1:
   version "3.0.1"
   resolved "https://registry.npmjs.org/isobject/-/isobject-3.0.1.tgz"
-  integrity sha1-TkMekrEalzFjaqH5yNHMvP2reN8=
+  integrity sha1-TkMekrEalzFjaqH5yNHMvP2reN8= sha512-WhB9zCku7EGTj/HQQRz5aUQEUeoQZH2bWcltRErOpymJ4boYE6wL9Tbr23krRPSZ+C5zqNSrSw+Cc7sZZ4b7vg==
 
-jest-worker@^27.0.2, jest-worker@^27.0.6:
-  version "27.4.4"
-  resolved "https://registry.npmjs.org/jest-worker/-/jest-worker-27.4.4.tgz"
-  integrity sha512-jfwxYJvfua1b1XkyuyPh01ATmgg4e5fPM/muLmhy9Qc6dmiwacQB0MLHaU6IjEsv/+nAixHGxTn8WllA27Pn0w==
-  dependencies:
-    "@types/node" "*"
-    merge-stream "^2.0.0"
-    supports-color "^8.0.0"
-
-jest-worker@^27.4.5:
+jest-worker@^27.0.2, jest-worker@^27.4.5:
   version "27.5.1"
-  resolved "https://registry.yarnpkg.com/jest-worker/-/jest-worker-27.5.1.tgz#8d146f0900e8973b106b6f73cc1e9a8cb86f8db0"
+  resolved "https://registry.npmjs.org/jest-worker/-/jest-worker-27.5.1.tgz"
   integrity sha512-7vuh85V5cdDofPyxn58nrPjBktZo0u9x1g8WtjQol+jZDaE+fhN+cIvTj11GndBnMnyfrUOG1sZQxCdjKh+DKg==
   dependencies:
     "@types/node" "*"
@@ -3042,22 +2760,17 @@ jest-worker@^27.4.5:
   resolved "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz"
   integrity sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==
 
-jsesc@^2.5.1:
-  version "2.5.2"
-  resolved "https://registry.npmjs.org/jsesc/-/jsesc-2.5.2.tgz"
-  integrity sha512-OYu7XEzjkCQ3C5Ps3QIZsQfNpqoJyZZA99wd9aWd05NCtC5pWOkShK2mkL6HXQR6/Cy2lbNdPlZBpuQHXE63gA==
+jsesc@^3.0.2:
+  version "3.1.0"
+  resolved "https://registry.npmjs.org/jsesc/-/jsesc-3.1.0.tgz"
+  integrity sha512-/sM3dO2FOzXjKQhJuo0Q173wf2KOo8t4I8vHy6lF9poUp7bKT0/NHE8fPX23PwfhnykfqnC2xRxOnVw5XuGIaA==
 
 jsesc@~0.5.0:
   version "0.5.0"
   resolved "https://registry.npmjs.org/jsesc/-/jsesc-0.5.0.tgz"
-  integrity sha1-597mbjXW/Bb3EP6R1c9p9w8IkR0=
-
-json-parse-better-errors@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.npmjs.org/json-parse-better-errors/-/json-parse-better-errors-1.0.2.tgz"
-  integrity sha512-mrqyZKfX5EhL7hvqcV6WG1yYjnjeuYDzDhhcAAUrq8Po85NBQBJP+ZDUT75qZQ98IkUoBqdkExkukOU7Ts2wrw==
+  integrity sha1-597mbjXW/Bb3EP6R1c9p9w8IkR0= sha512-uZz5UnB7u4T9LvwmFqXii7pZSouaRPorGs5who1Ip7VO0wxanFvBL7GkM6dTHlgX+jhBApRetaWpnDabOeTcnA==
 
-json-parse-even-better-errors@^2.3.0:
+json-parse-even-better-errors@^2.3.0, json-parse-even-better-errors@^2.3.1:
   version "2.3.1"
   resolved "https://registry.npmjs.org/json-parse-even-better-errors/-/json-parse-even-better-errors-2.3.1.tgz"
   integrity sha512-xyFwyhro/JEof6Ghe2iz2NcXoj2sloNsWr/XsERDK/oiPCfaNhl5ONfp+jQdAZRQQ0IJWNzH9zIZF7li91kh2w==
@@ -3073,11 +2786,9 @@ json-schema-traverse@^1.0.0:
   integrity sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==
 
 json5@^2.1.2:
-  version "2.2.0"
-  resolved "https://registry.npmjs.org/json5/-/json5-2.2.0.tgz"
-  integrity sha512-f+8cldu7X/y7RAJurMEJmdoKXGB/X550w2Nr3tTbezL6RwEE/iMcm+tZnXeoZtKuOq6ft8+CqzEkrIgx1fPoQA==
-  dependencies:
-    minimist "^1.2.5"
+  version "2.2.3"
+  resolved "https://registry.npmjs.org/json5/-/json5-2.2.3.tgz"
+  integrity sha512-XmOWe7eyHYH14cLdVPoyg+GOH3rYX++KpzrylJwSW98t3Nk+U8XOl8FWKOgwtzdb8lXGf6zYwDUzeHMWfxasyg==
 
 kind-of@^6.0.2:
   version "6.0.3"
@@ -3089,6 +2800,14 @@ klona@^2.0.5:
   resolved "https://registry.npmjs.org/klona/-/klona-2.0.5.tgz"
   integrity sha512-pJiBpiXMbt7dkzXe8Ghj/u4FfXOOa98fPW+bihOJ4SjnoijweJrNThJfd3ifXpXhREjpoF2mZVH1GfS9LV3kHQ==
 
+launch-editor@^2.6.1:
+  version "2.11.1"
+  resolved "https://registry.npmjs.org/launch-editor/-/launch-editor-2.11.1.tgz"
+  integrity sha512-SEET7oNfgSaB6Ym0jufAdCeo3meJVeCaaDyzRygy0xsp2BFKCprcfHljTq4QkzTLUxEKkFK6OK4811YM2oSrRg==
+  dependencies:
+    picocolors "^1.1.1"
+    shell-quote "^1.8.3"
+
 lilconfig@^2.0.3:
   version "2.0.4"
   resolved "https://registry.npmjs.org/lilconfig/-/lilconfig-2.0.4.tgz"
@@ -3105,9 +2824,9 @@ loader-runner@^4.2.0:
   integrity sha512-92+huvxMvYlMzMt0iIOukcwYBFpkYJdpl2xsZ7LrlayO7E8SOv+JJUEK17B/dJIHAOLMfh2dZZ/Y18WgmGtYNw==
 
 loader-utils@^2.0.0:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/loader-utils/-/loader-utils-2.0.2.tgz#d6e3b4fb81870721ae4e0868ab11dd638368c129"
-  integrity sha512-TM57VeHptv569d/GKh6TAYdzKblwDNiumOdkFnejjD0XwTH87K90w3O7AiJRqdQoXygvi1VQTJTLGhJl7WqA7A==
+  version "2.0.4"
+  resolved "https://registry.npmjs.org/loader-utils/-/loader-utils-2.0.4.tgz"
+  integrity sha512-xXqpXoINfFhgua9xiqD8fPFHgkoq1mmmpE92WlDbm9rNRd/EbRb+Gqf908T2DMfuHjjJlksiK2RbHVOdD/MqSw==
   dependencies:
     big.js "^5.2.2"
     emojis-list "^3.0.0"
@@ -3123,22 +2842,17 @@ locate-path@^5.0.0:
 lodash.debounce@^4.0.8:
   version "4.0.8"
   resolved "https://registry.npmjs.org/lodash.debounce/-/lodash.debounce-4.0.8.tgz"
-  integrity sha1-gteb/zCmfEAF/9XiUVMArZyk168=
+  integrity sha1-gteb/zCmfEAF/9XiUVMArZyk168= sha512-FT1yDzDYEoYWhnSGnpE/4Kj1fLZkDFyqRb7fNt6FdYOSxlUWAtp42Eh6Wb0rGIv/m9Bgo7x4GhQbm5Ys4SG5ow==
 
 lodash.memoize@^4.1.2:
   version "4.1.2"
   resolved "https://registry.npmjs.org/lodash.memoize/-/lodash.memoize-4.1.2.tgz"
-  integrity sha1-vMbEmkKihA7Zl/Mj6tpezRguC/4=
+  integrity sha1-vMbEmkKihA7Zl/Mj6tpezRguC/4= sha512-t7j+NzmgnQzTAYXcsHYLgimltOV1MXHtlOWf6GjL9Kj8GK5FInw5JotxvbOs+IvV1/Dzo04/fCGfLVs7aXb4Ag==
 
 lodash.uniq@^4.5.0:
   version "4.5.0"
   resolved "https://registry.npmjs.org/lodash.uniq/-/lodash.uniq-4.5.0.tgz"
-  integrity sha1-0CJTc662Uq3BvILklFM5qEJ1R3M=
-
-lodash@^4.17.14:
-  version "4.17.21"
-  resolved "https://registry.npmjs.org/lodash/-/lodash-4.17.21.tgz"
-  integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==
+  integrity sha1-0CJTc662Uq3BvILklFM5qEJ1R3M= sha512-xfBaXQd9ryd9dlSDvnvI0lvxfLJlYAZzXomUYzLKtUeOQvOP5piqAWuGtrhWeqaXK9hhoM/iyJc5AV+XfsX3HQ==
 
 loose-envify@^1.1.0, loose-envify@^1.4.0:
   version "1.4.0"
@@ -3147,13 +2861,6 @@ loose-envify@^1.1.0, loose-envify@^1.4.0:
   dependencies:
     js-tokens "^3.0.0 || ^4.0.0"
 
-lru-cache@^6.0.0:
-  version "6.0.0"
-  resolved "https://registry.npmjs.org/lru-cache/-/lru-cache-6.0.0.tgz"
-  integrity sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA==
-  dependencies:
-    yallist "^4.0.0"
-
 make-dir@^3.0.2, make-dir@^3.1.0:
   version "3.1.0"
   resolved "https://registry.npmjs.org/make-dir/-/make-dir-3.1.0.tgz"
@@ -3161,6 +2868,11 @@ make-dir@^3.0.2, make-dir@^3.1.0:
   dependencies:
     semver "^6.0.0"
 
+math-intrinsics@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz"
+  integrity sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==
+
 mdn-data@2.0.14:
   version "2.0.14"
   resolved "https://registry.npmjs.org/mdn-data/-/mdn-data-2.0.14.tgz"
@@ -3169,55 +2881,67 @@ mdn-data@2.0.14:
 media-typer@0.3.0:
   version "0.3.0"
   resolved "https://registry.npmjs.org/media-typer/-/media-typer-0.3.0.tgz"
-  integrity sha1-hxDXrwqmJvj/+hzgAWhUUmMlV0g=
+  integrity sha1-hxDXrwqmJvj/+hzgAWhUUmMlV0g= sha512-dq+qelQ9akHpcOl/gUVRTxVIOkAJ1wR3QAvb4RsVjS8oVoFjDGTc679wJYmUmknUF5HwMLOgb5O+a3KxfWapPQ==
 
-memfs@^3.4.1:
-  version "3.4.1"
-  resolved "https://registry.yarnpkg.com/memfs/-/memfs-3.4.1.tgz#b78092f466a0dce054d63d39275b24c71d3f1305"
-  integrity sha512-1c9VPVvW5P7I85c35zAdEr1TD5+F11IToIHIlrVIcflfnzPkJa0ZoYEoEdYDP8KgPFoSZ/opDrUsAoZWym3mtw==
+memfs@^4.43.1:
+  version "4.49.0"
+  resolved "https://registry.npmjs.org/memfs/-/memfs-4.49.0.tgz"
+  integrity sha512-L9uC9vGuc4xFybbdOpRLoOAOq1YEBBsocCs5NVW32DfU+CZWWIn3OVF+lB8Gp4ttBVSMazwrTrjv8ussX/e3VQ==
   dependencies:
-    fs-monkey "1.0.3"
+    "@jsonjoy.com/json-pack" "^1.11.0"
+    "@jsonjoy.com/util" "^1.9.0"
+    glob-to-regex.js "^1.0.1"
+    thingies "^2.5.0"
+    tree-dump "^1.0.3"
+    tslib "^2.0.0"
 
-merge-descriptors@1.0.1:
-  version "1.0.1"
-  resolved "https://registry.npmjs.org/merge-descriptors/-/merge-descriptors-1.0.1.tgz"
-  integrity sha1-sAqqVW3YtEVoFQ7J0blT8/kMu2E=
+merge-descriptors@1.0.3:
+  version "1.0.3"
+  resolved "https://registry.npmjs.org/merge-descriptors/-/merge-descriptors-1.0.3.tgz"
+  integrity sha512-gaNvAS7TZ897/rVaZ0nMtAyxNyi/pdbjbAwUpFQpN70GqnVfOiXpeUUMKRBmzXaSQ8DdTX4/0ms62r2K+hE6mQ==
 
 merge-stream@^2.0.0:
   version "2.0.0"
   resolved "https://registry.npmjs.org/merge-stream/-/merge-stream-2.0.0.tgz"
   integrity sha512-abv/qOcuPfk3URPfDzmZU1LKmuw8kT+0nIHvKrKgFrwifol/doWcdA4ZqsWQ8ENrFKkd67Mfpo/LovbIUsbt3w==
 
-merge2@^1.3.0:
-  version "1.4.1"
-  resolved "https://registry.npmjs.org/merge2/-/merge2-1.4.1.tgz"
-  integrity sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==
-
 methods@~1.1.2:
   version "1.1.2"
   resolved "https://registry.npmjs.org/methods/-/methods-1.1.2.tgz"
-  integrity sha1-VSmk1nZUE07cxSZmVoNbD4Ua/O4=
+  integrity sha1-VSmk1nZUE07cxSZmVoNbD4Ua/O4= sha512-iclAHeNqNm68zFtnZ0e+1L2yUIdvzNoauKU4WBA3VvH/vPFieF7qfRlwUZU+DA9P9bPXIS90ulxoUoCH23sV2w==
 
-micromatch@^4.0.2, micromatch@^4.0.4:
-  version "4.0.4"
-  resolved "https://registry.npmjs.org/micromatch/-/micromatch-4.0.4.tgz"
-  integrity sha512-pRmzw/XUcwXGpD9aI9q/0XOwLNygjETJ8y0ao0wdqprrzDa4YnxLcz7fQRZr8voh8V10kGhABbNcHVk5wHgWwg==
+micromatch@^4.0.2:
+  version "4.0.8"
+  resolved "https://registry.npmjs.org/micromatch/-/micromatch-4.0.8.tgz"
+  integrity sha512-PXwfBhYu0hBCPw8Dn0E+WDYb7af3dSLVWKi3HGv84IdF4TyFoC0ysxFd0Goxw7nSv4T/PzEJQxsYsEiFCKo2BA==
   dependencies:
-    braces "^3.0.1"
-    picomatch "^2.2.3"
+    braces "^3.0.3"
+    picomatch "^2.3.1"
+
+mime-db@^1.54.0:
+  version "1.54.0"
+  resolved "https://registry.npmjs.org/mime-db/-/mime-db-1.54.0.tgz"
+  integrity sha512-aU5EJuIN2WDemCcAp2vFBfp/m4EAhWJnUNSSw0ixs7/kXbd6Pg64EmwJkNdFhB8aWt1sH2CTXrLxo/iAGV3oPQ==
 
-mime-db@1.51.0, "mime-db@>= 1.43.0 < 2":
+"mime-db@>= 1.43.0 < 2", mime-db@1.51.0:
   version "1.51.0"
   resolved "https://registry.npmjs.org/mime-db/-/mime-db-1.51.0.tgz"
   integrity sha512-5y8A56jg7XVQx2mbv1lu49NR4dokRnhZYTtL+KGfaa27uq4pSTXkwQkFJl4pkRMyNFz/EtYDSkiiEHx3F7UN6g==
 
-mime-types@^2.1.27, mime-types@^2.1.31, mime-types@~2.1.17, mime-types@~2.1.24:
+mime-types@^2.1.27, mime-types@~2.1.17, mime-types@~2.1.24, mime-types@~2.1.34:
   version "2.1.34"
   resolved "https://registry.npmjs.org/mime-types/-/mime-types-2.1.34.tgz"
   integrity sha512-6cP692WwGIs9XXdOO4++N+7qjqv0rqxxVvJ3VHPh/Sc9mVZcQP+ZGhkKiTvWMQRr2tbHkJP/Yn7Y0npb3ZBs4A==
   dependencies:
     mime-db "1.51.0"
 
+mime-types@^3.0.1:
+  version "3.0.1"
+  resolved "https://registry.npmjs.org/mime-types/-/mime-types-3.0.1.tgz"
+  integrity sha512-xRc4oEhT6eaBpU1XF7AjpOFD+xQmXNB5OVKwp4tqCuBpHLS/ZbBDrc07mYTDqVMg6PfxUjjNp85O6Cd2Z/5HWA==
+  dependencies:
+    mime-db "^1.54.0"
+
 mime@1.6.0:
   version "1.6.0"
   resolved "https://registry.npmjs.org/mime/-/mime-1.6.0.tgz"
@@ -3230,7 +2954,7 @@ mimic-fn@^2.1.0:
 
 mini-css-extract-plugin@^2.6.0:
   version "2.6.0"
-  resolved "https://registry.yarnpkg.com/mini-css-extract-plugin/-/mini-css-extract-plugin-2.6.0.tgz#578aebc7fc14d32c0ad304c2c34f08af44673f5e"
+  resolved "https://registry.npmjs.org/mini-css-extract-plugin/-/mini-css-extract-plugin-2.6.0.tgz"
   integrity sha512-ndG8nxCEnAemsg4FSgS+yNyHKgkTB4nPKqCOgh65j3/30qqC5RaSQQXMm++Y6sb6E1zRSxPkztj9fqxhS1Eo6w==
   dependencies:
     schema-utils "^4.0.0"
@@ -3240,87 +2964,58 @@ minimalistic-assert@^1.0.0:
   resolved "https://registry.npmjs.org/minimalistic-assert/-/minimalistic-assert-1.0.1.tgz"
   integrity sha512-UtJcAD4yEaGtjPezWuO9wC4nwUnVH/8/Im3yEHQP4b67cXlD/Qr9hdITCU1xDbSEXg2XKNaP8jsReV7vQd00/A==
 
-minimatch@^3.0.4:
-  version "3.0.4"
-  resolved "https://registry.npmjs.org/minimatch/-/minimatch-3.0.4.tgz"
-  integrity sha512-yJHVQEhyqPLUTgt9B83PXu6W3rx4MvvHvSUvToogpwoGDOUQ+yDrR0HRot+yOCdCO7u4hX3pWft6kWBBcqh0UA==
-  dependencies:
-    brace-expansion "^1.1.7"
-
-minimist@^1.2.5:
-  version "1.2.5"
-  resolved "https://registry.npmjs.org/minimist/-/minimist-1.2.5.tgz"
-  integrity sha512-FM9nNUYrRBAELZQT3xeZQ7fmMOBg6nWNmJKTcgsJeaLstP/UODVpGsr5OhXhhXg6f+qtJ8uiZ+PUxkDWcgIXLw==
-
-mkdirp@^0.5.5:
-  version "0.5.5"
-  resolved "https://registry.npmjs.org/mkdirp/-/mkdirp-0.5.5.tgz"
-  integrity sha512-NKmAlESf6jMGym1++R0Ra7wvhV+wFW63FaSOFPwRahvea0gMUcGUhVeAg/0BC0wiv9ih5NYPB1Wn1UEI1/L+xQ==
-  dependencies:
-    minimist "^1.2.5"
-
 ms@2.0.0:
   version "2.0.0"
   resolved "https://registry.npmjs.org/ms/-/ms-2.0.0.tgz"
-  integrity sha1-VgiurfwAvmwpAd9fmGF4jeDVl8g=
-
-ms@2.1.1:
-  version "2.1.1"
-  resolved "https://registry.npmjs.org/ms/-/ms-2.1.1.tgz"
-  integrity sha512-tgp+dl5cGk28utYktBsrFqA7HKgrhgPsg6Z/EfhWI4gl1Hwq8B/GmY/0oXZ6nF8hDVesS/FpnYaD/kOWhYQvyg==
+  integrity sha1-VgiurfwAvmwpAd9fmGF4jeDVl8g= sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A==
 
-ms@2.1.2, ms@^2.1.1:
+ms@2.1.2:
   version "2.1.2"
   resolved "https://registry.npmjs.org/ms/-/ms-2.1.2.tgz"
   integrity sha512-sGkPx+VjMtmA6MX27oA4FBFELFCZZ4S4XqeGOXCv68tT+jb3vk/RyaKWP0PTKyWtmLSM0b+adUTEvbs1PEaH2w==
 
-multicast-dns-service-types@^1.1.0:
-  version "1.1.0"
-  resolved "https://registry.npmjs.org/multicast-dns-service-types/-/multicast-dns-service-types-1.1.0.tgz"
-  integrity sha1-iZ8R2WhuXgXLkbNdXw5jt3PPyQE=
+ms@2.1.3:
+  version "2.1.3"
+  resolved "https://registry.npmjs.org/ms/-/ms-2.1.3.tgz"
+  integrity sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==
 
-multicast-dns@^6.0.1:
-  version "6.2.3"
-  resolved "https://registry.npmjs.org/multicast-dns/-/multicast-dns-6.2.3.tgz"
-  integrity sha512-ji6J5enbMyGRHIAkAOu3WdV8nggqviKCEKtXcOqfphZZtQrmHKycfynJ2V7eVPUA4NhJ6V7Wf4TmGbTwKE9B6g==
+multicast-dns@^7.2.5:
+  version "7.2.5"
+  resolved "https://registry.npmjs.org/multicast-dns/-/multicast-dns-7.2.5.tgz"
+  integrity sha512-2eznPJP8z2BFLX50tf0LuODrpINqP1RVIm/CObbTcBRITQgmC/TjcREF1NeTBzIcR5XO/ukWo+YHOjBbFwIupg==
   dependencies:
-    dns-packet "^1.3.1"
+    dns-packet "^5.2.2"
     thunky "^1.0.2"
 
-nanoid@^3.1.30:
-  version "3.2.0"
-  resolved "https://registry.npmjs.org/nanoid/-/nanoid-3.2.0.tgz"
-  integrity sha512-fmsZYa9lpn69Ad5eDn7FMcnnSR+8R34W9qJEijxYhTbfOWzr22n1QxCMzXLK+ODyW2973V3Fux959iQoUxzUIA==
+nanoid@^3.3.11:
+  version "3.3.11"
+  resolved "https://registry.npmjs.org/nanoid/-/nanoid-3.3.11.tgz"
+  integrity sha512-N8SpfPUnUp1bK+PMYW8qSWdl9U+wwNWI4QKxOYDy9JAro3WMX7p2OeVRF9v+347pnakNevPmiHhNmZ2HbFA76w==
 
-nanoid@^3.3.1:
-  version "3.3.2"
-  resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.3.2.tgz#c89622fafb4381cd221421c69ec58547a1eec557"
-  integrity sha512-CuHBogktKwpm5g2sRgv83jEy2ijFzBwMoYA60orPDR7ynsLijJDqgsi4RDGj3OJpy3Ieb+LYwiRmIOGyytgITA==
+negotiator@~0.6.4:
+  version "0.6.4"
+  resolved "https://registry.npmjs.org/negotiator/-/negotiator-0.6.4.tgz"
+  integrity sha512-myRT3DiWPHqho5PrJaIRyaMv2kgYf0mUVgBNOYMuCH5Ki1yEiQaf/ZJuQ62nvpc44wL5WDbTX7yGJi1Neevw8w==
 
-negotiator@0.6.2:
-  version "0.6.2"
-  resolved "https://registry.npmjs.org/negotiator/-/negotiator-0.6.2.tgz"
-  integrity sha512-hZXc7K2e+PgeI1eDBe/10Ard4ekbfrrqG8Ep+8Jmf4JID2bNg7NvCPOZN+kfF574pFQI7mum2AUqDidoKqcTOw==
+negotiator@0.6.3:
+  version "0.6.3"
+  resolved "https://registry.npmjs.org/negotiator/-/negotiator-0.6.3.tgz"
+  integrity sha512-+EUsqGPLsM+j/zdChZjsnX51g4XrHFOIXwfnCVPGlQk/k5giakcKsuxCObBRu6DSm9opw/O6slWbJdghQM4bBg==
 
 neo-async@^2.6.2:
   version "2.6.2"
   resolved "https://registry.npmjs.org/neo-async/-/neo-async-2.6.2.tgz"
   integrity sha512-Yd3UES5mWCSqR+qNT93S3UoYUkqAZ9lLg8a7g9rimsWmYGK8cVToA4/sF3RrshdyV3sAGMXVUmpMYOw+dLpOuw==
 
-node-forge@^1.2.0:
-  version "1.2.1"
-  resolved "https://registry.npmjs.org/node-forge/-/node-forge-1.2.1.tgz"
-  integrity sha512-Fcvtbb+zBcZXbTTVwqGA5W+MKBj56UjVRevvchv5XrcyXbmNdesfZL37nlcWOfpgHhgmxApw3tQbTr4CqNmX4w==
-
-node-releases@^2.0.1:
-  version "2.0.1"
-  resolved "https://registry.npmjs.org/node-releases/-/node-releases-2.0.1.tgz"
-  integrity sha512-CqyzN6z7Q6aMeF/ktcMVTzhAHCEpf8SOarwpzpf8pNBY2k5/oM34UHldUwp8VKI7uxct2HxSRdJjBaZeESzcxA==
+node-forge@^1:
+  version "1.3.1"
+  resolved "https://registry.npmjs.org/node-forge/-/node-forge-1.3.1.tgz"
+  integrity sha512-dPEtOeMvF9VMcYV/1Wb8CPoVAXtp6MKMlcbAt4ddqmGqUJ6fQZFXkNZNkNlfevtNkGtaSoXf/vNNNSvgrdXwtA==
 
-node-releases@^2.0.2:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-2.0.2.tgz#7139fe71e2f4f11b47d4d2986aaf8c48699e0c01"
-  integrity sha512-XxYDdcQ6eKqp/YjI+tb2C5WM2LgjnZrfYg4vgQt49EK268b6gYCHsBLrK2qvJo4FmCtqmKezb0WZFK4fkrZNsg==
+node-releases@^2.0.21:
+  version "2.0.25"
+  resolved "https://registry.npmjs.org/node-releases/-/node-releases-2.0.25.tgz"
+  integrity sha512-4auku8B/vw5psvTiiN9j1dAOsXvMoGqJuKJcR+dTdqiXEK20mMTk1UEo3HS16LeGQsVG6+qKTPM9u/qQ2LqATA==
 
 normalize-path@^3.0.0, normalize-path@~3.0.0:
   version "3.0.0"
@@ -3330,7 +3025,7 @@ normalize-path@^3.0.0, normalize-path@~3.0.0:
 normalize-range@^0.1.2:
   version "0.1.2"
   resolved "https://registry.npmjs.org/normalize-range/-/normalize-range-0.1.2.tgz"
-  integrity sha1-LRDAa9/TEuqXd2laTShDlFa3WUI=
+  integrity sha1-LRDAa9/TEuqXd2laTShDlFa3WUI= sha512-bdok/XvKII3nUpklnV6P2hxtMNrCboOjAcyBuQnWEhO665FwrSNRxU+AqpsyvO6LgGYPspN+lu5CLtw4jPRKNA==
 
 normalize-url@^6.0.1:
   version "6.1.0"
@@ -3354,15 +3049,12 @@ nth-check@^2.0.0:
 object-assign@^4.1.1:
   version "4.1.1"
   resolved "https://registry.npmjs.org/object-assign/-/object-assign-4.1.1.tgz"
-  integrity sha1-IQmtx5ZYh8/AXLvUQsrIv7s2CGM=
+  integrity sha1-IQmtx5ZYh8/AXLvUQsrIv7s2CGM= sha512-rJgTQnkUnH1sFw8yT6VSU3zD3sWmu6sZhIseY8VX+GRu3P6F7Fu+JNDoXfklElbLJSnc3FUQHVe4cU5hj+BcUg==
 
-object-is@^1.0.1:
-  version "1.1.5"
-  resolved "https://registry.npmjs.org/object-is/-/object-is-1.1.5.tgz"
-  integrity sha512-3cyDsyHgtmi7I7DfSSI2LDp6SK2lwvtbg0p0R1e0RvTqF5ceGx+K2dfSjm1bKDMVCFEDAQvy+o8c6a7VujOddw==
-  dependencies:
-    call-bind "^1.0.2"
-    define-properties "^1.1.3"
+object-inspect@^1.13.3:
+  version "1.13.4"
+  resolved "https://registry.npmjs.org/object-inspect/-/object-inspect-1.13.4.tgz"
+  integrity sha512-W67iLl4J2EXEGTbfeHCffrjDfitvLANg0UlX3wFUUSTx92KXRFegMHUVgSqE+wvhAbi4WqjGg9czysTV2Epbew==
 
 object-keys@^1.0.12, object-keys@^1.1.1:
   version "1.1.1"
@@ -3384,24 +3076,17 @@ obuf@^1.0.0, obuf@^1.1.2:
   resolved "https://registry.npmjs.org/obuf/-/obuf-1.1.2.tgz"
   integrity sha512-PX1wu0AmAdPqOL1mWhqmlOd8kOIZQwGZw6rh7uby9fTc5lhaOWFLX3I6R1hrF9k3zUY40e6igsLGkDXK92LJNg==
 
-on-finished@~2.3.0:
-  version "2.3.0"
-  resolved "https://registry.npmjs.org/on-finished/-/on-finished-2.3.0.tgz"
-  integrity sha1-IPEzZIGwg811M3mSoWlxqi2QaUc=
+on-finished@^2.4.1, on-finished@2.4.1:
+  version "2.4.1"
+  resolved "https://registry.npmjs.org/on-finished/-/on-finished-2.4.1.tgz"
+  integrity sha512-oVlzkg3ENAhCk2zdv7IJwd/QUD4z2RxRwpkcGY8psCVcCYZNq4wYnVWALHM+brtuJjePWiYF/ClmuDr8Ch5+kg==
   dependencies:
     ee-first "1.1.1"
 
-on-headers@~1.0.2:
-  version "1.0.2"
-  resolved "https://registry.npmjs.org/on-headers/-/on-headers-1.0.2.tgz"
-  integrity sha512-pZAE+FJLoyITytdqK0U5s+FIpjN0JP3OzFi/u8Rx+EV5/W+JTWGXG8xFzevE7AjBfDqHv/8vL8qQsIhHnqRkrA==
-
-once@^1.3.0:
-  version "1.4.0"
-  resolved "https://registry.npmjs.org/once/-/once-1.4.0.tgz"
-  integrity sha1-WDsap3WWHUsROsF9nFC6753Xa9E=
-  dependencies:
-    wrappy "1"
+on-headers@~1.1.0:
+  version "1.1.0"
+  resolved "https://registry.npmjs.org/on-headers/-/on-headers-1.1.0.tgz"
+  integrity sha512-737ZY3yNnXy37FHkQxPzt4UZ2UWPWiCZWLvFZ4fu5cueciegX0zGPnrlY6bwRg4FdQOe9YU8MkmJwGhoMybl8A==
 
 onetime@^5.1.2:
   version "5.1.2"
@@ -3410,14 +3095,15 @@ onetime@^5.1.2:
   dependencies:
     mimic-fn "^2.1.0"
 
-open@^8.0.9:
-  version "8.4.0"
-  resolved "https://registry.npmjs.org/open/-/open-8.4.0.tgz"
-  integrity sha512-XgFPPM+B28FtCCgSb9I+s9szOC1vZRSwgWsRUA5ylIxRTgKozqjOCrVOqGsYABPYK5qnfqClxZTFBa8PKt2v6Q==
+open@^10.0.3:
+  version "10.2.0"
+  resolved "https://registry.npmjs.org/open/-/open-10.2.0.tgz"
+  integrity sha512-YgBpdJHPyQ2UE5x+hlSXcnejzAvD0b22U2OuAP+8OnlJT+PjWPxtgmGqKKc+RgTM63U9gN0YzrYc71R2WT/hTA==
   dependencies:
-    define-lazy-prop "^2.0.0"
-    is-docker "^2.1.1"
-    is-wsl "^2.2.0"
+    default-browser "^5.2.1"
+    define-lazy-prop "^3.0.0"
+    is-inside-container "^1.0.0"
+    wsl-utils "^0.1.0"
 
 p-limit@^2.2.0:
   version "2.3.0"
@@ -3433,19 +3119,13 @@ p-locate@^4.1.0:
   dependencies:
     p-limit "^2.2.0"
 
-p-map@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.npmjs.org/p-map/-/p-map-4.0.0.tgz"
-  integrity sha512-/bjOqmgETBYB5BoEeGVea8dmvHb2m9GLy1E9W43yeyfP6QQCZGFNa+XRceJEuDB6zqr+gKpIAmlLebMpykw/MQ==
-  dependencies:
-    aggregate-error "^3.0.0"
-
-p-retry@^4.5.0:
-  version "4.6.1"
-  resolved "https://registry.npmjs.org/p-retry/-/p-retry-4.6.1.tgz"
-  integrity sha512-e2xXGNhZOZ0lfgR9kL34iGlU8N/KO0xZnQxVEwdeOvpqNDQfdnxIYizvWtK8RglUa3bGqI8g0R/BdfzLMxRkiA==
+p-retry@^6.2.0:
+  version "6.2.1"
+  resolved "https://registry.npmjs.org/p-retry/-/p-retry-6.2.1.tgz"
+  integrity sha512-hEt02O4hUct5wtwg4H4KcWgDdm+l1bOaEy/hWzd8xtXB9BqxTWBBhb+2ImAtH4Cv4rPjV76xN3Zumqk3k3AhhQ==
   dependencies:
-    "@types/retry" "^0.12.0"
+    "@types/retry" "0.12.2"
+    is-network-error "^1.0.0"
     retry "^0.13.1"
 
 p-try@^2.0.0:
@@ -3480,11 +3160,6 @@ path-exists@^4.0.0:
   resolved "https://registry.npmjs.org/path-exists/-/path-exists-4.0.0.tgz"
   integrity sha512-ak9Qy5Q7jYb2Wwcey5Fpvg2KoAc/ZIhLSLOSBmRmygPsGwkVVt0fZa0qrtMz+m6tJTAHfZQ8FnmB4MG4LWy7/w==
 
-path-is-absolute@^1.0.0:
-  version "1.0.1"
-  resolved "https://registry.npmjs.org/path-is-absolute/-/path-is-absolute-1.0.1.tgz"
-  integrity sha1-F0uSaHNVNP+8es5r9TpanhtcX18=
-
 path-key@^3.0.0, path-key@^3.1.0:
   version "3.1.1"
   resolved "https://registry.npmjs.org/path-key/-/path-key-3.1.1.tgz"
@@ -3495,25 +3170,25 @@ path-parse@^1.0.6:
   resolved "https://registry.npmjs.org/path-parse/-/path-parse-1.0.7.tgz"
   integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
 
-path-to-regexp@0.1.7:
-  version "0.1.7"
-  resolved "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.7.tgz"
-  integrity sha1-32BBeABfUi8V60SQ5yR6G/qmf4w=
+path-to-regexp@0.1.12:
+  version "0.1.12"
+  resolved "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.12.tgz"
+  integrity sha512-RA1GjUVMnvYFxuqovrEqZoxxW5NUZqbwKtYz/Tt7nXerk0LbLblQmrsgdeOxV5SFHf0UDggjS/bSeOZwt1pmEQ==
 
 path-type@^4.0.0:
   version "4.0.0"
   resolved "https://registry.npmjs.org/path-type/-/path-type-4.0.0.tgz"
   integrity sha512-gDKb8aZMDeD/tZWs9P6+q0J9Mwkdl6xMV8TjnGP3qJVJ06bdMgkbBlLU8IdfOsIsFz2BW1rNVT3XuNEl8zPAvw==
 
-picocolors@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.npmjs.org/picocolors/-/picocolors-1.0.0.tgz"
-  integrity sha512-1fygroTLlHu66zi26VoTDv8yRgm0Fccecssto+MhsZ0D/DGW2sm8E8AjW7NU5VVTRt5GxbeZ5qBuJr+HyLYkjQ==
+picocolors@^1.0.0, picocolors@^1.1.1:
+  version "1.1.1"
+  resolved "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz"
+  integrity sha512-xceH2snhtb5M9liqDsmEw56le376mTZkEX/jEb/RxNFyegNul7eNslCXP9FDj/Lcu0X8KEyMceP2ntpaHrDEVA==
 
-picomatch@^2.0.4, picomatch@^2.2.1, picomatch@^2.2.3:
-  version "2.3.0"
-  resolved "https://registry.npmjs.org/picomatch/-/picomatch-2.3.0.tgz"
-  integrity sha512-lY1Q/PiJGC2zOv/z391WOTD+Z02bCgsFfvxoXXf6h7kv9o+WmsmzYqrAwY63sNgOxE4xEdq0WyUnXfKeBrSvYw==
+picomatch@^2.0.4, picomatch@^2.2.1, picomatch@^2.3.1:
+  version "2.3.1"
+  resolved "https://registry.npmjs.org/picomatch/-/picomatch-2.3.1.tgz"
+  integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==
 
 pkg-dir@^4.1.0, pkg-dir@^4.2.0:
   version "4.2.0"
@@ -3522,15 +3197,6 @@ pkg-dir@^4.1.0, pkg-dir@^4.2.0:
   dependencies:
     find-up "^4.0.0"
 
-portfinder@^1.0.28:
-  version "1.0.28"
-  resolved "https://registry.npmjs.org/portfinder/-/portfinder-1.0.28.tgz"
-  integrity sha512-Se+2isanIcEqf2XMHjyUKskczxbPH7dQnlMjXX6+dybayyHvAf/TCgyMRlzf/B6QDhAEFOGes0pzRo3by4AbMA==
-  dependencies:
-    async "^2.6.2"
-    debug "^3.1.1"
-    mkdirp "^0.5.5"
-
 postcss-attribute-case-insensitive@^5.0.0:
   version "5.0.0"
   resolved "https://registry.npmjs.org/postcss-attribute-case-insensitive/-/postcss-attribute-case-insensitive-5.0.0.tgz"
@@ -3548,28 +3214,28 @@ postcss-calc@^8.0.0:
 
 postcss-clamp@^4.1.0:
   version "4.1.0"
-  resolved "https://registry.yarnpkg.com/postcss-clamp/-/postcss-clamp-4.1.0.tgz#7263e95abadd8c2ba1bd911b0b5a5c9c93e02363"
+  resolved "https://registry.npmjs.org/postcss-clamp/-/postcss-clamp-4.1.0.tgz"
   integrity sha512-ry4b1Llo/9zz+PKC+030KUnPITTJAHeOwjfAyyB60eT0AorGLdzp52s31OsPRHRf8NchkgFoG2y6fCfn1IV1Ow==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 postcss-color-functional-notation@^4.2.2:
   version "4.2.2"
-  resolved "https://registry.yarnpkg.com/postcss-color-functional-notation/-/postcss-color-functional-notation-4.2.2.tgz#f59ccaeb4ee78f1b32987d43df146109cc743073"
+  resolved "https://registry.npmjs.org/postcss-color-functional-notation/-/postcss-color-functional-notation-4.2.2.tgz"
   integrity sha512-DXVtwUhIk4f49KK5EGuEdgx4Gnyj6+t2jBSEmxvpIK9QI40tWrpS2Pua8Q7iIZWBrki2QOaeUdEaLPPa91K0RQ==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 postcss-color-hex-alpha@^8.0.3:
   version "8.0.3"
-  resolved "https://registry.yarnpkg.com/postcss-color-hex-alpha/-/postcss-color-hex-alpha-8.0.3.tgz#61a0fd151d28b128aa6a8a21a2dad24eebb34d52"
+  resolved "https://registry.npmjs.org/postcss-color-hex-alpha/-/postcss-color-hex-alpha-8.0.3.tgz"
   integrity sha512-fESawWJCrBV035DcbKRPAVmy21LpoyiXdPTuHUfWJ14ZRjY7Y7PA6P4g8z6LQGYhU1WAxkTxjIjurXzoe68Glw==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 postcss-color-rebeccapurple@^7.0.2:
   version "7.0.2"
-  resolved "https://registry.yarnpkg.com/postcss-color-rebeccapurple/-/postcss-color-rebeccapurple-7.0.2.tgz#5d397039424a58a9ca628762eb0b88a61a66e079"
+  resolved "https://registry.npmjs.org/postcss-color-rebeccapurple/-/postcss-color-rebeccapurple-7.0.2.tgz"
   integrity sha512-SFc3MaocHaQ6k3oZaFwH8io6MdypkUtEy/eXzXEB1vEQlO3S3oDc/FSZA8AsS04Z25RirQhlDlHLh3dn7XewWw==
   dependencies:
     postcss-value-parser "^4.2.0"
@@ -3598,7 +3264,7 @@ postcss-custom-media@^8.0.0:
 
 postcss-custom-properties@^12.1.5:
   version "12.1.5"
-  resolved "https://registry.yarnpkg.com/postcss-custom-properties/-/postcss-custom-properties-12.1.5.tgz#e669cfff89b0ea6fc85c45864a32b450cb6b196f"
+  resolved "https://registry.npmjs.org/postcss-custom-properties/-/postcss-custom-properties-12.1.5.tgz"
   integrity sha512-FHbbB/hRo/7cxLGkc2NS7cDRIDN1oFqQnUKBiyh4b/gwk8DD8udvmRDpUhEK836kB8ggUCieHVOvZDnF9XhI3g==
   dependencies:
     postcss-value-parser "^4.2.0"
@@ -3612,7 +3278,7 @@ postcss-custom-selectors@^6.0.0:
 
 postcss-dir-pseudo-class@^6.0.4:
   version "6.0.4"
-  resolved "https://registry.yarnpkg.com/postcss-dir-pseudo-class/-/postcss-dir-pseudo-class-6.0.4.tgz#9afe49ea631f0cb36fa0076e7c2feb4e7e3f049c"
+  resolved "https://registry.npmjs.org/postcss-dir-pseudo-class/-/postcss-dir-pseudo-class-6.0.4.tgz"
   integrity sha512-I8epwGy5ftdzNWEYok9VjW9whC4xnelAtbajGv4adql4FIF09rnrxnA9Y8xSHN47y7gqFIv10C5+ImsLeJpKBw==
   dependencies:
     postcss-selector-parser "^6.0.9"
@@ -3639,7 +3305,7 @@ postcss-discard-overridden@^5.0.1:
 
 postcss-double-position-gradients@^3.1.1:
   version "3.1.1"
-  resolved "https://registry.yarnpkg.com/postcss-double-position-gradients/-/postcss-double-position-gradients-3.1.1.tgz#a12cfdb7d11fa1a99ccecc747f0c19718fb37152"
+  resolved "https://registry.npmjs.org/postcss-double-position-gradients/-/postcss-double-position-gradients-3.1.1.tgz"
   integrity sha512-jM+CGkTs4FcG53sMPjrrGE0rIvLDdCrqMzgDC5fLI7JHDO7o6QG8C5TQBtExb13hdBdoH9C2QVbG4jo2y9lErQ==
   dependencies:
     "@csstools/postcss-progressive-custom-properties" "^1.1.0"
@@ -3647,21 +3313,21 @@ postcss-double-position-gradients@^3.1.1:
 
 postcss-env-function@^4.0.6:
   version "4.0.6"
-  resolved "https://registry.yarnpkg.com/postcss-env-function/-/postcss-env-function-4.0.6.tgz#7b2d24c812f540ed6eda4c81f6090416722a8e7a"
+  resolved "https://registry.npmjs.org/postcss-env-function/-/postcss-env-function-4.0.6.tgz"
   integrity sha512-kpA6FsLra+NqcFnL81TnsU+Z7orGtDTxcOhl6pwXeEq1yFPpRMkCDpHhrz8CFQDr/Wfm0jLiNQ1OsGGPjlqPwA==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 postcss-focus-visible@^6.0.4:
   version "6.0.4"
-  resolved "https://registry.yarnpkg.com/postcss-focus-visible/-/postcss-focus-visible-6.0.4.tgz#50c9ea9afa0ee657fb75635fabad25e18d76bf9e"
+  resolved "https://registry.npmjs.org/postcss-focus-visible/-/postcss-focus-visible-6.0.4.tgz"
   integrity sha512-QcKuUU/dgNsstIK6HELFRT5Y3lbrMLEOwG+A4s5cA+fx3A3y/JTq3X9LaOj3OC3ALH0XqyrgQIgey/MIZ8Wczw==
   dependencies:
     postcss-selector-parser "^6.0.9"
 
 postcss-focus-within@^5.0.4:
   version "5.0.4"
-  resolved "https://registry.yarnpkg.com/postcss-focus-within/-/postcss-focus-within-5.0.4.tgz#5b1d2ec603195f3344b716c0b75f61e44e8d2e20"
+  resolved "https://registry.npmjs.org/postcss-focus-within/-/postcss-focus-within-5.0.4.tgz"
   integrity sha512-vvjDN++C0mu8jz4af5d52CB184ogg/sSxAFS+oUJQq2SuCe7T5U2iIsVJtsCp2d6R4j0jr5+q3rPkBVZkXD9fQ==
   dependencies:
     postcss-selector-parser "^6.0.9"
@@ -3673,12 +3339,12 @@ postcss-font-variant@^5.0.0:
 
 postcss-gap-properties@^3.0.3:
   version "3.0.3"
-  resolved "https://registry.yarnpkg.com/postcss-gap-properties/-/postcss-gap-properties-3.0.3.tgz#6401bb2f67d9cf255d677042928a70a915e6ba60"
+  resolved "https://registry.npmjs.org/postcss-gap-properties/-/postcss-gap-properties-3.0.3.tgz"
   integrity sha512-rPPZRLPmEKgLk/KlXMqRaNkYTUpE7YC+bOIQFN5xcu1Vp11Y4faIXv6/Jpft6FMnl6YRxZqDZG0qQOW80stzxQ==
 
 postcss-image-set-function@^4.0.6:
   version "4.0.6"
-  resolved "https://registry.yarnpkg.com/postcss-image-set-function/-/postcss-image-set-function-4.0.6.tgz#bcff2794efae778c09441498f40e0c77374870a9"
+  resolved "https://registry.npmjs.org/postcss-image-set-function/-/postcss-image-set-function-4.0.6.tgz"
   integrity sha512-KfdC6vg53GC+vPd2+HYzsZ6obmPqOk6HY09kttU19+Gj1nC3S3XBVEXDHxkhxTohgZqzbUb94bKXvKDnYWBm/A==
   dependencies:
     postcss-value-parser "^4.2.0"
@@ -3690,7 +3356,7 @@ postcss-initial@^4.0.1:
 
 postcss-lab-function@^4.1.2:
   version "4.1.2"
-  resolved "https://registry.yarnpkg.com/postcss-lab-function/-/postcss-lab-function-4.1.2.tgz#b75afe43ba9c1f16bfe9bb12c8109cabd55b5fc2"
+  resolved "https://registry.npmjs.org/postcss-lab-function/-/postcss-lab-function-4.1.2.tgz"
   integrity sha512-isudf5ldhg4fk16M8viAwAbg6Gv14lVO35N3Z/49NhbwPQ2xbiEoHgrRgpgQojosF4vF7jY653ktB6dDrUOR8Q==
   dependencies:
     "@csstools/postcss-progressive-custom-properties" "^1.1.0"
@@ -3707,7 +3373,7 @@ postcss-loader@^6.2.1:
 
 postcss-logical@^5.0.4:
   version "5.0.4"
-  resolved "https://registry.yarnpkg.com/postcss-logical/-/postcss-logical-5.0.4.tgz#ec75b1ee54421acc04d5921576b7d8db6b0e6f73"
+  resolved "https://registry.npmjs.org/postcss-logical/-/postcss-logical-5.0.4.tgz"
   integrity sha512-RHXxplCeLh9VjinvMrZONq7im4wjWGlRJAqmAVLXyZaXwfDWP73/oq4NdIp+OZwhQUMj0zjqDfM5Fj7qby+B4g==
 
 postcss-media-minmax@^5.0.0:
@@ -3797,7 +3463,7 @@ postcss-modules-values@^4.0.0:
 
 postcss-nesting@^10.1.3:
   version "10.1.3"
-  resolved "https://registry.yarnpkg.com/postcss-nesting/-/postcss-nesting-10.1.3.tgz#f0b1cd7ae675c697ab6a5a5ca1feea4784a2ef77"
+  resolved "https://registry.npmjs.org/postcss-nesting/-/postcss-nesting-10.1.3.tgz"
   integrity sha512-wUC+/YCik4wH3StsbC5fBG1s2Z3ZV74vjGqBFYtmYKlVxoio5TYGM06AiaKkQPPlkXWn72HKfS7Cw5PYxnoXSw==
   dependencies:
     postcss-selector-parser "^6.0.9"
@@ -3871,7 +3537,7 @@ postcss-normalize-whitespace@^5.0.1:
 
 postcss-opacity-percentage@^1.1.2:
   version "1.1.2"
-  resolved "https://registry.yarnpkg.com/postcss-opacity-percentage/-/postcss-opacity-percentage-1.1.2.tgz#bd698bb3670a0a27f6d657cc16744b3ebf3b1145"
+  resolved "https://registry.npmjs.org/postcss-opacity-percentage/-/postcss-opacity-percentage-1.1.2.tgz"
   integrity sha512-lyUfF7miG+yewZ8EAk9XUBIlrHyUE6fijnesuz+Mj5zrIHIEw6KcIZSOk/elVMqzLvREmXB83Zi/5QpNRYd47w==
 
 postcss-ordered-values@^5.0.2:
@@ -3884,7 +3550,7 @@ postcss-ordered-values@^5.0.2:
 
 postcss-overflow-shorthand@^3.0.3:
   version "3.0.3"
-  resolved "https://registry.yarnpkg.com/postcss-overflow-shorthand/-/postcss-overflow-shorthand-3.0.3.tgz#ebcfc0483a15bbf1b27fdd9b3c10125372f4cbc2"
+  resolved "https://registry.npmjs.org/postcss-overflow-shorthand/-/postcss-overflow-shorthand-3.0.3.tgz"
   integrity sha512-CxZwoWup9KXzQeeIxtgOciQ00tDtnylYIlJBBODqkgS/PU2jISuWOL/mYLHmZb9ZhZiCaNKsCRiLp22dZUtNsg==
 
 postcss-page-break@^3.0.4:
@@ -3894,14 +3560,14 @@ postcss-page-break@^3.0.4:
 
 postcss-place@^7.0.4:
   version "7.0.4"
-  resolved "https://registry.yarnpkg.com/postcss-place/-/postcss-place-7.0.4.tgz#eb026650b7f769ae57ca4f938c1addd6be2f62c9"
+  resolved "https://registry.npmjs.org/postcss-place/-/postcss-place-7.0.4.tgz"
   integrity sha512-MrgKeiiu5OC/TETQO45kV3npRjOFxEHthsqGtkh3I1rPbZSbXGD/lZVi9j13cYh+NA8PIAPyk6sGjT9QbRyvSg==
   dependencies:
     postcss-value-parser "^4.2.0"
 
 postcss-preset-env@^7.4.3:
   version "7.4.3"
-  resolved "https://registry.yarnpkg.com/postcss-preset-env/-/postcss-preset-env-7.4.3.tgz#fb1c8b4cb405da042da0ddb8c5eda7842c08a449"
+  resolved "https://registry.npmjs.org/postcss-preset-env/-/postcss-preset-env-7.4.3.tgz"
   integrity sha512-dlPA65g9KuGv7YsmGyCKtFkZKCPLkoVMUE3omOl6yM+qrynVHxFvf0tMuippIrXB/sB/MyhL1FgTIbrO+qMERg==
   dependencies:
     "@csstools/postcss-color-function" "^1.0.3"
@@ -3950,7 +3616,7 @@ postcss-preset-env@^7.4.3:
 
 postcss-pseudo-class-any-link@^7.1.1:
   version "7.1.1"
-  resolved "https://registry.yarnpkg.com/postcss-pseudo-class-any-link/-/postcss-pseudo-class-any-link-7.1.1.tgz#534eb1dadd9945eb07830dbcc06fb4d5d865b8e0"
+  resolved "https://registry.npmjs.org/postcss-pseudo-class-any-link/-/postcss-pseudo-class-any-link-7.1.1.tgz"
   integrity sha512-JRoLFvPEX/1YTPxRxp1JO4WxBVXJYrSY7NHeak5LImwJ+VobFMwYDQHvfTXEpcn+7fYIeGkC29zYFhFWIZD8fg==
   dependencies:
     postcss-selector-parser "^6.0.9"
@@ -3983,17 +3649,9 @@ postcss-selector-not@^5.0.0:
   dependencies:
     balanced-match "^1.0.0"
 
-postcss-selector-parser@^6.0.2, postcss-selector-parser@^6.0.4, postcss-selector-parser@^6.0.5:
-  version "6.0.6"
-  resolved "https://registry.npmjs.org/postcss-selector-parser/-/postcss-selector-parser-6.0.6.tgz"
-  integrity sha512-9LXrvaaX3+mcv5xkg5kFwqSzSH1JIObIx51PrndZwlmznwXRfxMddDvo9gve3gVR8ZTKgoFDdWkbRFmEhT4PMg==
-  dependencies:
-    cssesc "^3.0.0"
-    util-deprecate "^1.0.2"
-
-postcss-selector-parser@^6.0.9:
+postcss-selector-parser@^6.0.2, postcss-selector-parser@^6.0.4, postcss-selector-parser@^6.0.5, postcss-selector-parser@^6.0.9:
   version "6.0.10"
-  resolved "https://registry.yarnpkg.com/postcss-selector-parser/-/postcss-selector-parser-6.0.10.tgz#79b61e2c0d1bfc2602d549e11d0876256f8df88d"
+  resolved "https://registry.npmjs.org/postcss-selector-parser/-/postcss-selector-parser-6.0.10.tgz"
   integrity sha512-IQ7TZdoaqbT+LCpShg46jnZVlhWD2w6iQYAcYXfHARZ7X1t/UGhhceQDs5X0cGqKvYlHNOuv7Oa1xmb0oQuA3w==
   dependencies:
     cssesc "^3.0.0"
@@ -4020,27 +3678,18 @@ postcss-value-parser@^4.0.2, postcss-value-parser@^4.1.0, postcss-value-parser@^
   resolved "https://registry.npmjs.org/postcss-value-parser/-/postcss-value-parser-4.2.0.tgz"
   integrity sha512-1NNCs6uurfkVbeXG4S8JFT9t19m45ICnif8zWLd5oPSZ50QnwMfK+H3jv408d4jw/7Bttv5axS5IiHoLaVNHeQ==
 
-postcss@^8.3.5:
-  version "8.4.5"
-  resolved "https://registry.npmjs.org/postcss/-/postcss-8.4.5.tgz"
-  integrity sha512-jBDboWM8qpaqwkMwItqTQTiFikhs/67OYVvblFFTM7MrZjt6yMKd6r2kgXizEbTTljacm4NldIlZnhbjr84QYg==
-  dependencies:
-    nanoid "^3.1.30"
-    picocolors "^1.0.0"
-    source-map-js "^1.0.1"
-
-postcss@^8.4.7:
-  version "8.4.12"
-  resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.4.12.tgz#1e7de78733b28970fa4743f7da6f3763648b1905"
-  integrity sha512-lg6eITwYe9v6Hr5CncVbK70SoioNQIq81nsaG86ev5hAidQvmOeETBqs7jm43K2F5/Ley3ytDtriImV6TpNiSg==
+"postcss@^7.0.0 || ^8.0.1", postcss@^8, postcss@^8.0.0, postcss@^8.0.2, postcss@^8.0.3, postcss@^8.0.9, postcss@^8.1.0, postcss@^8.1.2, postcss@^8.2.15, postcss@^8.2.2, postcss@^8.3, postcss@^8.3.5, postcss@^8.4, postcss@^8.4.6, postcss@^8.4.7:
+  version "8.5.6"
+  resolved "https://registry.npmjs.org/postcss/-/postcss-8.5.6.tgz"
+  integrity sha512-3Ybi1tAuwAP9s0r1UQ2J4n5Y0G05bJkpUIO0/bI9MhwmD70S5aTWbXGBwxHrelT+XM1k6dM0pk+SwNkpTRN7Pg==
   dependencies:
-    nanoid "^3.3.1"
-    picocolors "^1.0.0"
-    source-map-js "^1.0.2"
+    nanoid "^3.3.11"
+    picocolors "^1.1.1"
+    source-map-js "^1.2.1"
 
 preact@^10.7.0:
   version "10.7.0"
-  resolved "https://registry.yarnpkg.com/preact/-/preact-10.7.0.tgz#3bd424677a894c8199f66a881df283b44bea0eeb"
+  resolved "https://registry.npmjs.org/preact/-/preact-10.7.0.tgz"
   integrity sha512-9MEURwzNMKpAil/t6+wabDIJI6oG6GnwypYxiJDvQnW+fHDTt51PYuLZ1QUM31hFr7sDaj9qTaShAF9VIxuxGQ==
 
 process-nextick-args@~2.0.0:
@@ -4057,7 +3706,7 @@ prop-types@^15.7.2:
     object-assign "^4.1.1"
     react-is "^16.8.1"
 
-proxy-addr@~2.0.5:
+proxy-addr@~2.0.7:
   version "2.0.7"
   resolved "https://registry.npmjs.org/proxy-addr/-/proxy-addr-2.0.7.tgz"
   integrity sha512-llQsMLSUDUPT44jdrU/O37qlnifitDP+ZwrmmZcoSKyLKvtZxpyV0n2/bD/N4tBAAZ/gJEdZU7KMraoK1+XYAg==
@@ -4070,15 +3719,12 @@ punycode@^2.1.0:
   resolved "https://registry.npmjs.org/punycode/-/punycode-2.1.1.tgz"
   integrity sha512-XRsRjdf+j5ml+y/6GKHPZbrF/8p2Yga0JPtdqTIY2Xe5ohJPD9saDJJLPvp9+NSBprVvevdXZybnj2cv8OEd0A==
 
-qs@6.7.0:
-  version "6.7.0"
-  resolved "https://registry.npmjs.org/qs/-/qs-6.7.0.tgz"
-  integrity sha512-VCdBRNFTX1fyE7Nb6FYoURo/SPe62QCaAyzJvUjwRaIsc+NePBEniHlvxFmmX56+HZphIGtV0XeCirBtpDrTyQ==
-
-queue-microtask@^1.2.2:
-  version "1.2.3"
-  resolved "https://registry.npmjs.org/queue-microtask/-/queue-microtask-1.2.3.tgz"
-  integrity sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==
+qs@6.13.0:
+  version "6.13.0"
+  resolved "https://registry.npmjs.org/qs/-/qs-6.13.0.tgz"
+  integrity sha512-+38qI9SOr8tfZ4QmJNplMUxqjbe7LKvvZgWdExBOmd+egZTtjLB67Gu0HRX3u/XOq7UU2Nx6nsjvS16Z9uwfpg==
+  dependencies:
+    side-channel "^1.0.6"
 
 randombytes@^2.1.0:
   version "2.1.0"
@@ -4092,19 +3738,19 @@ range-parser@^1.2.1, range-parser@~1.2.1:
   resolved "https://registry.npmjs.org/range-parser/-/range-parser-1.2.1.tgz"
   integrity sha512-Hrgsx+orqoygnmhFbKaHE6c296J+HTAQXoxEF6gNupROmmGJRoyzfG3ccAveqCBrwr/2yxQ5BVd/GTl5agOwSg==
 
-raw-body@2.4.0:
-  version "2.4.0"
-  resolved "https://registry.npmjs.org/raw-body/-/raw-body-2.4.0.tgz"
-  integrity sha512-4Oz8DUIwdvoa5qMJelxipzi/iJIi40O5cGV1wNYp5hvZP8ZN0T+jiNkL0QepXs+EsQ9XJ8ipEDoiH70ySUJP3Q==
+raw-body@2.5.2:
+  version "2.5.2"
+  resolved "https://registry.npmjs.org/raw-body/-/raw-body-2.5.2.tgz"
+  integrity sha512-8zGqypfENjCIqGhgXToC8aB2r7YrBX+AQAfIPs/Mlk+BtPTztOvTS01NRW/3Eh60J+a48lt8qsCzirQ6loCVfA==
   dependencies:
-    bytes "3.1.0"
-    http-errors "1.7.2"
+    bytes "3.1.2"
+    http-errors "2.0.0"
     iconv-lite "0.4.24"
     unpipe "1.0.0"
 
-react-dom@17:
+react-dom@>=16.8.0, react-dom@17:
   version "17.0.2"
-  resolved "https://registry.yarnpkg.com/react-dom/-/react-dom-17.0.2.tgz#ecffb6845e3ad8dbfcdc498f0d0a939736502c23"
+  resolved "https://registry.npmjs.org/react-dom/-/react-dom-17.0.2.tgz"
   integrity sha512-s4h96KtLDUQlsENhMn1ar8t2bEa+q/YAtj8pPPdIjPDGBDIVNsrD9aXNWqspUe6AzKCIG0C1HZZLqLV7qpOBGA==
   dependencies:
     loose-envify "^1.1.0"
@@ -4121,9 +3767,9 @@ react-is@^17.0.2:
   resolved "https://registry.npmjs.org/react-is/-/react-is-17.0.2.tgz"
   integrity sha512-w2GsyukL62IJnlaff/nRegPQR94C/XXamvMWmSHRJ4y7Ts/4ocGRmTHvOs8PSE6pB3dWOrD/nueuU5sduBsQ4w==
 
-react@17:
+"react@^16.8.0 || ^17.0.0 || ^18.0.0", react@>=16.12.0, react@>=16.8.0, react@17, react@17.0.2:
   version "17.0.2"
-  resolved "https://registry.yarnpkg.com/react/-/react-17.0.2.tgz#d0b5cc516d29eb3eee383f75b62864cfb6800037"
+  resolved "https://registry.npmjs.org/react/-/react-17.0.2.tgz"
   integrity sha512-gnhPt75i/dq/z3/6q/0asP78D0u592D5L1pd7M8P+dck6Fu/jJeL6iVVK23fptSUZj8Vjf++7wXA8UNclGQcbA==
   dependencies:
     loose-envify "^1.1.0"
@@ -4167,28 +3813,16 @@ rechoir@^0.7.0:
 
 regenerate-unicode-properties@^10.0.1:
   version "10.0.1"
-  resolved "https://registry.yarnpkg.com/regenerate-unicode-properties/-/regenerate-unicode-properties-10.0.1.tgz#7f442732aa7934a3740c779bb9b3340dccc1fb56"
+  resolved "https://registry.npmjs.org/regenerate-unicode-properties/-/regenerate-unicode-properties-10.0.1.tgz"
   integrity sha512-vn5DU6yg6h8hP/2OkQo3K7uVILvY4iu0oI4t3HFa81UPkhGJwkRwM10JEc3upjdhHjs/k8GJY1sRBhk5sr69Bw==
   dependencies:
     regenerate "^1.4.2"
 
-regenerate-unicode-properties@^9.0.0:
-  version "9.0.0"
-  resolved "https://registry.npmjs.org/regenerate-unicode-properties/-/regenerate-unicode-properties-9.0.0.tgz"
-  integrity sha512-3E12UeNSPfjrgwjkR81m5J7Aw/T55Tu7nUyZVQYCKEOs+2dkxEY+DpPtZzO4YruuiPb7NkYLVcyJC4+zCbk5pA==
-  dependencies:
-    regenerate "^1.4.2"
-
 regenerate@^1.4.2:
   version "1.4.2"
   resolved "https://registry.npmjs.org/regenerate/-/regenerate-1.4.2.tgz"
   integrity sha512-zrceR/XhGYU/d/opr2EKO7aRHUeiBI8qjtfHqADTwZd6Szfy16la6kqD0MIUs5z5hx6AaKa+PixpPrR289+I0A==
 
-regenerator-runtime@^0.13.4:
-  version "0.13.9"
-  resolved "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.13.9.tgz"
-  integrity sha512-p3VT+cOEgxFsRRA9X4lkI1E+k2/CtnKtU4gcxyaCUreilL/vqI6CdZ3wxVUx3UOUg+gnUOQQcRI7BmSI656MYA==
-
 regenerator-transform@^0.14.2:
   version "0.14.5"
   resolved "https://registry.npmjs.org/regenerator-transform/-/regenerator-transform-0.14.5.tgz"
@@ -4196,29 +3830,9 @@ regenerator-transform@^0.14.2:
   dependencies:
     "@babel/runtime" "^7.8.4"
 
-regexp.prototype.flags@^1.2.0:
-  version "1.3.1"
-  resolved "https://registry.npmjs.org/regexp.prototype.flags/-/regexp.prototype.flags-1.3.1.tgz"
-  integrity sha512-JiBdRBq91WlY7uRJ0ds7R+dU02i6LKi8r3BuQhNXn+kmeLN+EfHhfjqMRis1zJxnlu88hq/4dx0P2OP3APRTOA==
-  dependencies:
-    call-bind "^1.0.2"
-    define-properties "^1.1.3"
-
-regexpu-core@^4.7.1:
-  version "4.8.0"
-  resolved "https://registry.npmjs.org/regexpu-core/-/regexpu-core-4.8.0.tgz"
-  integrity sha512-1F6bYsoYiz6is+oz70NWur2Vlh9KWtswuRuzJOfeYUrfPX2o8n74AnUVaOGDbUqVGO9fNHu48/pjJO4sNVwsOg==
-  dependencies:
-    regenerate "^1.4.2"
-    regenerate-unicode-properties "^9.0.0"
-    regjsgen "^0.5.2"
-    regjsparser "^0.7.0"
-    unicode-match-property-ecmascript "^2.0.0"
-    unicode-match-property-value-ecmascript "^2.0.0"
-
 regexpu-core@^5.0.1:
   version "5.0.1"
-  resolved "https://registry.yarnpkg.com/regexpu-core/-/regexpu-core-5.0.1.tgz#c531122a7840de743dcf9c83e923b5560323ced3"
+  resolved "https://registry.npmjs.org/regexpu-core/-/regexpu-core-5.0.1.tgz"
   integrity sha512-CriEZlrKK9VJw/xQGJpQM5rY88BtuL8DM+AEwvcThHilbxiTAy8vq4iJnd2tqq8wLmjbGZzP7ZcKFjbGkmEFrw==
   dependencies:
     regenerate "^1.4.2"
@@ -4228,26 +3842,14 @@ regexpu-core@^5.0.1:
     unicode-match-property-ecmascript "^2.0.0"
     unicode-match-property-value-ecmascript "^2.0.0"
 
-regjsgen@^0.5.2:
-  version "0.5.2"
-  resolved "https://registry.npmjs.org/regjsgen/-/regjsgen-0.5.2.tgz"
-  integrity sha512-OFFT3MfrH90xIW8OOSyUrk6QHD5E9JOTeGodiJeBS3J6IwlgzJMNE/1bZklWz5oTg+9dCMyEetclvCVXOPoN3A==
-
 regjsgen@^0.6.0:
   version "0.6.0"
-  resolved "https://registry.yarnpkg.com/regjsgen/-/regjsgen-0.6.0.tgz#83414c5354afd7d6627b16af5f10f41c4e71808d"
+  resolved "https://registry.npmjs.org/regjsgen/-/regjsgen-0.6.0.tgz"
   integrity sha512-ozE883Uigtqj3bx7OhL1KNbCzGyW2NQZPl6Hs09WTvCuZD5sTI4JY58bkbQWa/Y9hxIsvJ3M8Nbf7j54IqeZbA==
 
-regjsparser@^0.7.0:
-  version "0.7.0"
-  resolved "https://registry.npmjs.org/regjsparser/-/regjsparser-0.7.0.tgz"
-  integrity sha512-A4pcaORqmNMDVwUjWoTzuhwMGpP+NykpfqAsEgI1FSH/EzC7lrN5TMd+kN8YCovX+jMpu8eaqXgXPCa0g8FQNQ==
-  dependencies:
-    jsesc "~0.5.0"
-
 regjsparser@^0.8.2:
   version "0.8.4"
-  resolved "https://registry.yarnpkg.com/regjsparser/-/regjsparser-0.8.4.tgz#8a14285ffcc5de78c5b95d62bbf413b6bc132d5f"
+  resolved "https://registry.npmjs.org/regjsparser/-/regjsparser-0.8.4.tgz"
   integrity sha512-J3LABycON/VNEu3abOviqGHuB/LOtOQj8SKmfP9anY5GfAVw/SPjwzSjxGjbZXIxbGfqTHtJw58C2Li/WkStmA==
   dependencies:
     jsesc "~0.5.0"
@@ -4260,7 +3862,7 @@ require-from-string@^2.0.2:
 requires-port@^1.0.0:
   version "1.0.0"
   resolved "https://registry.npmjs.org/requires-port/-/requires-port-1.0.0.tgz"
-  integrity sha1-kl0mAdOaxIXgkc8NpcbmlNw9yv8=
+  integrity sha1-kl0mAdOaxIXgkc8NpcbmlNw9yv8= sha512-KigOCHcocU3XODJxsu8i/j8T9tzT4adHiecwORRQ0ZZFcp7ahwXuRU1m+yuO90C5ZUyGeGfocHDI14M3L3yDAQ==
 
 resolve-cwd@^3.0.0:
   version "3.0.0"
@@ -4292,26 +3894,12 @@ retry@^0.13.1:
   resolved "https://registry.npmjs.org/retry/-/retry-0.13.1.tgz"
   integrity sha512-XQBQ3I8W1Cge0Seh+6gjj03LbmRFWuoszgK9ooCpwYIrhhoO80pfq4cUkU5DkknwfOfFteRwlZ56PYOGYyFWdg==
 
-reusify@^1.0.4:
-  version "1.0.4"
-  resolved "https://registry.npmjs.org/reusify/-/reusify-1.0.4.tgz"
-  integrity sha512-U9nH88a3fc/ekCF1l0/UP1IosiuIjyTh7hBvXVMHYgVcfGvt897Xguj2UOLDeI5BG2m7/uwyaLVT6fbtCwTyzw==
-
-rimraf@^3.0.2:
-  version "3.0.2"
-  resolved "https://registry.npmjs.org/rimraf/-/rimraf-3.0.2.tgz"
-  integrity sha512-JZkJMZkAGFFPP2YqXZXPbMlMBgsxzE8ILs4lMIX/2o0L9UBw9O/Y3o6wFw/i9YLapcUJWwqbi3kdxIPdC62TIA==
-  dependencies:
-    glob "^7.1.3"
-
-run-parallel@^1.1.9:
-  version "1.2.0"
-  resolved "https://registry.npmjs.org/run-parallel/-/run-parallel-1.2.0.tgz"
-  integrity sha512-5l4VyZR86LZ/lDxZTR6jqL8AFE2S0IFLMP26AbjsLVADxHdhB/c0GUsH+y39UfCi3dzz8OlQuPmnaJOMoDHQBA==
-  dependencies:
-    queue-microtask "^1.2.2"
+run-applescript@^7.0.0:
+  version "7.1.0"
+  resolved "https://registry.npmjs.org/run-applescript/-/run-applescript-7.1.0.tgz"
+  integrity sha512-DPe5pVFaAsinSaV6QjQ6gdiedWDcRCbUuiQfQa2wmWV7+xC9bGulGI8+TdRmoFkAPaBXk8CrAbnlY2ISniJ47Q==
 
-safe-buffer@5.1.2, safe-buffer@>=5.1.0, safe-buffer@^5.0.1, safe-buffer@^5.1.0, safe-buffer@~5.1.0, safe-buffer@~5.1.1:
+safe-buffer@^5.1.0, safe-buffer@>=5.1.0, safe-buffer@~5.1.0, safe-buffer@~5.1.1:
   version "5.1.2"
   resolved "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz"
   integrity sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g==
@@ -4321,6 +3909,11 @@ safe-buffer@~5.2.0:
   resolved "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.2.1.tgz"
   integrity sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==
 
+safe-buffer@5.2.1:
+  version "5.2.1"
+  resolved "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.2.1.tgz"
+  integrity sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==
+
 "safer-buffer@>= 2.1.2 < 3":
   version "2.1.2"
   resolved "https://registry.npmjs.org/safer-buffer/-/safer-buffer-2.1.2.tgz"
@@ -4328,7 +3921,7 @@ safe-buffer@~5.2.0:
 
 scheduler@^0.20.2:
   version "0.20.2"
-  resolved "https://registry.yarnpkg.com/scheduler/-/scheduler-0.20.2.tgz#4baee39436e34aa93b4874bddcbf0fe8b8b50e91"
+  resolved "https://registry.npmjs.org/scheduler/-/scheduler-0.20.2.tgz"
   integrity sha512-2eWfGgAqqWFGqtdMmcL5zCMK1U8KlXv8SQFGglL3CEtd0aDVDWgeF/YoCmvln55m5zSk3J/20hTaSBeSObsQDQ==
   dependencies:
     loose-envify "^1.1.0"
@@ -4343,15 +3936,6 @@ schema-utils@^2.6.5:
     ajv "^6.12.4"
     ajv-keywords "^3.5.2"
 
-schema-utils@^3.1.0, schema-utils@^3.1.1:
-  version "3.1.1"
-  resolved "https://registry.npmjs.org/schema-utils/-/schema-utils-3.1.1.tgz"
-  integrity sha512-Y5PQxS4ITlC+EahLuXaY86TXfR7Dc5lw294alXOq86JAHCihAIZfqv8nNCWvaEJvaC51uN9hbLGeV0cFBdH+Fw==
-  dependencies:
-    "@types/json-schema" "^7.0.8"
-    ajv "^6.12.5"
-    ajv-keywords "^3.5.2"
-
 schema-utils@^4.0.0:
   version "4.0.0"
   resolved "https://registry.npmjs.org/schema-utils/-/schema-utils-4.0.0.tgz"
@@ -4362,65 +3946,89 @@ schema-utils@^4.0.0:
     ajv-formats "^2.1.1"
     ajv-keywords "^5.0.0"
 
+schema-utils@^4.2.0:
+  version "4.3.3"
+  resolved "https://registry.npmjs.org/schema-utils/-/schema-utils-4.3.3.tgz"
+  integrity sha512-eflK8wEtyOE6+hsaRVPxvUKYCpRgzLqDTb8krvAsRIwOGlHoSgYLgBXoubGgLd2fT41/OUYdb48v4k4WWHQurA==
+  dependencies:
+    "@types/json-schema" "^7.0.9"
+    ajv "^8.9.0"
+    ajv-formats "^2.1.1"
+    ajv-keywords "^5.1.0"
+
+schema-utils@^4.3.0:
+  version "4.3.3"
+  resolved "https://registry.npmjs.org/schema-utils/-/schema-utils-4.3.3.tgz"
+  integrity sha512-eflK8wEtyOE6+hsaRVPxvUKYCpRgzLqDTb8krvAsRIwOGlHoSgYLgBXoubGgLd2fT41/OUYdb48v4k4WWHQurA==
+  dependencies:
+    "@types/json-schema" "^7.0.9"
+    ajv "^8.9.0"
+    ajv-formats "^2.1.1"
+    ajv-keywords "^5.1.0"
+
+schema-utils@^4.3.3:
+  version "4.3.3"
+  resolved "https://registry.npmjs.org/schema-utils/-/schema-utils-4.3.3.tgz"
+  integrity sha512-eflK8wEtyOE6+hsaRVPxvUKYCpRgzLqDTb8krvAsRIwOGlHoSgYLgBXoubGgLd2fT41/OUYdb48v4k4WWHQurA==
+  dependencies:
+    "@types/json-schema" "^7.0.9"
+    ajv "^8.9.0"
+    ajv-formats "^2.1.1"
+    ajv-keywords "^5.1.0"
+
 select-hose@^2.0.0:
   version "2.0.0"
   resolved "https://registry.npmjs.org/select-hose/-/select-hose-2.0.0.tgz"
-  integrity sha1-Yl2GWPhlr0Psliv8N2o3NZpJlMo=
+  integrity sha1-Yl2GWPhlr0Psliv8N2o3NZpJlMo= sha512-mEugaLK+YfkijB4fx0e6kImuJdCIt2LxCRcbEYPqRGCs4F2ogyfZU5IAZRdjCP8JPq2AtdNoC/Dux63d9Kiryg==
 
-selfsigned@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.npmjs.org/selfsigned/-/selfsigned-2.0.0.tgz"
-  integrity sha512-cUdFiCbKoa1mZ6osuJs2uDHrs0k0oprsKveFiiaBKCNq3SYyb5gs2HxhQyDNLCmL51ZZThqi4YNDpCK6GOP1iQ==
+selfsigned@^2.4.1:
+  version "2.4.1"
+  resolved "https://registry.npmjs.org/selfsigned/-/selfsigned-2.4.1.tgz"
+  integrity sha512-th5B4L2U+eGLq1TVh7zNRGBapioSORUeymIydxgFpwww9d2qyKvtuPU2jJuHvYAwwqi2Y596QBL3eEqcPEYL8Q==
   dependencies:
-    node-forge "^1.2.0"
-
-semver@7.0.0:
-  version "7.0.0"
-  resolved "https://registry.npmjs.org/semver/-/semver-7.0.0.tgz"
-  integrity sha512-+GB6zVA9LWh6zovYQLALHwv5rb2PHGlJi3lfiqIHxR0uuwCgefcOJc59v9fv1w8GbStwxuuqqAjI9NMAOOgq1A==
+    "@types/node-forge" "^1.3.0"
+    node-forge "^1"
 
 semver@^6.0.0, semver@^6.1.1, semver@^6.1.2, semver@^6.3.0:
-  version "6.3.0"
-  resolved "https://registry.npmjs.org/semver/-/semver-6.3.0.tgz"
-  integrity sha512-b39TBaTSfV6yBrapU89p5fKekE2m/NwnDocOVruQFS1/veMgdzuPcnOM34M6CwxW8jH/lxEa5rBoDeUwu5HHTw==
+  version "6.3.1"
+  resolved "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz"
+  integrity sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==
 
 semver@^7.3.5:
-  version "7.3.5"
-  resolved "https://registry.npmjs.org/semver/-/semver-7.3.5.tgz"
-  integrity sha512-PoeGJYh8HK4BTO/a9Tf6ZG3veo/A7ZVsYrSA6J8ny9nb3B1VrpkuN+z9OE5wfE5p6H4LchYZsegiQgbJD94ZFQ==
-  dependencies:
-    lru-cache "^6.0.0"
+  version "7.7.3"
+  resolved "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz"
+  integrity sha512-SdsKMrI9TdgjdweUSR9MweHA4EJ8YxHn8DFaDisvhVlUOe4BF1tLD7GAj0lIqWVl+dPb/rExr0Btby5loQm20Q==
 
-send@0.17.1:
-  version "0.17.1"
-  resolved "https://registry.npmjs.org/send/-/send-0.17.1.tgz"
-  integrity sha512-BsVKsiGcQMFwT8UxypobUKyv7irCNRHk1T0G680vk88yf6LBByGcZJOTJCrTP2xVN6yI+XjPJcNuE3V4fT9sAg==
+send@0.19.0:
+  version "0.19.0"
+  resolved "https://registry.npmjs.org/send/-/send-0.19.0.tgz"
+  integrity sha512-dW41u5VfLXu8SJh5bwRmyYUbAoSB3c9uQh6L8h/KtsFREPWpbX1lrljJo186Jc4nmci/sGUZ9a0a0J2zgfq2hw==
   dependencies:
     debug "2.6.9"
-    depd "~1.1.2"
-    destroy "~1.0.4"
+    depd "2.0.0"
+    destroy "1.2.0"
     encodeurl "~1.0.2"
     escape-html "~1.0.3"
     etag "~1.8.1"
     fresh "0.5.2"
-    http-errors "~1.7.2"
+    http-errors "2.0.0"
     mime "1.6.0"
-    ms "2.1.1"
-    on-finished "~2.3.0"
+    ms "2.1.3"
+    on-finished "2.4.1"
     range-parser "~1.2.1"
-    statuses "~1.5.0"
+    statuses "2.0.1"
 
-serialize-javascript@^6.0.0:
-  version "6.0.0"
-  resolved "https://registry.npmjs.org/serialize-javascript/-/serialize-javascript-6.0.0.tgz"
-  integrity sha512-Qr3TosvguFt8ePWqsvRfrKyQXIiW+nGbYpy8XK24NQHE83caxWt+mIymTT19DGFbNWNLfEwsrkSmN64lVWB9ag==
+serialize-javascript@^6.0.0, serialize-javascript@^6.0.2:
+  version "6.0.2"
+  resolved "https://registry.npmjs.org/serialize-javascript/-/serialize-javascript-6.0.2.tgz"
+  integrity sha512-Saa1xPByTTq2gdeFZYLLo+RFE35NHZkAbqZeWNd3BpzppeVisAqpDjcp8dyf6uIvEqJRd46jemmyA4iFIeVk8g==
   dependencies:
     randombytes "^2.1.0"
 
 serve-index@^1.9.1:
   version "1.9.1"
   resolved "https://registry.npmjs.org/serve-index/-/serve-index-1.9.1.tgz"
-  integrity sha1-03aNabHn2C5c4FD/9bRTvqEqkjk=
+  integrity sha1-03aNabHn2C5c4FD/9bRTvqEqkjk= sha512-pXHfKNP4qujrtteMrSBb0rc8HJ9Ms/GrXwcUtUtD5s4ewDJI8bT3Cz2zTVRMKtri49pLx2e0Ya8ziP5Ya2pZZw==
   dependencies:
     accepts "~1.3.4"
     batch "0.6.1"
@@ -4430,25 +4038,25 @@ serve-index@^1.9.1:
     mime-types "~2.1.17"
     parseurl "~1.3.2"
 
-serve-static@1.14.1:
-  version "1.14.1"
-  resolved "https://registry.npmjs.org/serve-static/-/serve-static-1.14.1.tgz"
-  integrity sha512-JMrvUwE54emCYWlTI+hGrGv5I8dEwmco/00EvkzIIsR7MqrHonbD9pO2MOfFnpFntl7ecpZs+3mW+XbQZu9QCg==
+serve-static@1.16.2:
+  version "1.16.2"
+  resolved "https://registry.npmjs.org/serve-static/-/serve-static-1.16.2.tgz"
+  integrity sha512-VqpjJZKadQB/PEbEwvFdO43Ax5dFBZ2UECszz8bQ7pi7wt//PWe1P6MN7eCnjsatYtBT6EuiClbjSWP2WrIoTw==
   dependencies:
-    encodeurl "~1.0.2"
+    encodeurl "~2.0.0"
     escape-html "~1.0.3"
     parseurl "~1.3.3"
-    send "0.17.1"
+    send "0.19.0"
 
 setprototypeof@1.1.0:
   version "1.1.0"
   resolved "https://registry.npmjs.org/setprototypeof/-/setprototypeof-1.1.0.tgz"
   integrity sha512-BvE/TwpZX4FXExxOxZyRGQQv651MSwmWKZGqvmPcRIjDqWub67kTKuIMx43cZZrS/cBBzwBcNDWoFxt2XEFIpQ==
 
-setprototypeof@1.1.1:
-  version "1.1.1"
-  resolved "https://registry.npmjs.org/setprototypeof/-/setprototypeof-1.1.1.tgz"
-  integrity sha512-JvdAWfbXeIGaZ9cILp38HntZSFSo3mWg6xGcJJsd+d4aRMOqauag1C63dJfDw7OaMYwEbHMOxEZ1lqVRYP2OAw==
+setprototypeof@1.2.0:
+  version "1.2.0"
+  resolved "https://registry.npmjs.org/setprototypeof/-/setprototypeof-1.2.0.tgz"
+  integrity sha512-E5LDX7Wrp85Kil5bhZv46j8jOeboKq5JMmYM3gVGdGH8xFpPWXUMsNrlODCrkoxMEeNi/XZIwuRvY4XNwYMJpw==
 
 shallow-clone@^3.0.0:
   version "3.0.1"
@@ -4469,17 +4077,57 @@ shebang-regex@^3.0.0:
   resolved "https://registry.npmjs.org/shebang-regex/-/shebang-regex-3.0.0.tgz"
   integrity sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A==
 
+shell-quote@^1.8.3:
+  version "1.8.3"
+  resolved "https://registry.npmjs.org/shell-quote/-/shell-quote-1.8.3.tgz"
+  integrity sha512-ObmnIF4hXNg1BqhnHmgbDETF8dLPCggZWBjkQfhZpbszZnYur5DUljTcCHii5LC3J5E0yeO/1LIMyH+UvHQgyw==
+
+side-channel-list@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.npmjs.org/side-channel-list/-/side-channel-list-1.0.0.tgz"
+  integrity sha512-FCLHtRD/gnpCiCHEiJLOwdmFP+wzCmDEkc9y7NsYxeF4u7Btsn1ZuwgwJGxImImHicJArLP4R0yX4c2KCrMrTA==
+  dependencies:
+    es-errors "^1.3.0"
+    object-inspect "^1.13.3"
+
+side-channel-map@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.npmjs.org/side-channel-map/-/side-channel-map-1.0.1.tgz"
+  integrity sha512-VCjCNfgMsby3tTdo02nbjtM/ewra6jPHmpThenkTYh8pG9ucZ/1P8So4u4FGBek/BjpOVsDCMoLA/iuBKIFXRA==
+  dependencies:
+    call-bound "^1.0.2"
+    es-errors "^1.3.0"
+    get-intrinsic "^1.2.5"
+    object-inspect "^1.13.3"
+
+side-channel-weakmap@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.npmjs.org/side-channel-weakmap/-/side-channel-weakmap-1.0.2.tgz"
+  integrity sha512-WPS/HvHQTYnHisLo9McqBHOJk2FkHO/tlpvldyrnem4aeQp4hai3gythswg6p01oSoTl58rcpiFAjF2br2Ak2A==
+  dependencies:
+    call-bound "^1.0.2"
+    es-errors "^1.3.0"
+    get-intrinsic "^1.2.5"
+    object-inspect "^1.13.3"
+    side-channel-map "^1.0.1"
+
+side-channel@^1.0.6:
+  version "1.1.0"
+  resolved "https://registry.npmjs.org/side-channel/-/side-channel-1.1.0.tgz"
+  integrity sha512-ZX99e6tRweoUXqR+VBrslhda51Nh5MTQwou5tnUDgbtyM0dBgmhEDtWGP/xbKn6hqfPRHujUNwz5fy/wbbhnpw==
+  dependencies:
+    es-errors "^1.3.0"
+    object-inspect "^1.13.3"
+    side-channel-list "^1.0.0"
+    side-channel-map "^1.0.1"
+    side-channel-weakmap "^1.0.2"
+
 signal-exit@^3.0.3:
   version "3.0.6"
   resolved "https://registry.npmjs.org/signal-exit/-/signal-exit-3.0.6.tgz"
   integrity sha512-sDl4qMFpijcGw22U5w63KmD3cZJfBuFlVNbVMKje2keoKML7X2UzWbc4XrmEbDwg0NXJc3yv4/ox7b+JWb57kQ==
 
-slash@^3.0.0:
-  version "3.0.0"
-  resolved "https://registry.npmjs.org/slash/-/slash-3.0.0.tgz"
-  integrity sha512-g9Q1haeby36OSStwb4ntCGGGaKsaVSjQ68fBxoQcutl5fS1vuY18H3wSt3jFyFtrkx+Kz0V1G85A4MyAdDMi2Q==
-
-sockjs@^0.3.21:
+sockjs@^0.3.24:
   version "0.3.24"
   resolved "https://registry.npmjs.org/sockjs/-/sockjs-0.3.24.tgz"
   integrity sha512-GJgLTZ7vYb/JtPSSZ10hsOYIvEYsjbNU+zPdIHcUaWVNUEPivzxku31865sSSud0Da0W4lEeOPlmw93zLQchuQ==
@@ -4488,15 +4136,10 @@ sockjs@^0.3.21:
     uuid "^8.3.2"
     websocket-driver "^0.7.4"
 
-source-map-js@^1.0.1:
-  version "1.0.1"
-  resolved "https://registry.npmjs.org/source-map-js/-/source-map-js-1.0.1.tgz"
-  integrity sha512-4+TN2b3tqOCd/kaGRJ/sTYA0tR0mdXx26ipdolxcwtJVqEnqNYvlCAt1q3ypy4QMlYus+Zh34RNtYLoq2oQ4IA==
-
-source-map-js@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/source-map-js/-/source-map-js-1.0.2.tgz#adbc361d9c62df380125e7f161f71c826f1e490c"
-  integrity sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==
+source-map-js@^1.2.1:
+  version "1.2.1"
+  resolved "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz"
+  integrity sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==
 
 source-map-support@~0.5.20:
   version "0.5.21"
@@ -4506,20 +4149,15 @@ source-map-support@~0.5.20:
     buffer-from "^1.0.0"
     source-map "^0.6.0"
 
-source-map@^0.5.0:
-  version "0.5.7"
-  resolved "https://registry.npmjs.org/source-map/-/source-map-0.5.7.tgz"
-  integrity sha1-igOdLRAh0i0eoUyA2OpGi6LvP8w=
-
-source-map@^0.6.0, source-map@^0.6.1:
+source-map@^0.6.0:
   version "0.6.1"
   resolved "https://registry.npmjs.org/source-map/-/source-map-0.6.1.tgz"
   integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
 
-source-map@~0.7.2:
-  version "0.7.3"
-  resolved "https://registry.npmjs.org/source-map/-/source-map-0.7.3.tgz"
-  integrity sha512-CkCj6giN3S+n9qrYiBTX5gystlENnRW5jZeNLHpe6aue+SrHcG5VYwujhW9s4dY31mEGsxBDrHR6oI69fTXsaQ==
+source-map@^0.6.1:
+  version "0.6.1"
+  resolved "https://registry.npmjs.org/source-map/-/source-map-0.6.1.tgz"
+  integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
 
 spdy-transport@^3.0.0:
   version "3.0.0"
@@ -4549,10 +4187,15 @@ stable@^0.1.8:
   resolved "https://registry.npmjs.org/stable/-/stable-0.1.8.tgz"
   integrity sha512-ji9qxRnOVfcuLDySj9qzhGSEFVobyt1kIOSkj1qZzYLzq7Tos/oUUWvotUPQLlrsidqsK6tBH89Bc9kL5zHA6w==
 
-"statuses@>= 1.4.0 < 2", "statuses@>= 1.5.0 < 2", statuses@~1.5.0:
+"statuses@>= 1.4.0 < 2":
   version "1.5.0"
   resolved "https://registry.npmjs.org/statuses/-/statuses-1.5.0.tgz"
-  integrity sha1-Fhx9rBd2Wf2YEfQ3cfqZOBR4Yow=
+  integrity sha1-Fhx9rBd2Wf2YEfQ3cfqZOBR4Yow= sha512-OpZ3zP+jT1PI7I8nemJX4AKmAX070ZkYPVWV/AaKTJl+tXCTGyVdC1a4SL8RUQYEwk/f34ZX8UTykN68FwrqAA==
+
+statuses@2.0.1:
+  version "2.0.1"
+  resolved "https://registry.npmjs.org/statuses/-/statuses-2.0.1.tgz"
+  integrity sha512-RwNA9Z/7PrK06rYLIzFMlaF+l73iwpzsqRIFgbMLbTcLD6cOao82TaWefPXQvB2fOC4AjuYSEndS7N/mTCbkdQ==
 
 string_decoder@^1.1.1:
   version "1.3.0"
@@ -4568,13 +4211,6 @@ string_decoder@~1.1.1:
   dependencies:
     safe-buffer "~5.1.0"
 
-strip-ansi@^7.0.0:
-  version "7.0.1"
-  resolved "https://registry.npmjs.org/strip-ansi/-/strip-ansi-7.0.1.tgz"
-  integrity sha512-cXNxvT8dFNRVfhVME3JAe98mkXDYN2O1l7jmcwMnOslDeESg1rF/OZMtK0nRAhiari1unG5cD4jG3rapUAkLbw==
-  dependencies:
-    ansi-regex "^6.0.1"
-
 strip-final-newline@^2.0.0:
   version "2.0.0"
   resolved "https://registry.npmjs.org/strip-final-newline/-/strip-final-newline-2.0.0.tgz"
@@ -4593,13 +4229,6 @@ stylehacks@^5.0.1:
     browserslist "^4.16.0"
     postcss-selector-parser "^6.0.4"
 
-supports-color@^5.3.0:
-  version "5.5.0"
-  resolved "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz"
-  integrity sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==
-  dependencies:
-    has-flag "^3.0.0"
-
 supports-color@^8.0.0:
   version "8.1.1"
   resolved "https://registry.npmjs.org/supports-color/-/supports-color-8.1.1.tgz"
@@ -4620,42 +4249,37 @@ svgo@^2.7.0:
     picocolors "^1.0.0"
     stable "^0.1.8"
 
-tapable@^2.1.1, tapable@^2.2.0:
-  version "2.2.1"
-  resolved "https://registry.npmjs.org/tapable/-/tapable-2.2.1.tgz"
-  integrity sha512-GNzQvQTOIP6RyTfE2Qxb8ZVlNmw0n88vp1szwWRimP02mnTsx3Wtn5qRdqY9w2XduFNUgvOwhNnQsjwCp+kqaQ==
-
-terser-webpack-plugin@^5.1.3:
-  version "5.2.5"
-  resolved "https://registry.npmjs.org/terser-webpack-plugin/-/terser-webpack-plugin-5.2.5.tgz"
-  integrity sha512-3luOVHku5l0QBeYS8r4CdHYWEGMmIj3H1U64jgkdZzECcSOJAyJ9TjuqcQZvw1Y+4AOBN9SeYJPJmFn2cM4/2g==
-  dependencies:
-    jest-worker "^27.0.6"
-    schema-utils "^3.1.1"
-    serialize-javascript "^6.0.0"
-    source-map "^0.6.1"
-    terser "^5.7.2"
+tapable@^2.2.0, tapable@^2.3.0:
+  version "2.3.0"
+  resolved "https://registry.npmjs.org/tapable/-/tapable-2.3.0.tgz"
+  integrity sha512-g9ljZiwki/LfxmQADO3dEY1CbpmXT5Hm2fJ+QaGKwSXUylMybePR7/67YW7jOrrvjEgL1Fmz5kzyAjWVWLlucg==
 
-terser-webpack-plugin@^5.3.1:
-  version "5.3.1"
-  resolved "https://registry.yarnpkg.com/terser-webpack-plugin/-/terser-webpack-plugin-5.3.1.tgz#0320dcc270ad5372c1e8993fabbd927929773e54"
-  integrity sha512-GvlZdT6wPQKbDNW/GDQzZFg/j4vKU96yl2q6mcUkzKOgW4gwf1Z8cZToUCrz31XHlPWH8MVb1r2tFtdDtTGJ7g==
+terser-webpack-plugin@^5.3.1, terser-webpack-plugin@^5.3.11:
+  version "5.3.14"
+  resolved "https://registry.npmjs.org/terser-webpack-plugin/-/terser-webpack-plugin-5.3.14.tgz"
+  integrity sha512-vkZjpUjb6OMS7dhV+tILUW6BhpDR7P2L/aQSAv+Uwk+m8KATX9EccViHTJR2qDtACKPIYndLGCyl3FMo+r2LMw==
   dependencies:
+    "@jridgewell/trace-mapping" "^0.3.25"
     jest-worker "^27.4.5"
-    schema-utils "^3.1.1"
-    serialize-javascript "^6.0.0"
-    source-map "^0.6.1"
-    terser "^5.7.2"
+    schema-utils "^4.3.0"
+    serialize-javascript "^6.0.2"
+    terser "^5.31.1"
 
-terser@^5.7.2:
-  version "5.10.0"
-  resolved "https://registry.npmjs.org/terser/-/terser-5.10.0.tgz"
-  integrity sha512-AMmF99DMfEDiRJfxfY5jj5wNH/bYO09cniSqhfoyxc8sFoYIgkJy86G04UoZU5VjlpnplVu0K6Tx6E9b5+DlHA==
+terser@^5.31.1:
+  version "5.44.0"
+  resolved "https://registry.npmjs.org/terser/-/terser-5.44.0.tgz"
+  integrity sha512-nIVck8DK+GM/0Frwd+nIhZ84pR/BX7rmXMfYwyg+Sri5oGVE99/E3KvXqpC2xHFxyqXyGHTKBSioxxplrO4I4w==
   dependencies:
+    "@jridgewell/source-map" "^0.3.3"
+    acorn "^8.15.0"
     commander "^2.20.0"
-    source-map "~0.7.2"
     source-map-support "~0.5.20"
 
+thingies@^2.5.0:
+  version "2.5.0"
+  resolved "https://registry.npmjs.org/thingies/-/thingies-2.5.0.tgz"
+  integrity sha512-s+2Bwztg6PhWUD7XMfeYm5qliDdSiZm7M7n8KjTkIsm3l/2lgVRc2/Gx/v+ZX8lT4FMA+i8aQvhcWylldc+ZNw==
+
 thunky@^1.0.2:
   version "1.1.0"
   resolved "https://registry.npmjs.org/thunky/-/thunky-1.1.0.tgz"
@@ -4664,12 +4288,7 @@ thunky@^1.0.2:
 timsort@^0.3.0:
   version "0.3.0"
   resolved "https://registry.npmjs.org/timsort/-/timsort-0.3.0.tgz"
-  integrity sha1-QFQRqOfmM5/mTbmiNN4R3DHgK9Q=
-
-to-fast-properties@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.npmjs.org/to-fast-properties/-/to-fast-properties-2.0.0.tgz"
-  integrity sha1-3F5pjL0HkmW8c+A3doGk5Og/YW4=
+  integrity sha1-QFQRqOfmM5/mTbmiNN4R3DHgK9Q= sha512-qsdtZH+vMoCARQtyod4imc2nIJwg9Cc7lPRrw9CzF8ZKR0khdr8+2nX80PBhET3tcyTtJDxAffGh2rXH4tyU8A==
 
 to-regex-range@^5.0.1:
   version "5.0.1"
@@ -4678,22 +4297,32 @@ to-regex-range@^5.0.1:
   dependencies:
     is-number "^7.0.0"
 
-toidentifier@1.0.0:
-  version "1.0.0"
-  resolved "https://registry.npmjs.org/toidentifier/-/toidentifier-1.0.0.tgz"
-  integrity sha512-yaOH/Pk/VEhBWWTlhI+qXxDFXlejDGcQipMlyxda9nthulaxLZUNcUqFxokp0vcYnvteJln5FNQDRrxj3YcbVw==
+toidentifier@1.0.1:
+  version "1.0.1"
+  resolved "https://registry.npmjs.org/toidentifier/-/toidentifier-1.0.1.tgz"
+  integrity sha512-o5sSPKEkg/DIQNmH43V0/uerLrpzVedkUh8tGNvaeXpfpuwjKenlSox/2O/BTlZUtEe+JG7s5YhEz608PlAHRA==
+
+tree-dump@^1.0.3, tree-dump@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.npmjs.org/tree-dump/-/tree-dump-1.1.0.tgz"
+  integrity sha512-rMuvhU4MCDbcbnleZTFezWsaZXRFemSqAM+7jPnzUl1fo9w3YEKOxAeui0fz3OI4EU4hf23iyA7uQRVko+UaBA==
 
 tslib@^1.0.0:
   version "1.14.1"
-  resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
+  resolved "https://registry.npmjs.org/tslib/-/tslib-1.14.1.tgz"
   integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
 
+tslib@^2, tslib@^2.0.0, tslib@2:
+  version "2.8.1"
+  resolved "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz"
+  integrity sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==
+
 tslib@^2.3.0:
   version "2.3.1"
   resolved "https://registry.npmjs.org/tslib/-/tslib-2.3.1.tgz"
   integrity sha512-77EbyPPpMz+FRFRuAFlWMtmgUWGe9UOG2Z25NqCwiIjRhOf5iKGuzSe5P2w1laq+FkRy4p+PCuVkJSGkzTEKVw==
 
-type-is@~1.6.17, type-is@~1.6.18:
+type-is@~1.6.18:
   version "1.6.18"
   resolved "https://registry.npmjs.org/type-is/-/type-is-1.6.18.tgz"
   integrity sha512-TkRKr9sUTxEH8MdfuCSP7VizJyzRNMjj2J2do2Jr3Kym598JVdEksuzPQCnlFPW4ky9Q+iA+ma9BGm06XQBy8g==
@@ -4724,10 +4353,18 @@ unicode-property-aliases-ecmascript@^2.0.0:
   resolved "https://registry.npmjs.org/unicode-property-aliases-ecmascript/-/unicode-property-aliases-ecmascript-2.0.0.tgz"
   integrity sha512-5Zfuy9q/DFr4tfO7ZPeVXb1aPoeQSdeFMLpYuFebehDAhbuevLs5yxSZmIFN1tP5F9Wl4IpJrYojg85/zgyZHQ==
 
-unpipe@1.0.0, unpipe@~1.0.0:
+unpipe@~1.0.0, unpipe@1.0.0:
   version "1.0.0"
   resolved "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz"
-  integrity sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw=
+  integrity sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw= sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==
+
+update-browserslist-db@^1.1.3:
+  version "1.1.3"
+  resolved "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.1.3.tgz"
+  integrity sha512-UxhIZQ+QInVdunkDAaiazvvT/+fXL5Osr0JZlJulepYu6Jd7qJtDZjlur0emRlT71EN3ScPoE7gvsuIKKNavKw==
+  dependencies:
+    escalade "^3.2.0"
+    picocolors "^1.1.1"
 
 uri-js@^4.2.2:
   version "4.4.1"
@@ -4738,18 +4375,18 @@ uri-js@^4.2.2:
 
 use-isomorphic-layout-effect@^1.1.1:
   version "1.1.2"
-  resolved "https://registry.yarnpkg.com/use-isomorphic-layout-effect/-/use-isomorphic-layout-effect-1.1.2.tgz#497cefb13d863d687b08477d9e5a164ad8c1a6fb"
+  resolved "https://registry.npmjs.org/use-isomorphic-layout-effect/-/use-isomorphic-layout-effect-1.1.2.tgz"
   integrity sha512-49L8yCO3iGT/ZF9QttjwLF/ZD9Iwto5LnH5LmEdk/6cFmXddqi2ulF0edxTwjj+7mqvpVVGQWvbXZdn32wRSHA==
 
 util-deprecate@^1.0.1, util-deprecate@^1.0.2, util-deprecate@~1.0.1:
   version "1.0.2"
   resolved "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz"
-  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
+  integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8= sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==
 
 utils-merge@1.0.1:
   version "1.0.1"
   resolved "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz"
-  integrity sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM=
+  integrity sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM= sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==
 
 uuid@^8.3.2:
   version "8.3.2"
@@ -4759,12 +4396,12 @@ uuid@^8.3.2:
 vary@~1.1.2:
   version "1.1.2"
   resolved "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz"
-  integrity sha1-IpnwLG3tMNSllhsLn3RSShj2NPw=
+  integrity sha1-IpnwLG3tMNSllhsLn3RSShj2NPw= sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg==
 
-watchpack@^2.3.1:
-  version "2.3.1"
-  resolved "https://registry.npmjs.org/watchpack/-/watchpack-2.3.1.tgz"
-  integrity sha512-x0t0JuydIo8qCNctdDrn1OzH/qDzk2+rdCOC3YzumZ42fiMqmQ7T3xQurykYMhYfHaPHTp4ZxAx2NfUo1K6QaA==
+watchpack@^2.4.4:
+  version "2.4.4"
+  resolved "https://registry.npmjs.org/watchpack/-/watchpack-2.4.4.tgz"
+  integrity sha512-c5EGNOiyxxV5qmTtAB7rbiXxi1ooX1pQKMLX/MIabJjRA0SJBQOjKF+KSVfHkr9U1cADPon0mRiVe/riyaiDUA==
   dependencies:
     glob-to-regexp "^0.4.1"
     graceful-fs "^4.1.2"
@@ -4776,9 +4413,9 @@ wbuf@^1.1.0, wbuf@^1.7.3:
   dependencies:
     minimalistic-assert "^1.0.0"
 
-webpack-cli@^4.9.2:
+webpack-cli@^4.9.2, webpack-cli@4.x.x:
   version "4.9.2"
-  resolved "https://registry.yarnpkg.com/webpack-cli/-/webpack-cli-4.9.2.tgz#77c1adaea020c3f9e2db8aad8ea78d235c83659d"
+  resolved "https://registry.npmjs.org/webpack-cli/-/webpack-cli-4.9.2.tgz"
   integrity sha512-m3/AACnBBzK/kMTcxWHcZFPrw/eQuY4Df1TxvIWfWM2x7mRqBQCqKEd96oCUa9jkapLBaFfRce33eGDb4Pr7YQ==
   dependencies:
     "@discoveryjs/json-ext" "^0.5.0"
@@ -4794,52 +4431,51 @@ webpack-cli@^4.9.2:
     rechoir "^0.7.0"
     webpack-merge "^5.7.3"
 
-webpack-dev-middleware@^5.3.1:
-  version "5.3.1"
-  resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-5.3.1.tgz#aa079a8dedd7e58bfeab358a9af7dab304cee57f"
-  integrity sha512-81EujCKkyles2wphtdrnPg/QqegC/AtqNH//mQkBYSMqwFVCQrxM6ktB2O/SPlZy7LqeEfTbV3cZARGQz6umhg==
+webpack-dev-middleware@^7.4.2:
+  version "7.4.5"
+  resolved "https://registry.npmjs.org/webpack-dev-middleware/-/webpack-dev-middleware-7.4.5.tgz"
+  integrity sha512-uxQ6YqGdE4hgDKNf7hUiPXOdtkXvBJXrfEGYSx7P7LC8hnUYGK70X6xQXUvXeNyBDDcsiQXpG2m3G9vxowaEuA==
   dependencies:
     colorette "^2.0.10"
-    memfs "^3.4.1"
-    mime-types "^2.1.31"
+    memfs "^4.43.1"
+    mime-types "^3.0.1"
+    on-finished "^2.4.1"
     range-parser "^1.2.1"
     schema-utils "^4.0.0"
 
-webpack-dev-server@^4.7.4:
-  version "4.7.4"
-  resolved "https://registry.yarnpkg.com/webpack-dev-server/-/webpack-dev-server-4.7.4.tgz#d0ef7da78224578384e795ac228d8efb63d5f945"
-  integrity sha512-nfdsb02Zi2qzkNmgtZjkrMOcXnYZ6FLKcQwpxT7MvmHKc+oTtDsBju8j+NMyAygZ9GW1jMEUpy3itHtqgEhe1A==
-  dependencies:
-    "@types/bonjour" "^3.5.9"
-    "@types/connect-history-api-fallback" "^1.3.5"
-    "@types/express" "^4.17.13"
-    "@types/serve-index" "^1.9.1"
-    "@types/sockjs" "^0.3.33"
-    "@types/ws" "^8.2.2"
+webpack-dev-server@^5.2.2:
+  version "5.2.2"
+  resolved "https://registry.npmjs.org/webpack-dev-server/-/webpack-dev-server-5.2.2.tgz"
+  integrity sha512-QcQ72gh8a+7JO63TAx/6XZf/CWhgMzu5m0QirvPfGvptOusAxG12w2+aua1Jkjr7hzaWDnJ2n6JFeexMHI+Zjg==
+  dependencies:
+    "@types/bonjour" "^3.5.13"
+    "@types/connect-history-api-fallback" "^1.5.4"
+    "@types/express" "^4.17.21"
+    "@types/express-serve-static-core" "^4.17.21"
+    "@types/serve-index" "^1.9.4"
+    "@types/serve-static" "^1.15.5"
+    "@types/sockjs" "^0.3.36"
+    "@types/ws" "^8.5.10"
     ansi-html-community "^0.0.8"
-    bonjour "^3.5.0"
-    chokidar "^3.5.3"
+    bonjour-service "^1.2.1"
+    chokidar "^3.6.0"
     colorette "^2.0.10"
     compression "^1.7.4"
-    connect-history-api-fallback "^1.6.0"
-    default-gateway "^6.0.3"
-    del "^6.0.0"
-    express "^4.17.1"
+    connect-history-api-fallback "^2.0.0"
+    express "^4.21.2"
     graceful-fs "^4.2.6"
-    html-entities "^2.3.2"
-    http-proxy-middleware "^2.0.0"
-    ipaddr.js "^2.0.1"
-    open "^8.0.9"
-    p-retry "^4.5.0"
-    portfinder "^1.0.28"
-    schema-utils "^4.0.0"
-    selfsigned "^2.0.0"
+    http-proxy-middleware "^2.0.9"
+    ipaddr.js "^2.1.0"
+    launch-editor "^2.6.1"
+    open "^10.0.3"
+    p-retry "^6.2.0"
+    schema-utils "^4.2.0"
+    selfsigned "^2.4.1"
     serve-index "^1.9.1"
-    sockjs "^0.3.21"
+    sockjs "^0.3.24"
     spdy "^4.0.2"
-    strip-ansi "^7.0.0"
-    webpack-dev-middleware "^5.3.1"
-    ws "^8.4.2"
+    webpack-dev-middleware "^7.4.2"
+    ws "^8.18.0"
 
 webpack-merge@^5.7.3:
   version "5.8.0"
@@ -4849,42 +4485,43 @@ webpack-merge@^5.7.3:
     clone-deep "^4.0.1"
     wildcard "^2.0.0"
 
-webpack-sources@^3.2.3:
-  version "3.2.3"
-  resolved "https://registry.yarnpkg.com/webpack-sources/-/webpack-sources-3.2.3.tgz#2d4daab8451fd4b240cc27055ff6a0c2ccea0cde"
-  integrity sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w==
-
-webpack@^5.70.0:
-  version "5.71.0"
-  resolved "https://registry.yarnpkg.com/webpack/-/webpack-5.71.0.tgz#b01fcf379570b8c5ee06ca06c829ca168c951884"
-  integrity sha512-g4dFT7CFG8LY0iU5G8nBL6VlkT21Z7dcYDpJAEJV5Q1WLb9UwnFbrem1k7K52ILqEmomN7pnzWFxxE6SlDY56A==
-  dependencies:
-    "@types/eslint-scope" "^3.7.3"
-    "@types/estree" "^0.0.51"
-    "@webassemblyjs/ast" "1.11.1"
-    "@webassemblyjs/wasm-edit" "1.11.1"
-    "@webassemblyjs/wasm-parser" "1.11.1"
-    acorn "^8.4.1"
-    acorn-import-assertions "^1.7.6"
-    browserslist "^4.14.5"
+webpack-sources@^3.3.3:
+  version "3.3.3"
+  resolved "https://registry.npmjs.org/webpack-sources/-/webpack-sources-3.3.3.tgz"
+  integrity sha512-yd1RBzSGanHkitROoPFd6qsrxt+oFhg/129YzheDGqeustzX0vTZJZsSsQjVQC4yzBQ56K55XU8gaNCtIzOnTg==
+
+webpack@^5.0.0, webpack@^5.1.0, webpack@^5.70.0, webpack@>=2, "webpack@4.x.x || 5.x.x":
+  version "5.102.1"
+  resolved "https://registry.npmjs.org/webpack/-/webpack-5.102.1.tgz"
+  integrity sha512-7h/weGm9d/ywQ6qzJ+Xy+r9n/3qgp/thalBbpOi5i223dPXKi04IBtqPN9nTd+jBc7QKfvDbaBnFipYp4sJAUQ==
+  dependencies:
+    "@types/eslint-scope" "^3.7.7"
+    "@types/estree" "^1.0.8"
+    "@types/json-schema" "^7.0.15"
+    "@webassemblyjs/ast" "^1.14.1"
+    "@webassemblyjs/wasm-edit" "^1.14.1"
+    "@webassemblyjs/wasm-parser" "^1.14.1"
+    acorn "^8.15.0"
+    acorn-import-phases "^1.0.3"
+    browserslist "^4.26.3"
     chrome-trace-event "^1.0.2"
-    enhanced-resolve "^5.9.2"
-    es-module-lexer "^0.9.0"
+    enhanced-resolve "^5.17.3"
+    es-module-lexer "^1.2.1"
     eslint-scope "5.1.1"
     events "^3.2.0"
     glob-to-regexp "^0.4.1"
-    graceful-fs "^4.2.9"
-    json-parse-better-errors "^1.0.2"
+    graceful-fs "^4.2.11"
+    json-parse-even-better-errors "^2.3.1"
     loader-runner "^4.2.0"
     mime-types "^2.1.27"
     neo-async "^2.6.2"
-    schema-utils "^3.1.0"
-    tapable "^2.1.1"
-    terser-webpack-plugin "^5.1.3"
-    watchpack "^2.3.1"
-    webpack-sources "^3.2.3"
+    schema-utils "^4.3.3"
+    tapable "^2.3.0"
+    terser-webpack-plugin "^5.3.11"
+    watchpack "^2.4.4"
+    webpack-sources "^3.3.3"
 
-websocket-driver@>=0.5.1, websocket-driver@^0.7.4:
+websocket-driver@^0.7.4, websocket-driver@>=0.5.1:
   version "0.7.4"
   resolved "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz"
   integrity sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg==
@@ -4910,20 +4547,17 @@ wildcard@^2.0.0:
   resolved "https://registry.npmjs.org/wildcard/-/wildcard-2.0.0.tgz"
   integrity sha512-JcKqAHLPxcdb9KM49dufGXn2x3ssnfjbcaQdLlfZsL9rH9wgDQjUtDxbo8NE0F6SFvydeu1VhZe7hZuHsB2/pw==
 
-wrappy@1:
-  version "1.0.2"
-  resolved "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz"
-  integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
-
-ws@^8.4.2:
-  version "8.5.0"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.5.0.tgz#bfb4be96600757fe5382de12c670dab984a1ed4f"
-  integrity sha512-BWX0SWVgLPzYwF8lTzEy1egjhS4S4OEAHfsO8o65WOVsrnSRGaSiUaa9e0ggGlkMTtBlmOpEXiie9RUcBO86qg==
+ws@^8.18.0:
+  version "8.18.3"
+  resolved "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz"
+  integrity sha512-PEIGCY5tSlUt50cqyMXfCzX+oOPqN0vuGqWzbcJ2xvnkzkq46oOpz7dQaTDBdfICb4N14+GARUDw2XV2N4tvzg==
 
-yallist@^4.0.0:
-  version "4.0.0"
-  resolved "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz"
-  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
+wsl-utils@^0.1.0:
+  version "0.1.0"
+  resolved "https://registry.npmjs.org/wsl-utils/-/wsl-utils-0.1.0.tgz"
+  integrity sha512-h3Fbisa2nKGPxCpm89Hk33lBLsnaGBvctQopaBSOW/uIs6FTe1ATyAnKFJrzVs9vpGdsTe73WF3V4lIsk4Gacw==
+  dependencies:
+    is-wsl "^3.1.0"
 
 yaml@^1.10.0, yaml@^1.10.2:
   version "1.10.2"
diff --git a/docs/assets/plugins/jquery-scrollTo/package.json b/docs/assets/plugins/jquery-scrollTo/package.json
index dcf5c5041e7428..6ace57faa322eb 100644
--- a/docs/assets/plugins/jquery-scrollTo/package.json
+++ b/docs/assets/plugins/jquery-scrollTo/package.json
@@ -5,7 +5,7 @@
   "main": "jquery.scrollTo.js",
   "license": "MIT",
   "ignore": ["**/.*","demo","tests","CHANGELOG","README.md","composer.json","bower.json"],
-  "dependencies": { "jquery": ">=1.8" },
+  "dependencies": { "jquery": ">=3.5.0" },
   "homepage": "https://github.com/flesler/jquery.scrollTo/",
   "docs": "https://github.com/flesler/jquery.scrollTo/",
   "demo": "http://demos.flesler.com/jquery/scrollTo/",
diff --git a/docs/assets/plugins/lightbox/package.json b/docs/assets/plugins/lightbox/package.json
index 361c2ecc27800e..1dd99ebbdf862e 100644
--- a/docs/assets/plugins/lightbox/package.json
+++ b/docs/assets/plugins/lightbox/package.json
@@ -26,7 +26,7 @@
 		"url": "https://github.com/ashleydw/lightbox/blob/master/LICENSE.txt"
 	},
 	"devDependencies": {
-		"grunt": "^0.4.5",
+		"grunt": ">=1.5.3",
 		"grunt-banner": "~0.2.0",
 		"grunt-contrib-coffee": "~0.8.0",
 		"grunt-contrib-uglify": "~0.2.2",
diff --git a/docs/en/annotator_entries/Reader2Doc.md b/docs/en/annotator_entries/Reader2Doc.md
new file mode 100644
index 00000000000000..86818f556c1b16
--- /dev/null
+++ b/docs/en/annotator_entries/Reader2Doc.md
@@ -0,0 +1,107 @@
+{%- capture title -%}
+Reader2Doc
+{%- endcapture -%}
+
+{%- capture description -%}
+The Reader2Doc annotator enables seamless integration of document reading capabilities into existing Spark NLP workflows. It allows you to efficiently extract and structure content from a wide range of document types, making it easier to reuse and extend your pipelines without additional preprocessing steps.
+
+Supported File Formats:
+- Text: `.txt`  
+- HTML: `.html`, `.htm`  
+- Microsoft Word: `.doc`, `.docx`  
+- Microsoft Excel: `.xls`, `.xlsx`  
+- Microsoft PowerPoint: `.ppt`, `.pptx`  
+- Email files: `.eml`, `.msg`  
+- PDF documents: `.pdf` 
+
+Parameters:
+- `explodeDocs` : Whether to output one document per row (default: `true`). Set to `false` to combine all content into a single row per input file.  
+- `flattenOutput` : Whether to output plain text with minimal metadata (default: `false`).  
+- `outputAsDocument` : Whether to output data as one single document instead of multiple records (default: `false`).  
+- `excludeNonText` : Whether to exclude non-textual data such as tables and images (default: `false`).  
+
+For an extended example see the
+[example notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/data-preprocessing/SparkNLP_Reader2Doc_Demo.ipynb).
+
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+NONE
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from johnsnowlabs.reader import Reader2Doc
+from johnsnowlabs.nlp.base import DocumentAssembler
+from pyspark.ml import Pipeline
+
+reader2doc = Reader2Doc() \
+    .setContentType("application/pdf") \
+    .setContentPath(f"{pdf_directory}/")
+
+pipeline = Pipeline(stages=[reader2doc])
+
+pipeline_model = pipeline.fit(empty_data_set)
+result_df = pipeline_model.transform(empty_data_set)
+
+result_df.show()
++------------------------------------------------------------------------------------------------------------------------------------+
+|document                                                                                                                            |
++------------------------------------------------------------------------------------------------------------------------------------+
+|[{document, 0, 14, This is a Title, {pageNumber -> 1, elementType -> Title, fileName -> pdf-title.pdf}, []}]                        |
+|[{document, 15, 38, This is a narrative text, {pageNumber -> 1, elementType -> NarrativeText, fileName -> pdf-title.pdf}, []}]      |
+|[{document, 39, 68, This is another narrative text, {pageNumber -> 1, elementType -> NarrativeText, fileName -> pdf-title.pdf}, []}]|
++------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.reader.Reader2Doc
+import com. johnsnowlabs.nlp.base.DocumentAssembler
+import org.apache.spark.ml.Pipeline
+
+val reader2Doc = new Reader2Doc()
+  .setContentType("application/pdf")
+  .setContentPath(s"$pdfDirectory/")
+
+val pipeline = new Pipeline()
+  .setStages(Array(reader2Doc))
+
+val pipelineModel = pipeline.fit(emptyDataSet)
+val resultDf = pipelineModel.transform(emptyDataSet)
+
+resultDf.show()
++------------------------------------------------------------------------------------------------------------------------------------+
+|document                                                                                                                            |
++------------------------------------------------------------------------------------------------------------------------------------+
+|[{document, 0, 14, This is a Title, {pageNumber -> 1, elementType -> Title, fileName -> pdf-title.pdf}, []}]                        |
+|[{document, 15, 38, This is a narrative text, {pageNumber -> 1, elementType -> NarrativeText, fileName -> pdf-title.pdf}, []}]      |
+|[{document, 39, 68, This is another narrative text, {pageNumber -> 1, elementType -> NarrativeText, fileName -> pdf-title.pdf}, []}]|
++------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture api_link -%} 
+[Reader2Doc](/api/com/johnsnowlabs/reader/Reader2Doc.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[Reader2Doc](/api/python/reference/autosummary/sparknlp/reader/reader2doc/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[Reader2Doc](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/reader/Reader2Doc.scala)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/annotator_entries/Reader2Image.md b/docs/en/annotator_entries/Reader2Image.md
new file mode 100644
index 00000000000000..60de36f0627363
--- /dev/null
+++ b/docs/en/annotator_entries/Reader2Image.md
@@ -0,0 +1,113 @@
+{%- capture title -%}
+Reader2Image
+{%- endcapture -%}
+
+{%- capture description -%}
+The Reader2Image annotator enables seamless integration of image reading capabilities into existing Spark NLP workflows. It allows you to efficiently extract and structure image content from both individual image files and documents with embedded images.
+
+With this, you can read image files or extract images from documents such as PDFs, Word, Excel, PowerPoint, HTML, Markdown, and email files. All extracted images are returned as structured Spark DataFrames with associated metadata, ready for downstream processing in Spark NLP pipelines.
+
+Supported File Formats:
+- Image files: `.png`, `.jpg`, `.jpeg`, `.bmp`, `.gif`  
+- Documents with embedded images: `.pdf`, `.doc`, `.docx`, `.ppt`, `.pptx`, `.xls`, `.xlsx`, `.eml`, `.msg`, `.html`, `.htm`, `.md`
+
+Parameters:
+- `contentPath`: Path to the documents or image files to read.  
+- `inputCol`: Input column containing the documents.  
+- `outputCol`: Output column for structured image data (default: `"image"`).  
+- `explodeDocs`: Whether to split multi-document files into separate rows (default: `true`).  
+- `explodeImages`: Whether to output one image per row (default: `true`). Set to `false` to combine multiple images per file into a single record.  
+- `readAsImage`: Whether to read PDF pages as images (default: `true`).  
+- `storeContent`: Whether to store the raw file content alongside structured output (default: `false`).  
+- `flattenOutput`: Whether to return plain image data with minimal metadata (default: `false`).  
+- `outputAsDocument`: Whether to output all extracted images as a single combined document (default: `false`).  
+- `excludeNonImage`: Whether to skip non-image content found in mixed-format files (default: `false`).  
+- `contentType`: MIME type of the documents (e.g., "text/html", "application/pdf").  
+- `userMessage`: Custom message describing the image for prompt-based models (default: `"Describe this image"`).  
+- `promptTemplate`: Format of the output prompt for image models (default: `"qwen2vl-chat"`).  
+- `customPromptTemplate`: Custom prompt template for image models when `promptTemplate` is `"custom"`.
+
+For an extended example see the
+[example notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/data-preprocessing/SparkNLP_Reader2Image_Demo.ipynb).
+
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+NONE
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+IMAGE
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.reader.reader2image import Reader2Image
+from pyspark.ml import Pipeline
+
+reader2Image = Reader2Image() \
+    .setContentType("text/html") \
+    .setContentPath("./example-images.html") \
+    .setOutputCol("image")
+
+pipeline = Pipeline(stages=[reader2Image])
+
+pipelineModel = pipeline.fit(emptyDataSet)
+resultDf = pipelineModel.transform(emptyDataSet)
+
+resultDf.show()
++-------------------+--------------------+
+|           fileName|               image|
++-------------------+--------------------+
+|example-images.html|[{image, example-...|
+|example-images.html|[{image, example-...|
++-------------------+--------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.reader.Reader2Image
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import org.apache.spark.ml.Pipeline
+
+val reader2Image = new Reader2Image()
+  .setContentType("text/html")
+  .setContentPath("./example-images.html")
+  .setOutputCol("image")
+
+val pipeline = new Pipeline().setStages(Array(reader2Image))
+
+val pipelineModel = pipeline.fit(emptyDataSet)
+val resultDf = pipelineModel.transform(emptyDataSet)
+
+resultDf.show()
++-------------------+--------------------+
+|           fileName|               image|
++-------------------+--------------------+
+|example-images.html|[{image, example-...|
+|example-images.html|[{image, example-...|
++-------------------+--------------------+
+{%- endcapture -%}
+
+{%- capture api_link -%} 
+[Reader2Image](/api/com/johnsnowlabs/reader/Reader2Image.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[Reader2Image](/api/python/reference/autosummary/sparknlp/reader/Reader2Image/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[Reader2Image](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/reader/Reader2Image.scala)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/annotator_entries/Reader2Table.md b/docs/en/annotator_entries/Reader2Table.md
new file mode 100644
index 00000000000000..f374b06581bbb4
--- /dev/null
+++ b/docs/en/annotator_entries/Reader2Table.md
@@ -0,0 +1,104 @@
+{%- capture title -%}
+Reader2Table
+{%- endcapture -%}
+
+{%- capture description -%}
+The Reader2Table annotator enables seamless extraction of tabular content from documents within existing Spark NLP workflows. It allows you to efficiently parse tables from a wide variety of file types, including HTML, Word, Excel, PowerPoint, and CSV files, and return them as structured Spark DataFrames with metadata, ready for downstream processing or analysis.
+
+Supported File Formats:
+- HTML: `.html`, `.htm`  
+- Word documents: `.doc`, `.docx`  
+- Excel spreadsheets: `.xls`, `.xlsx`  
+- PowerPoint presentations: `.ppt`, `.pptx`  
+- CSV files: `.csv`  
+
+Parameters:
+- `contentPath`: Path to the input documents or table-containing files (required).  
+- `inputCol`: Input column name (optional).  
+- `outputCol`: Output column name for structured table data (default: `document`).  
+- `contentType`: MIME type of the documents (e.g., `"text/html"`, `"application/vnd.ms-excel"`) (optional).  
+- `explodeDocs`: Whether to split multi-document files into separate rows (default: `true`).  
+- `storeContent`: Whether to include the raw file content alongside structured output (default: `false`).  
+- `flattenOutput`: Whether to return plain table data with minimal metadata (default: `false`).  
+- `inferTableStructure`: Whether to detect and preserve table structure from the input (default: `true`).  
+- `outputFormat`: Format of the extracted table output (`json-table` or `html-table`, default: `json-table`).  
+- `ignoreExceptions`: Whether to ignore exceptions during processing (default: `true`).  
+
+For an extended example see the
+[example notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/data-preprocessing/SparkNLP_Reader2Table_Demo.ipynb).
+
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+NONE
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+TABLE
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.reader.reader2table import Reader2Table
+from pyspark.ml import Pipeline
+
+reader2Table = Reader2Table() \
+    .setContentType("application/csv") \
+    .setContentPath(f"./csvDirectory")
+
+pipeline = Pipeline(stages=[reader2Table])
+
+pipelineModel = pipeline.fit(emptyDataSet)
+resultDf = pipelineModel.transform(emptyDataSet)
+
+resultDf.show()
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|fileName        |document                                                                                                                                                                                    |
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|stanley-cups.csv|[{document, 0, 137, {"caption":"","header":[],"rows":[["Team","Location","Stanley Cups"],["Blues","STL","1"],["Flyers","PHI","2"],["Maple Leafs","TOR","13"]]}, {elementType -> Table}, []}]|
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.reader.Reader2Table
+import org.apache.spark.ml.Pipeline
+
+val reader2Table = new Reader2Table()
+  .setContentType("application/csv")
+  .setContentPath(s"$csvDirectory/")
+
+val pipeline = new Pipeline().setStages(Array(reader2Table))
+
+val pipelineModel = pipeline.fit(emptyDataSet)
+val resultDf = pipelineModel.transform(emptyDataSet)
+
+resultDf.show()
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|fileName        |document                                                                                                                                                                                    |
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|stanley-cups.csv|[{document, 0, 137, {"caption":"","header":[],"rows":[["Team","Location","Stanley Cups"],["Blues","STL","1"],["Flyers","PHI","2"],["Maple Leafs","TOR","13"]]}, {elementType -> Table}, []}]|
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture api_link -%} 
+[Reader2Table](/api/com/johnsnowlabs/reader/Reader2Table.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[Reader2Table](/api/python/reference/autosummary/sparknlp/reader/Reader2Table/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[Reader2Table](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/reader/Reader2Table.scala)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/annotator_entries/ReaderAssembler.md b/docs/en/annotator_entries/ReaderAssembler.md
new file mode 100644
index 00000000000000..a1035c56758867
--- /dev/null
+++ b/docs/en/annotator_entries/ReaderAssembler.md
@@ -0,0 +1,106 @@
+{%- capture title -%}
+ReaderAssembler
+{%- endcapture -%}
+
+{%- capture description -%}
+The ReaderAssembler annotator provides a unified interface to combine multiple Spark NLP readers such as `Reader2Doc`, `Reader2Table`, and `Reader2Image` into a single, configurable component. It automatically selects and orchestrates the appropriate reader for each input based on file type, content type, and configured priorities, allowing you to process heterogeneous content (documents, tables, images) seamlessly in one pipeline.
+
+Supported Input Types:
+- Text: `txt`, `html`, `htm`, `md`, `xml`, `csv`  
+- Documents: `pdf`, `doc`, `docx`, `xls`, `xlsx`, `ppt`, `pptx`  
+- Email: `eml`, `msg`  
+- Images: `png`, `jpg`, `jpeg`, `bmp`, `gif`  
+
+Parameters:
+- `contentPath`: Path to the content source (file or directory).  
+- `inputCol`: Input column name for in-memory string content (optional).  
+- `outputCol`: Base output column name; appends `text`, `table`, `image` for respective reader outputs (default: `document`).  
+- `contentType`: MIME type of the content (e.g., `text/html`, `application/pdf`) (optional).  
+- `explodeDocs`: Whether to split multi-document files into separate rows (default: `false`).  
+- `flattenOutput`: Whether to return plain content with minimal metadata (default: `false`).  
+- `inferTableStructure`: Whether to automatically detect table structure from tabular content (default: `true`).  
+- `excludeNonText`: Whether to ignore non-text rows, such as tables, in Reader2Doc (default: `false`).  
+- `userMessage`: Custom message describing the content for prompt-based models (op—ional).  
+- `promptTemplate`: Template format for prompt generation (optional).  
+- `customPromptTemplate`: Custom prompt template if `promptTemplate` is set to `"custom"` (optional).  
+
+For an extended example see the
+[example notebook](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/data-preprocessing/SparkNLP_ReaderAssembler_Demo.ipynb).
+
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+NONE
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+DOCUMENT, TABLE, IMAGE
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from johnsnowlabs.reader import ReaderAssembler
+from pyspark.ml import Pipeline
+
+reader_assembler = ReaderAssembler() \
+    .setContentType("text/html") \
+    .setContentPath("/table-image.html") \
+    .setOutputCol("document")
+
+pipeline = Pipeline(stages=[reader_assembler])
+
+pipeline_model = pipeline.fit(empty_data_set)
+result_df = pipeline_model.transform(empty_data_set)
+
+result_df.show()
++--------+--------------------+--------------------+--------------------+---------+
+|fileName|       document_text|      document_table|      document_image|exception|
++--------+--------------------+--------------------+--------------------+---------+
+|    null|[{document, 0, 26...|[{document, 0, 50...|[{image, , 5, 5, ...|     null|
++--------+--------------------+--------------------+--------------------+---------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.reader.ReaderAssembler
+import org.apache.spark.ml.Pipeline
+
+val readerAssembler = new ReaderAssembler()
+  .setContentType("text/html")
+  .setContentPath(s"$htmlFilesDirectory/table-image.html")
+  .setOutputCol("document")
+
+val pipeline = new Pipeline().setStages(Array(readerAssembler))
+
+val pipelineModel = pipeline.fit(emptyDataSet)
+val resultDf = pipelineModel.transform(emptyDataSet)
+
+resultDf.show()
++--------+--------------------+--------------------+--------------------+---------+
+|fileName|       document_text|      document_table|      document_image|exception|
++--------+--------------------+--------------------+--------------------+---------+
+|    null|[{document, 0, 26...|[{document, 0, 50...|[{image, , 5, 5, ...|     null|
++--------+--------------------+--------------------+--------------------+---------+
+{%- endcapture -%}
+
+{%- capture api_link -%} 
+[ReaderAssembler](/api/com/johnsnowlabs/reader/ReaderAssembler.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[ReaderAssembler](/api/python/reference/autosummary/sparknlp/reader/reader_assembler/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[ReaderAssembler](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/reader/ReaderAssembler.scala)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/annotators.md b/docs/en/annotators.md
index c7c1681d91eca5..a2bc41ba715a0e 100644
--- a/docs/en/annotators.md
+++ b/docs/en/annotators.md
@@ -82,13 +82,15 @@ There are two types of Annotators:
 {% include templates/anno_table_entry.md path="" name="NerConverter" summary="Converts a IOB or IOB2 representation of NER to a user-friendly one, by associating the tokens of recognized entities and their label."%}
 {% include templates/anno_table_entry.md path="" name="NerCrf" summary="Extracts Named Entities based on a CRF Model."%}
 {% include templates/anno_table_entry.md path="" name="NerDL" summary="This Named Entity recognition annotator is a generic NER model based on Neural Networks."%}
-{% include templates/anno_table_entry.md path="" name="NerDLGraphChecker" summary="Checks whether a suitable NerDL graph is available for the given training dataset, before any
-computations/training is done."%}
 {% include templates/anno_table_entry.md path="" name="NerOverwriter" summary="Overwrites entities of specified strings."%}
 {% include templates/anno_table_entry.md path="" name="Normalizer" summary="Removes all dirty characters from text following a regex pattern and transforms words based on a provided dictionary."%}
 {% include templates/anno_table_entry.md path="" name="NorvigSweeting Spellchecker" summary="Retrieves tokens and makes corrections automatically if not found in an English dictionary."%}
 {% include templates/anno_table_entry.md path="" name="POSTagger (Part of speech tagger)" summary="Averaged Perceptron model to tag words part-of-speech."%}
 {% include templates/anno_table_entry.md path="" name="PromptAssembler" summary="Assembles a sequence of messages into a single string using a template."%}
+{% include templates/anno_table_entry.md path="" name="Reader2Doc" summary="Reads documents from files or directories and outputs structured text for NLP pipelines."%}
+{% include templates/anno_table_entry.md path="" name="Reader2Image" summary="Extracts images from files or documents and outputs them as structured Spark DataFrames with metadata."%}
+{% include templates/anno_table_entry.md path="" name="Reader2Table" summary="Reads tables from documents or CSV/Excel files and outputs structured tabular content for downstream processing."%}
+{% include templates/anno_table_entry.md path="" name="ReaderAssembler" summary="Combines multiple document or message segments into a single unified document for NLP processing."%}
 {% include templates/anno_table_entry.md path="" name="RecursiveTokenizer" summary="Tokenizes raw text recursively based on a handful of definable rules."%}
 {% include templates/anno_table_entry.md path="" name="RegexMatcher" summary="Uses rules to match a set of regular expressions and associate them with a provided identifier."%}
 {% include templates/anno_table_entry.md path="" name="RegexTokenizer" summary="A tokenizer that splits text by a regex pattern."%}
@@ -120,52 +122,83 @@ Additionally, these transformers are available.
 |---|---|---|
 {% include templates/anno_table_entry.md path="./transformers" name="AlbertEmbeddings" summary="ALBERT: A Lite BERT for Self-supervised Learning of Language Representations"%}
 {% include templates/anno_table_entry.md path="./transformers" name="AlbertForQuestionAnswering" summary="AlbertForQuestionAnswering can load ALBERT Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
-{% include templates/anno_table_entry.md path="./transformers" name="AlbertForTokenClassification" summary="AlbertForTokenClassification can load ALBERT Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="AlbertForSequenceClassification" summary="AlbertForSequenceClassification can load ALBERT Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%}
-{% include templates/anno_table_entry.md path="./transformers" name="BartForZeroShotClassification" summary="BartForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language inference) tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="AlbertForTokenClassification" summary="AlbertForTokenClassification can load ALBERT Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="BartForZeroShotClassification" summary="BartForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="BartTransformer" summary="BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer"%}
+{% include templates/anno_table_entry.md path="./transformers" name="BertEmbeddings" summary="BERT (Bidirectional Encoder Representations from Transformers) embeddings for token-level vector representations."%}
 {% include templates/anno_table_entry.md path="./transformers" name="BertForQuestionAnswering" summary="BertForQuestionAnswering can load Bert Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
 {% include templates/anno_table_entry.md path="./transformers" name="BertForSequenceClassification" summary="Bert Models with sequence classification/regression head on top."%}
 {% include templates/anno_table_entry.md path="./transformers" name="BertForTokenClassification" summary="BertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="BertForZeroShotClassification" summary="BertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="BertSentenceEmbeddings" summary="Sentence-level embeddings using BERT. BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture."%}
+{% include templates/anno_table_entry.md path="./transformers" name="BLIPForQuestionAnswering" summary="BLIPForQuestionAnswering loads BLIP models with a visual question answering head, enabling multimodal tasks where the model can answer text questions about images." %}
 {% include templates/anno_table_entry.md path="./transformers" name="CamemBertEmbeddings" summary="CamemBert is based on Facebook's RoBERTa model released in 2019."%}
 {% include templates/anno_table_entry.md path="./transformers" name="CamemBertForQuestionAnswering" summary="CamemBertForQuestionAnswering can load CamemBERT Models with a span classification head on top for extractive question-answering tasks like SQuAD"%}
 {% include templates/anno_table_entry.md path="./transformers" name="CamemBertForSequenceClassification" summary="amemBertForSequenceClassification can load CamemBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="CamemBertForTokenClassification" summary="CamemBertForTokenClassification can load CamemBERT Models with a token classification head on top"%}
 {% include templates/anno_table_entry.md path="./transformers" name="CLIPForZeroShotClassification" summary="Zero Shot Image Classifier based on CLIP"%}
+{% include templates/anno_table_entry.md path="./transformers" name="CoHereTransformer" summary="Integrates with Cohere's large language models for text generation and other NLP tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="ConvNextForImageClassification" summary="ConvNextForImageClassification is an image classifier based on ConvNet models"%}
+{% include templates/anno_table_entry.md path="./transformers" name="CPMTransformer" summary="CPMTransformer provides access to CPM models, large-scale Chinese autoregressive language models designed for natural language understanding and generation." %}
 {% include templates/anno_table_entry.md path="./transformers" name="DeBertaEmbeddings" summary="DeBERTa builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in RoBERTa."%}
 {% include templates/anno_table_entry.md path="./transformers" name="DeBertaForQuestionAnswering" summary="DeBertaForQuestionAnswering can load DeBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
 {% include templates/anno_table_entry.md path="./transformers" name="DeBertaForSequenceClassification" summary="DeBertaForSequenceClassification can load DeBerta v2 & v3 Models with sequence classification/regression head on top."%}
 {% include templates/anno_table_entry.md path="./transformers" name="DeBertaForTokenClassification" summary="DeBertaForTokenClassification can load DeBERTA Models v2 and v3 with a token classification head on top."%}
+{% include templates/anno_table_entry.md path="./transformers" name="DeBertaForZeroShotClassification" summary="DeBertaForZeroShotClassification loads DeBERTa models fine-tuned for zero-shot classification, allowing natural language inference (NLI)-based categorization without task-specific training data." %}
 {% include templates/anno_table_entry.md path="./transformers" name="DistilBertEmbeddings" summary="DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base."%}
 {% include templates/anno_table_entry.md path="./transformers" name="DistilBertForQuestionAnswering" summary="DistilBertForQuestionAnswering can load DistilBert Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
 {% include templates/anno_table_entry.md path="./transformers" name="DistilBertForSequenceClassification" summary="DistilBertForSequenceClassification can load DistilBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="DistilBertForTokenClassification" summary="DistilBertForTokenClassification can load DistilBERT Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="DistilBertForZeroShotClassification" summary="DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="E5Embeddings" summary="Sentence embeddings using E5, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task."%}
-{% include templates/anno_table_entry.md path="./transformers" name="MiniLMEmbeddings" summary="Sentence embeddings using MiniLM, a lightweight and efficient sentence embedding model that can generate text embeddings for various NLP tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="E5VEmbeddings" summary="Provides access to E5-V, a multimodal embedding model for joint text and image representation."%}
 {% include templates/anno_table_entry.md path="./transformers" name="ElmoEmbeddings" summary="Word embeddings from ELMo (Embeddings from Language Models), a language model trained on the 1 Billion Word Benchmark."%}
+{% include templates/anno_table_entry.md path="./transformers" name="Florence2Transformer" summary="Loads Microsoft's Florence-2, a versatile vision foundation model capable of handling a wide range of vision and vision-language tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="Gemma3ForMultiModal" summary="Loads Google's Gemma 3, a state-of-the-art multimodal model for advanced reasoning across text, images, and other data types."%}
 {% include templates/anno_table_entry.md path="./transformers" name="GPT2Transformer" summary="GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages."%}
 {% include templates/anno_table_entry.md path="./transformers" name="HubertForCTC" summary="Hubert Model with a language modeling head on top for Connectionist Temporal Classification (CTC)."%}
 {% include templates/anno_table_entry.md path="./transformers" name="InstructorEmbeddings" summary="Sentence embeddings using INSTRUCTOR."%}
+{% include templates/anno_table_entry.md path="./transformers" name="InternVLForMultiModal" summary="Loads InternVL, a powerful vision-language model designed for large-scale, multimodal understanding and generation."%}
+{% include templates/anno_table_entry.md path="./transformers" name="JanusForMultiModal" summary="Loads Janus, a multimodal model that excels at understanding and generating content across both visual and textual domains."%}
+{% include templates/anno_table_entry.md path="./transformers" name="LLAMA2Transformer" summary="LLAMA2Transformer provides access to the LLaMA 2 family of large language models, optimized for dialogue, reasoning, and text generation across various tasks." %}
+{% include templates/anno_table_entry.md path="./transformers" name="LLAMA3Transformer" summary="LLAMA3Transformer loads Meta’s LLaMA 3 models, state-of-the-art large language models optimized for advanced reasoning, long context, and instruction-following." %}
+{% include templates/anno_table_entry.md path="./transformers" name="LLAVAForMultiModal" summary="Loads LLaVA (Large Language and Vision Assistant), an open-source multimodal model that combines a vision encoder with a large language model."%}
 {% include templates/anno_table_entry.md path="./transformers" name="LongformerEmbeddings" summary="Longformer is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents."%}
 {% include templates/anno_table_entry.md path="./transformers" name="LongformerForQuestionAnswering" summary="LongformerForQuestionAnswering can load Longformer Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
 {% include templates/anno_table_entry.md path="./transformers" name="LongformerForSequenceClassification" summary="LongformerForSequenceClassification can load Longformer Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="LongformerForTokenClassification" summary="LongformerForTokenClassification can load Longformer Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="M2M100Transformer" summary="M2M100Transformer provides access to Facebook’s M2M100 multilingual translation models, capable of translating directly between 100+ languages without relying on English as an intermediate." %}
 {% include templates/anno_table_entry.md path="./transformers" name="MarianTransformer" summary="Marian is an efficient, free Neural Machine Translation framework written in pure C++ with minimal dependencies."%}
+{% include templates/anno_table_entry.md path="./transformers" name="MiniLMEmbeddings" summary="Sentence embeddings using MiniLM, a lightweight and efficient sentence embedding model that can generate text embeddings for various NLP tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="MistralTransformer" summary="MistralTransformer loads Mistral models, efficient dense and mixture-of-experts (MoE) language models optimized for high performance on reasoning and coding tasks." %}
+{% include templates/anno_table_entry.md path="./transformers" name="MLLamaForMultimodal" summary="MLLamaForMultimodal is an open-source multimodal model that combines a vision encoder with a large language model."%}
 {% include templates/anno_table_entry.md path="./transformers" name="MPNetEmbeddings" summary="Sentence embeddings using MPNet."%}
 {% include templates/anno_table_entry.md path="./transformers" name="MPNetForQuestionAnswering" summary="MPNet Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
 {% include templates/anno_table_entry.md path="./transformers" name="MPNetForSequenceClassification" summary="MPNet Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="MPNetForTokenClassification" summary="MPNetForTokenClassification loads MPNet models with a token classification head for sequence labeling tasks such as Named Entity Recognition (NER)." %}
+{% include templates/anno_table_entry.md path="./transformers" name="MxbaiEmbeddings" summary="MxbaiEmbeddings provides access to embedding models from Mxbai, optimized for retrieval, semantic search, and representation learning." %}
+{% include templates/anno_table_entry.md path="./transformers" name="NLLBTransformer" summary="NLLBTransformer loads the No Language Left Behind (NLLB) models, designed to support translation across 200+ languages with high accuracy." %}
+{% include templates/anno_table_entry.md path="./transformers" name="NomicEmbeddings" summary="NomicEmbeddings provides access to Nomic’s embedding models, designed for scalable semantic search, clustering, and retrieval tasks." %}
+{% include templates/anno_table_entry.md path="./transformers" name="OLMoTransformer" summary="Provides access to AI2's OLMo, a truly open language model designed for building and studying large language models."%}
 {% include templates/anno_table_entry.md path="./transformers" name="OpenAICompletion" summary="Transformer that makes a request for OpenAI Completion API for each executor."%}
+{% include templates/anno_table_entry.md path="./transformers" name="PaliGemmaForMultiModal" summary="Loads PaliGemma, Google's versatile vision-language model based on Gemma and SigLIP, designed for image captioning, VQA, and other multimodal tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="Phi2Transformer" summary="Phi2Transformer loads Microsoft’s Phi-2 small language model, optimized for reasoning, coding, and efficient NLP tasks with limited compute requirements." %}
+{% include templates/anno_table_entry.md path="./transformers" name="Phi3Transformer" summary="Phi3Transformer provides access to Microsoft’s Phi-3 family of small language models, supporting long contexts and optimized for instruction following, reasoning, and code generation." %}
+{% include templates/anno_table_entry.md path="./transformers" name="Phi3Vision" summary="Loads Microsoft’s Phi-3-Vision, a lightweight, high-performance multimodal model that brings language and vision capabilities to resource-constrained environments."%}
+{% include templates/anno_table_entry.md path="./transformers" name="Phi4Transformer" summary="Provides access to Microsoft's Phi-4 family of powerful and efficient small language models."%}
+{% include templates/anno_table_entry.md path="./transformers" name="Qwen2VLTransformer" summary="Loads Alibaba’s Qwen2-VL models, a family of powerful multimodal large language models for advanced vision-language tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="QwenTransformer" summary="QwenTransformer loads Alibaba’s Qwen models, a family of large language models optimized for advanced reasoning, dialogue, and multilingual NLP tasks." %}
 {% include templates/anno_table_entry.md path="./transformers" name="RoBertaEmbeddings" summary="RoBERTa: A Robustly Optimized BERT Pretraining Approach"%}
 {% include templates/anno_table_entry.md path="./transformers" name="RoBertaForQuestionAnswering" summary="RoBertaForQuestionAnswering can load RoBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
 {% include templates/anno_table_entry.md path="./transformers" name="RoBertaForSequenceClassification" summary="RoBertaForSequenceClassification can load RoBERTa Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="RoBertaForTokenClassification" summary="RoBertaForTokenClassification can load RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
-{% include templates/anno_table_entry.md path="./transformers" name="RoBertaForZeroShotClassification" summary="RoBertaForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language inference) tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="RoBertaForZeroShotClassification" summary="RoBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="RoBertaSentenceEmbeddings" summary="Sentence-level embeddings using RoBERTa."%}
+{% include templates/anno_table_entry.md path="./transformers" name="SmolVLMTransformer" summary="Loads Smol-VLM, a family of small yet powerful vision language models designed for efficient multimodal reasoning."%}
+{% include templates/anno_table_entry.md path="./transformers" name="SnowFlakeEmbeddings" summary="SnowFlakeEmbeddings provides access to Snowflake’s embedding models, built for enterprise-scale retrieval-augmented generation (RAG) and semantic search." %}
 {% include templates/anno_table_entry.md path="./transformers" name="SpanBertCoref" summary="A coreference resolution model based on SpanBert."%}
+{% include templates/anno_table_entry.md path="./transformers" name="StarCoderTransformer" summary="StarCoderTransformer loads StarCoder models, code generation models trained on permissively licensed data, optimized for program synthesis, completion, and chat-based coding assistance." %}
 {% include templates/anno_table_entry.md path="./transformers" name="SwinForImageClassification" summary="SwinImageClassification is an image classifier based on Swin."%}
 {% include templates/anno_table_entry.md path="./transformers" name="T5Transformer" summary="T5 reconsiders all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input."%}
 {% include templates/anno_table_entry.md path="./transformers" name="TapasForQuestionAnswering" summary="TapasForQuestionAnswering is an implementation of TaPas - a BERT-based model specifically designed for answering questions about tabular data."%}
@@ -179,11 +212,11 @@ Additionally, these transformers are available.
 {% include templates/anno_table_entry.md path="./transformers" name="XlmRoBertaForQuestionAnswering" summary="XlmRoBertaForQuestionAnswering can load XLM-RoBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD."%}
 {% include templates/anno_table_entry.md path="./transformers" name="XlmRoBertaForSequenceClassification" summary="XlmRoBertaForSequenceClassification can load XLM-RoBERTa Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="XlmRoBertaForTokenClassification" summary="XlmRoBertaForTokenClassification can load XLM-RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
-{% include templates/anno_table_entry.md path="./transformers" name="XlmRoBertaForZeroShotClassification" summary="XlmRoBertaForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language inference) tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="XlmRoBertaForZeroShotClassification" summary="XlmRoBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="XlmRoBertaSentenceEmbeddings" summary="Sentence-level embeddings using XLM-RoBERTa."%}
 {% include templates/anno_table_entry.md path="./transformers" name="XlnetEmbeddings" summary="XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective."%}
-{% include templates/anno_table_entry.md path="./transformers" name="XlnetForTokenClassification" summary="XlnetForTokenClassification can load XLNet Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="XlnetForSequenceClassification" summary="XlnetForSequenceClassification can load XLNet Models with sequence classification/regression head on top e.g. for multi-class document classification tasks."%}
+{% include templates/anno_table_entry.md path="./transformers" name="XlnetForTokenClassification" summary="XlnetForTokenClassification can load XLNet Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks."%}
 {% include templates/anno_table_entry.md path="./transformers" name="ZeroShotNer" summary="ZeroShotNerModel implements zero shot named entity recognition by utilizing RoBERTa transformer models fine tuned on a question answering task."%}
 
 
@@ -198,4 +231,4 @@ Additionally, these transformers are available.
         {% assign file_name = file.path | remove:  parent_path | remove:  "/" | prepend: "annotator_entries/" %}
         {% include_relative {{ file_name }} %}
     {% endif %}
-{% endfor %}
+{% endfor %}
\ No newline at end of file
diff --git a/docs/en/faq.md b/docs/en/faq.md
index 17b9e58f99905f..3d5263c5e1c4f5 100644
--- a/docs/en/faq.md
+++ b/docs/en/faq.md
@@ -33,8 +33,21 @@ To use Spark NLP in Python, follow these steps:
 
    ```python
    import sparknlp
+
    spark = sparknlp.start()
    ```
+   
+   #### Optional Parameters
+   - **gpu** (`bool`, default `False`) – Enable GPU acceleration.  
+   - **apple_silicon** (`bool`, default `False`) – Support for macOS Apple Silicon.  
+   - **aarch64** (`bool`, default `False`) – Support for Linux Aarch64.  
+   - **memory** (`str`, default `"16G"`) – Spark driver memory.  
+   - **cache_folder** (`str`) – Folder for pretrained models (`~/cache_pretrained` by default).  
+   - **log_folder** (`str`) – Temp folder for embeddings/logs (local, HDFS, or DBFS only).  
+   - **params** (`dict`) – Custom Spark config options.  
+   - **cluster_tmp_dir** (`str`) – Where annotator logs are saved.  
+   - **real_time_output** (`bool`, default `False`) – Show JVM output live.  
+   - **output_level** (`int`, default `1`) – Logging verbosity.  
 
 3. **Use Annotators**:
    Spark NLP offers a variety of annotators (e.g., Tokenizer, SentenceDetector, Lemmatizer). To use them, first create the appropriate pipeline.
@@ -44,9 +57,15 @@ To use Spark NLP in Python, follow these steps:
    ```python
    from sparknlp.base import DocumentAssembler
    from sparknlp.annotator import Tokenizer
+   from pyspark.ml import Pipeline
+
+   documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("document")
 
-   documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
-   tokenizer = Tokenizer().setInputCols(["document"]).setOutputCol("token")
+   tokenizer = Tokenizer() \
+      .setInputCols(["document"]) \
+      .setOutputCol("token")
 
    pipeline = Pipeline(stages=[documentAssembler, tokenizer])
    ```
@@ -59,7 +78,7 @@ To use Spark NLP in Python, follow these steps:
    ```
 
 5. **Explore and Utilize Models**:
-   Spark NLP offers pre-trained models for tasks like Named Entity Recognition (NER), sentiment analysis, and more. You can easily plug these into your pipeline and customize as needed.
+   Spark NLP offers a wide range of [pretrained models](https://sparknlp.org/models) for tasks like Named Entity Recognition (NER), sentiment analysis, and more. You can easily plug these into your pipeline and customize as needed.
 
 6. **Further Reading**:
    Dive deeper into the [official documentation](https://sparknlp.org/docs/en/install) for more detailed examples, a complete list of annotators and models, and best practices for building NLP pipelines.
@@ -96,38 +115,59 @@ Both spaCy and Spark NLP are popular libraries for Natural Language Processing,
 
 Spark NLP provides a range of models to tackle various NLP tasks. These models are often pre-trained on large datasets and can be fine-tuned or used directly for inference. Some of the primary categories and examples of Spark NLP models include:
 
-1. **Named Entity Recognition (NER)**:
-   - Pre-trained models for recognizing entities such as persons, organizations, locations, etc.
-   - Specialized models for sectors like healthcare to detect medical entities.
+1. **Named Entity Recognition (NER)**  
+   - Pre-trained models for recognizing entities such as persons, organizations, and locations.  
+   - Specialized models for domains like healthcare to detect medical entities.  
 
-2. **Text Classification**:
-   - Models for tasks like sentiment analysis, topic classification, and more.
+2. **Text Classification**  
+   - Models for sentiment analysis, topic classification, intent detection, and more.  
 
-3. **Word Embeddings**:
-   - Word2Vec, GloVe, and BERT embeddings.
-   - Models to generate embeddings for words or sentences, useful in many downstream tasks.
+3. **Word Embeddings**  
+   - Word2Vec, GloVe, and transformer embeddings (BERT, RoBERTa, etc.).  
+   - Generate embeddings for words or sentences to power downstream tasks.  
 
-4. **Language Models**:
-   - Models like BERT, ALBERT, and ELECTRA are available pre-trained and can be fine-tuned for specific tasks.
+4. **Language Models**  
+   - Pre-trained transformer-based models like BERT, ALBERT, ELECTRA.  
+   - Can be fine-tuned for domain-specific tasks.  
 
-5. **Dependency Parsing**:
-   - Models that analyze the grammatical structure of a sentence and determine relationships between words.
+5. **Dependency Parsing**  
+   - Models that analyze grammatical structure and word relationships.  
+
+6. **Spell Checking and Correction**  
+   - Detect and correct spelling mistakes in text.  
+
+7. **Sentence Embeddings**  
+   - Models for sentence-level vector representations, such as Universal Sentence Encoder.  
+
+8. **Translation and Language Detection**  
+   - Detect the language of a given text or translate between languages.  
+
+9. **Text Matching**  
+   - Models for textual similarity, duplicate detection, or paraphrase detection.  
+
+10. **Question Answering (QA)**  
+   - Extractive QA models built on transformers (e.g., BERT, DistilBERT).  
 
-6. **Spell Checking and Correction**:
-   - Models that can detect and correct spelling mistakes in the text.
+11. **Summarization**  
+   - Models for abstractive or extractive text summarization.  
 
-7. **Sentence Embeddings**:
-   - Models to generate vector representations for entire sentences, such as Universal Sentence Encoder.
+12. **Entity Resolution / Normalization**  
+   - Map recognized entities to canonical forms (e.g., ICD-10 codes in healthcare).  
 
-8. **Translation and Language Detection**:
-   - Models to detect the language of a given text or translate text between languages.
+13. **Zero-Shot & Few-Shot Classification**  
+   - Transformer-based models that classify text into categories without task-specific training.  
 
-9. **Text Matching**:
-   - Models that can be used for tasks like textual similarity, paraphrase detection, etc.
+14. **Large Language Models (LLMs)**  
+   - General-purpose models (e.g., Phi, Qwen, LLaMA, Mistral, Falcon, GPT-style, and MPT).  
+   - Used for text generation, summarization, rewriting, reasoning, and more.  
 
-10. **Pretrained Pipelines**:
+15. **Vision-Language Models (VLMs)**  
+   - Multimodal models combining text and images (e.g., BLIP, CLIP, LLaVA, Kosmos-2).  
+   - Tasks include image captioning, document understanding, OCR post-processing, and visual QA.  
 
-- Ready-to-use pipelines that combine multiple models and annotators for common tasks, allowing users to quickly start processing text without building a custom pipeline.
+16. **Pretrained Pipelines**  
+   - Ready-to-use pipelines combining multiple models and annotators.  
+   - Allow quick text processing without building a custom pipeline.  
 
 For the latest list of models, detailed documentation, and instructions on how to use them, visiting the [Official Spark NLP Models Hub](https://sparknlp.org/models) would be beneficial.
 
diff --git a/docs/en/install.md b/docs/en/install.md
index e5c0443bdf9fde..cf07ff5b9c3ebf 100644
--- a/docs/en/install.md
+++ b/docs/en/install.md
@@ -799,71 +799,30 @@ Find out more about `Spark NLP` versions from our [release notes](https://github
 
 Spark NLP {{ site.sparknlp_version }} has been tested and is compatible with the following runtimes:
 
-**CPU:**
-
-- 9.1
-- 9.1 ML
-- 10.1
-- 10.1 ML
-- 10.2
-- 10.2 ML
-- 10.3
-- 10.3 ML
-- 10.4
-- 10.4 ML
-- 10.5
-- 10.5 ML
-- 11.0
-- 11.0 ML
-- 11.1
-- 11.1 ML
-- 11.2
-- 11.2 ML
-- 11.3
-- 11.3 ML
-- 12.0
-- 12.0 ML
-- 12.1
-- 12.1 ML
-- 12.2
-- 12.2 ML
-- 13.0
-- 13.0 ML
-- 13.1
-- 13.1 ML
-- 13.2
-- 13.2 ML
-- 13.3
-- 13.3 ML
-- 14.0
-- 14.0 ML
-- 14.1
-- 14.1 ML
-- 15.x
-- 15.x ML
-
-**GPU:**
-
-- 9.1 ML & GPU
-- 10.1 ML & GPU
-- 10.2 ML & GPU
-- 10.3 ML & GPU
-- 10.4 ML & GPU
-- 10.5 ML & GPU
-- 11.0 ML & GPU
-- 11.1 ML & GPU
-- 11.2 ML & GPU
-- 11.3 ML & GPU
-- 12.0 ML & GPU
-- 12.1 ML & GPU
-- 12.2 ML & GPU
-- 13.0 ML & GPU
-- 13.1 ML & GPU
-- 13.2 ML & GPU
-- 13.3 ML & GPU
-- 14.0 ML & GPU
-- 14.1 ML & GPU
-- 15.x ML & GPU
+{:.table-model-big}
+|   CPU              |   GPU              |
+|--------------------|--------------------|
+| 9.1 / 9.1 ML       | 9.1 ML & GPU       |
+| 10.1 / 10.1 ML     | 10.1 ML & GPU      |
+| 10.2 / 10.2 ML     | 10.2 ML & GPU      |
+| 10.3 / 10.3 ML     | 10.3 ML & GPU      |
+| 10.4 / 10.4 ML     | 10.4 ML & GPU      |
+| 10.5 / 10.5 ML     | 10.5 ML & GPU      |
+| 11.0 / 11.0 ML     | 11.0 ML & GPU      |
+| 11.1 / 11.1 ML     | 11.1 ML & GPU      |
+| 11.2 / 11.2 ML     | 11.2 ML & GPU      |
+| 11.3 / 11.3 ML     | 11.3 ML & GPU      |
+| 12.0 / 12.0 ML     | 12.0 ML & GPU      |
+| 12.1 / 12.1 ML     | 12.1 ML & GPU      |
+| 12.2 / 12.2 ML     | 12.2 ML & GPU      |
+| 13.0 / 13.0 ML     | 13.0 ML & GPU      |
+| 13.1 / 13.1 ML     | 13.1 ML & GPU      |
+| 13.2 / 13.2 ML     | 13.2 ML & GPU      |
+| 13.3 / 13.3 ML     | 13.3 ML & GPU      |
+| 14.0 / 14.0 ML     | 14.0 ML & GPU      |
+| 14.1 / 14.1 ML     | 14.1 ML & GPU      |
+| 15.x / 15.x ML     | 15.x ML & GPU      |
+| 16.4 / 16.4 ML     | 16.4 ML & GPU      |
 
 

 
diff --git a/docs/en/quickstart.md b/docs/en/quickstart.md
index 12b074cf606a91..d05eb4408f02d9 100644
--- a/docs/en/quickstart.md
+++ b/docs/en/quickstart.md
@@ -54,7 +54,7 @@ Join our channel, to ask for help and share your feedback. Developers and users
 Make sure to check out our demos built by Streamlit to showcase Spark NLP in action:
 
 {:.btn-block}
-[Spark NLP Demo](/demo){:.button.button--primary.button--rounded.button--md}
+[Spark NLP Demo](/demos){:.button.button--primary.button--rounded.button--md}
 
 

 
diff --git a/docs/en/spark_nlp.md b/docs/en/spark_nlp.md
index c01e8dc6aa3206..8cd9fa0a4e41cd 100644
--- a/docs/en/spark_nlp.md
+++ b/docs/en/spark_nlp.md
@@ -62,7 +62,7 @@ Join our channel, to ask for help and share your feedback. Developers and users
 Make sure to check out our demos built by Streamlit to showcase Spark NLP in action:
 
 {:.btn-block}
-[Spark NLP Demo](/demo){:.button.button--primary.button--rounded.button--md}
+[Spark NLP Demo](/demos){:.button.button--primary.button--rounded.button--md}
 
 
 
diff --git a/docs/en/tasks/automatic_speech_recognition.md b/docs/en/tasks/automatic_speech_recognition.md
index ee0e96ccdc774a..82a28a3820b70d 100644
--- a/docs/en/tasks/automatic_speech_recognition.md
+++ b/docs/en/tasks/automatic_speech_recognition.md
@@ -11,15 +11,9 @@ sidebar:
   nav: sparknlp
 ---
 
-**Automatic Speech Recognition (ASR)** is the technology that enables computers to recognize and process human speech into text. ASR plays a vital role in numerous applications, from voice-activated assistants to transcription services, making it an essential part of modern natural language processing (NLP) solutions. Spark NLP provides powerful tools for implementing ASR systems effectively.
+Automatic Speech Recognition (ASR), also known as Speech-to-Text (STT), is the process of converting spoken language into written text. ASR systems analyze audio signals to identify speech patterns and map them to words using models that combine acoustic, linguistic, and contextual understanding. This enables devices and applications to interpret human speech naturally and accurately.
 
-In this context, ASR involves converting spoken language into text by analyzing audio signals. Common use cases include:
-
-- **Voice Assistants:** Enabling devices like smartphones and smart speakers to understand and respond to user commands.
-- **Transcription Services:** Automatically converting audio recordings from meetings, interviews, or lectures into written text.
-- **Accessibility:** Helping individuals with disabilities interact with technology through voice commands.
-
-By leveraging ASR, organizations can enhance user experience, improve accessibility, and streamline workflows that involve audio data.
+ASR is widely used in virtual assistants like **Siri**, **Alexa**, and **Google Assistant** for voice commands, and it powers automatic captioning, meeting transcriptions, and accessibility tools that help users with hearing impairments. By turning speech into text, ASR enables faster, hands-free interaction and improves access to spoken content across many applications.
 
 

   
@@ -27,98 +21,78 @@ By leveraging ASR, organizations can enhance user experience, improve accessibil
 
 ## Picking a Model
 
-When selecting a model for Automatic Speech Recognition, it’s essential to evaluate several factors to ensure optimal performance for your specific use case. Begin by analyzing the **nature of your audio data**, considering the accent, language, and quality of the recordings. Determine if your task requires **real-time transcription** or if batch processing is sufficient, as some models excel in specific scenarios.
+When picking a model for Automatic Speech Recognition, consider the language coverage, accuracy requirements, and computational resources available. Pretrained models like **Whisper** (by OpenAI) are excellent general-purpose options that support over 100 languages and handle accents, background noise, and different speech styles effectively. For English-only or smaller-scale use cases, models such as **Wav2Vec 2.0**, **Conformer**, or **DeepSpeech** offer strong accuracy with lower resource demands.
 
-Next, assess the **model complexity**; simpler models may suffice for straightforward tasks, while more sophisticated models are better suited for nuanced speech recognition. Consider the **availability of diverse audio data** for training, as larger datasets can significantly enhance model performance. Define key **performance metrics** (e.g., word error rate, accuracy) to guide your choice, and ensure the model's interpretability meets your requirements. Finally, account for **resource constraints**, as advanced models typically demand more memory and processing power.
+If your application involves multiple languages, look for **multilingual ASR models** that can automatically detect and transcribe speech in different languages within the same audio. For real-time or on-device applications, lightweight versions of these models are better suited due to their faster processing speed. Ultimately, the best model depends on your trade-off between **accuracy, speed, language support, and deployment constraints**.
 
-To explore and select from a variety of models, visit [Spark NLP Models](https://sparknlp.org/models), where you can find models tailored for different ASR tasks and languages.
+To explore and select from a variety of models, visit [Spark NLP Models](https://sparknlp.org/models)
 
 #### Recommended Models for Automatic Speech Recognition Tasks
 - **General Speech Recognition:** Use models like [`asr_wav2vec2_large_xlsr_53_english_by_jonatasgrosman`](https://sparknlp.org/2022/09/24/asr_wav2vec2_large_xlsr_53_english_by_jonatasgrosman_en.html){:target="_blank"} for general-purpose transcription.
 - **Multilingual Support:** For applications requiring support for multiple languages, consider using models like [`asr_wav2vec2_large_xlsr_53_portuguese_by_jonatasgrosman`](https://sparknlp.org/2021/12/15/wav2vec2.html){:target="_blank"} from the [`Wav2Vec2ForCTC`](https://sparknlp.org/docs/en/transformers#wav2vec2forctc){:target="_blank"} transformer.
 
-By thoughtfully considering these factors and using the right models, you can enhance your ASR applications significantly.
-
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Step 1: Assemble the raw audio content into a suitable format
 audioAssembler = AudioAssembler() \
     .setInputCol("audio_content") \
     .setOutputCol("audio_assembler")
 
-# Step 2: Load a pre-trained Wav2Vec2 model for automatic speech recognition (ASR)
 speechToText = Wav2Vec2ForCTC \
     .pretrained() \
     .setInputCols(["audio_assembler"]) \
     .setOutputCol("text")
 
-# Step 3: Define the pipeline with audio assembler and speech-to-text model
-pipeline = Pipeline().setStages([audioAssembler, speechToText])
+pipeline = Pipeline().setStages([
+  audioAssembler, 
+  speechToText
+])
 
-# Step 4: Create a DataFrame containing the raw audio content (as floats)
 processedAudioFloats = spark.createDataFrame([[rawFloats]]).toDF("audio_content")
 
-# Step 5: Fit the pipeline and transform the audio data
-result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats)
+model = pipeline.fit(processedAudioFloats)
+result = model.transform(processedAudioFloats)
 
-# Step 6: Display the transcribed text from the audio
-result.select("text.result").show(truncate = False)
+result.select("text.result").show(truncate=False)
 
-+------------------------------------------------------------------------------------------+
-|result                                                                                    |
-+------------------------------------------------------------------------------------------+
-|[MISTER QUILTER IS THE APOSTLE OF THE MIDLE CLASES AND WE ARE GLAD TO WELCOME HIS GOSPEL ]|
-+------------------------------------------------------------------------------------------+
 ```
 ```scala
-import spark.implicits._
 import com.johnsnowlabs.nlp.base._
 import com.johnsnowlabs.nlp.annotators._
-import com.johnsnowlabs.nlp.annotators.audio.Wav2Vec2ForCTC
 import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.types._
 
-// Step 1: Assemble the raw audio content into a suitable format
-val audioAssembler: AudioAssembler = new AudioAssembler()
+val audioAssembler = new AudioAssembler()
   .setInputCol("audio_content")
   .setOutputCol("audio_assembler")
 
-// Step 2: Load a pre-trained Wav2Vec2 model for automatic speech recognition (ASR)
-val speechToText: Wav2Vec2ForCTC = Wav2Vec2ForCTC
+val speechToText = Wav2Vec2ForCTC
   .pretrained()
   .setInputCols("audio_assembler")
   .setOutputCol("text")
 
-// Step 3: Define the pipeline with audio assembler and speech-to-text model
-val pipeline: Pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText))
-
-// Step 4: Load raw audio floats from a CSV file
-val bufferedSource =
-  scala.io.Source.fromFile("src/test/resources/audio/csv/audio_floats.csv")
+val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText))
 
-// Step 5: Extract raw audio floats from CSV and convert to an array of floats
-val rawFloats = bufferedSource
-  .getLines()
-  .map(_.split(",").head.trim.toFloat)
-  .toArray
-bufferedSource.close
+val schema = StructType(Array(StructField("audio_content", ArrayType(FloatType))))
+val data = Seq(Seq(rawFloats))
+val processedAudioFloats = spark.createDataFrame(data.map(Tuple1(_))).toDF("audio_content")
 
-// Step 6: Create a DataFrame with raw audio content (as floats)
-val processedAudioFloats = Seq(rawFloats).toDF("audio_content")
+val model = pipeline.fit(processedAudioFloats)
+val result = model.transform(processedAudioFloats)
 
-// Step 7: Fit the pipeline and transform the audio data
-val result = pipeline.fit(processedAudioFloats).transform(processedAudioFloats)
+result.select("text.result").show(false)
 
-// Step 8: Display the transcribed text from the audio
-result.select("text.result").show(truncate = false)
+```
+

 
+

+```
 +------------------------------------------------------------------------------------------+
 |result                                                                                    |
 +------------------------------------------------------------------------------------------+
@@ -131,13 +105,13 @@ result.select("text.result").show(truncate = false)
 
 If you want to see the outputs of ASR models in real time, visit our interactive demos:
 
-- **[Wav2Vec2ForCTC](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-Wav2Vec2ForCTC){:target="_blank"}** – Try this powerful model for real-time speech-to-text from raw audio.
-- **[WhisperForCTC](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-WhisperForCTC){:target="_blank"}** – Test speech recognition in multiple languages and noisy environments.
-- **[HubertForCTC](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-HubertForCTC){:target="_blank"}** – Experience quick and accurate voice command recognition.
+- **[Wav2Vec2ForCTC](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-Wav2Vec2ForCTC){:target="_blank"}**
+- **[WhisperForCTC](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-WhisperForCTC){:target="_blank"}**
+- **[HubertForCTC](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-HubertForCTC){:target="_blank"}**
 
 ## Useful Resources
 
-Want to dive deeper into Automatic Speech Recognition with Spark NLP? Here are somText Preprocessinge curated resources to help you get started and explore further:
+Want to dive deeper into Automatic Speech Recognition with Spark NLP? Here are some curated resources to help you get started and explore further:
 
 **Articles and Guides**
 - *[Converting Speech to Text with Spark NLP and Python](https://www.johnsnowlabs.com/converting-speech-to-text-with-spark-nlp-and-python/){:target="_blank"}*
diff --git a/docs/en/tasks/image_captioning.md b/docs/en/tasks/image_captioning.md
index 4317729b8b3fa7..ab899d69e33f17 100644
--- a/docs/en/tasks/image_captioning.md
+++ b/docs/en/tasks/image_captioning.md
@@ -11,116 +11,98 @@ sidebar:
   nav: sparknlp
 ---
 
-**Image Captioning** is the process of generating descriptive text for an image based on its visual content. This task is crucial in computer vision and has a variety of applications, such as enhancing accessibility for visually impaired individuals, improving image search, and enriching multimedia content. Spark NLP integrates image captioning with other NLP and vision-based tasks, enabling efficient and scalable caption generation within the same framework.
+Image captioning is the task of automatically generating a short, meaningful description for an image. It combines **computer vision**, which helps the model understand what is in the image, with **natural language processing**, which enables it to express that understanding in human language. For example, given an image of a dog running on the beach, the model might generate a caption like *"A dog running along the shoreline."*
 
-By utilizing image captioning models, we can produce natural language descriptions that capture the key elements and context of images. Common use cases include:
+This process typically involves two main components: a **vision model** (such as a CNN or Vision Transformer) that extracts visual features from the image, and a **language model** (like an LSTM or Transformer) that uses those features to generate text. The goal is not just to recognize objects but also to describe their relationships, actions, and context in a fluent, natural sentence.
 
-- **Social Media**: Automatically generating captions for user-uploaded images.
-- **E-Commerce**: Generating product descriptions based on visual attributes.
-- **Accessibility**: Describing visual content for the visually impaired.
-- **Search Engines**: Improving search results by associating images with relevant text.
+Image captioning has many practical uses, such as improving accessibility for visually impaired users, enhancing image search systems, organizing large photo collections, and supporting content generation in applications that rely on visual media.
 
 ## Picking a Model
 
-When selecting a model for image captioning, it’s important to consider the **image complexity** and the **quality of captions** required. For example, some tasks may need simple, high-level descriptions (e.g., "a person riding a bike"), while others might require more detailed, context-rich captions (e.g., "a young man riding a mountain bike on a sunny day").
+When picking a model for image captioning, consider how complex and descriptive you want the captions to be. Most modern systems use a **vision encoder** to interpret the image and a **language decoder** to generate the text. Popular pretrained combinations include **CNN + LSTM** architectures, and more recently, **Vision Transformer (ViT)** or **CLIP** encoders paired with **Transformer-based decoders** like **GPT-2** or **T5**.  
 
-Additionally, assess the **performance metrics** such as **BLEU score** or **ROUGE score** for evaluating the quality of generated captions. Ensure that the model is well-suited to your specific dataset, whether it consists of simple images like products or more complex images like natural scenes.
+If you want a general-purpose captioning model that works well across a wide variety of images, options like **BLIP**, **BLIP-2**, **OFA**, or **GIT** provide strong results out of the box. These models are trained on large image–text datasets and can generate fluent, contextually rich captions.  
 
 Explore pre-trained image captioning models in the [Spark NLP Models Hub](https://sparknlp.org/models) for a variety of datasets and tasks.
 
-#### Recommended Models for Image Captioning
-- **VisionEncoderDecoder For Image Captioning:** This model can be used for generating descriptive captions based on images. It utilizes a transformer-based architecture, providing high-quality captions for various types of images. Check out the pre-trained model [`image-captioning-vit-gpt2`](https://sparknlp.org/2023/09/20/image_captioning_vit_gpt2_en.html){:target="_blank"}.
+
 
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-# Import necessary libraries from Spark NLP
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Load image data into a DataFrame, discarding any invalid images
-imageDF = spark.read \
-    .format("image") \
-    .option("dropInvalid", value=True) \
-    .load("src/test/resources/image/")
-
-# Create an ImageAssembler to prepare image data for processing
 imageAssembler = ImageAssembler() \
     .setInputCol("image") \
     .setOutputCol("image_assembler")
 
-# Initialize the VisionEncoderDecoder model for image captioning
 imageCaptioning = VisionEncoderDecoderForImageCaptioning \
-    .pretrained() \  # Load a pre-trained model for image captioning
-    .setBeamSize(2)
-    .setDoSample(False)
+    .pretrained() \
+    .setDoSample(False) \
     .setInputCols(["image_assembler"]) \
     .setOutputCol("caption")
 
-# Create a pipeline that includes the image assembler and image captioning stages
-pipeline = Pipeline().setStages([imageAssembler, imageCaptioning])
+pipeline = Pipeline().setStages([
+    imageAssembler, 
+    imageCaptioning
+])
+
+imageDF = spark.read \
+    .format("image") \
+    .option("dropInvalid", value=True) \
+    .load("path/to/images/folder")
 
-# Fit the pipeline on the image DataFrame and transform the data
-pipelineDF = pipeline.fit(imageDF).transform(imageDF)
+model = pipeline.fit(imageDF)
+result = model.transform(imageDF)
 
-# Select and display the image file name and the generated captions
-pipelineDF \
+result \
     .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "caption.result") \
     .show(truncate=False)
 
-+-----------------+---------------------------------------------------------+
-|image_name       |result                                                   |
-+-----------------+---------------------------------------------------------+
-|palace.JPEG      |[a large room filled with furniture and a large window]  |
-|egyptian_cat.jpeg|[a cat laying on a couch next to another cat]            |
-|hippopotamus.JPEG|[a brown bear in a body of water]                        |
-|hen.JPEG         |[a flock of chickens standing next to each other]        |
-|ostrich.JPEG     |[a large bird standing on top of a lush green field]     |
-|junco.JPEG       |[a small bird standing on a wet ground]                  |
-|bluetick.jpg     |[a small dog standing on a wooden floor]                 |
-|chihuahua.jpg    |[a small brown dog wearing a blue sweater]               |
-|tractor.JPEG     |[a man is standing in a field with a tractor]            |
-|ox.JPEG          |[a large brown cow standing on top of a lush green field]|
-+-----------------+---------------------------------------------------------+
 ```
 ```scala
-// Import necessary libraries from Spark NLP
-import com.johnsnowlabs.nlp.annotator._
-import com.johnsnowlabs.nlp.ImageAssembler
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotators._
 import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions._
 
-// Load image data into a DataFrame, discarding invalid images
-val imageDF: DataFrame = spark.read
-  .format("image")
-  .option("dropInvalid", value = true)
-  .load("src/test/resources/image/")
-
-// Image Assembler: Prepares image data for processing
 val imageAssembler = new ImageAssembler()
   .setInputCol("image")
   .setOutputCol("image_assembler")
 
-// Initialize image captioning model
 val imageCaptioning = VisionEncoderDecoderForImageCaptioning
   .pretrained()
-  .setBeamSize(2)
   .setDoSample(false)
   .setInputCols("image_assembler")
   .setOutputCol("caption")
 
-// Create and fit the pipeline
-val pipeline = new Pipeline().setStages(Array(imageAssembler, imageCaptioning))
-val pipelineDF = pipeline.fit(imageDF).transform(imageDF)
+val pipeline = new Pipeline().setStages(Array(
+  imageAssembler,
+  imageCaptioning
+))
 
-// Display image names and generated captions
-pipelineDF
+val imageDF = spark.read
+  .format("image")
+  .option("dropInvalid", true)
+  .load("path/to/images/folder")
+
+val model = pipeline.fit(imageDF)
+val result = model.transform(imageDF)
+
+result
   .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "caption.result")
-  .show(truncate = false)
+  .show(false)
 
+```
+

+
+

+```
 +-----------------+---------------------------------------------------------+
 |image_name       |result                                                   |
 +-----------------+---------------------------------------------------------+
@@ -136,6 +118,7 @@ pipelineDF
 |ox.JPEG          |[a large brown cow standing on top of a lush green field]|
 +-----------------+---------------------------------------------------------+
 ```
+

 
 ## Try Real-Time Demos!
 
diff --git a/docs/en/tasks/image_classification.md b/docs/en/tasks/image_classification.md
index b0a5cdcc07eec5..b3e7fecd25c2a2 100644
--- a/docs/en/tasks/image_classification.md
+++ b/docs/en/tasks/image_classification.md
@@ -11,115 +11,96 @@ sidebar:
   nav: sparknlp
 ---
 
-**Image classification** is the process of assigning a **label** or **category** to an image based on its visual content. This task is fundamental in the field of computer vision and has numerous applications, from facial recognition to product classification in e-commerce. Spark NLP provides tools that make it easier to integrate image classification into your data pipelines, allowing for scalable, efficient image processing within the same framework.
+Image classification is a way for computers to recognize what an image contains by assigning it a single label, such as **"dog"**, **"car"**, or **"cat"**. The model looks for patterns such as shapes, colors, and textures that distinguish one class from another. It does not locate where the object is in the image or handle multiple objects; it simply identifies the overall category.
 
-By using image classification models, we can analyze and classify images into predefined categories based on patterns and features in the image data. Some common use cases include:
-
-- Classifying product images into categories like **clothing**, **electronics**, **furniture**, etc.
-- Recognizing objects in images, such as identifying animals, vehicles, or various types of landscapes.
-- Detecting facial expressions and other human features for tasks like emotion analysis or identity verification.
+In practice, this is used to organize and tag large collections of photos (like in **Google Photos** or **stock image sites**), filter content, or power **visual search systems**. The model’s output usually includes a few possible labels with **confidence scores** that show how sure it is about each prediction.
 
 ## Picking a Model
 
-When selecting a model for image classification, it’s essential to consider several factors that ensure optimal performance for your specific use case. Start by evaluating the **type of images** you are working with, such as grayscale vs. colored, high-resolution vs. low-resolution, or simple vs. complex visual patterns. Determine whether your task requires **binary classification** (e.g., cat vs. dog) or **multiclass classification** (e.g., classifying various animal species), as the right model choice depends on the complexity of the task.
+When picking a model for image classification, think about what you are trying to achieve. For simple tasks like recognizing a few object types or when you have limited computing power, lightweight models such as **MobileNet**, **EfficientNet-Lite**, or **ResNet-18** are good starting points because they are fast and easy to deploy. If you have a larger dataset and need higher accuracy, deeper architectures like **ResNet-50**, **DenseNet**, or **EfficientNet-B7** generally perform better when properly fine-tuned.
 
-Next, assess the **computational power** available to you. Complex models such as CNNs (Convolutional Neural Networks) can be resource-intensive but deliver highly accurate results. Simpler models may be sufficient for less demanding tasks. Ensure the model's **performance metrics** (accuracy, precision, recall) align with your project goals, and consider the **interpretability** of the model—more advanced models may be less interpretable but offer greater accuracy.
+If your images belong to a specific domain, consider using a **domain-pretrained model** that has been trained on similar data. For example, **MedNet** is designed for medical imaging, **GeoResNet** works well for satellite imagery, and **CLIP** is effective for general-purpose image and text matching. These models often outperform generic ones on domain-specific tasks.
 
-Explore a wide variety of image classification models on the [Spark NLP Models](https://sparknlp.org/models), where you can find pre-trained models suited for different tasks and datasets.
+To explore and select from a variety of models, visit [Spark NLP Models](https://sparknlp.org/models), where you can find models tailored for different tasks and datasets.
 
 #### Recommended Models for Specific Image Classification Tasks
 - **Object Detection:** For detecting objects in images, models such as [`image_classifier_vit_base_patch16_224`](https://sparknlp.org/2022/08/10/image_classifier_vit_base_patch16_224_en_3_0.html){:target="_blank"} can be used to detect objects across multiple categories.
 - **Facial Expression Recognition:** Models like [`image_classifier_swin_swin_large_patch4_window12_384`](https://sparknlp.org/2023/03/23/pipeline_image_classifier_swin_swin_large_patch4_window12_384_en.html){:target="_blank"} are great for tasks that involve recognizing facial emotions.
 - **Scene Classification:** To classify scenes into categories like **urban**, **rural**, or **forest**, models like [`image_classifier_vit_base_patch16_224`](https://sparknlp.org/2022/08/10/image_classifier_vit_base_patch16_224_en_3_0.html){:target="_blank"} can be applied effectively.
 
-By carefully considering your data, task requirements, and available resources, you can make an informed decision and leverage the best models for your image classification needs.
-
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Load image data into a DataFrame, discarding any invalid images
-imageDF = spark.read \
-    .format("image") \
-    .option("dropInvalid", value=True) \
-    .load("src/test/resources/image/")
-
-# Image Assembler: Prepares image data for processing
 imageAssembler = ImageAssembler() \
     .setInputCol("image") \
     .setOutputCol("image_assembler")
 
-# ViTForImageClassification: Pretrained Vision Transformer model for image classification
 imageClassifier = ViTForImageClassification \
     .pretrained() \
     .setInputCols(["image_assembler"]) \
     .setOutputCol("class")
 
-# Create a pipeline with image assembler and classifier stages
-pipeline = Pipeline().setStages([imageAssembler, imageClassifier])
+pipeline = Pipeline().setStages([
+    imageAssembler, 
+    imageClassifier
+])
+
+imageDF = spark.read \
+    .format("image") \
+    .option("dropInvalid", value=True) \
+    .load("path/to/images/folder")
 
-# Fit the pipeline on the image DataFrame and transform the data
-pipelineDF = pipeline.fit(imageDF).transform(imageDF)
+model = pipeline.fit(imageDF)
+result = model.transform(imageDF)
 
-# Select and display the image file name and the classification result
-pipelineDF \
+result \
   .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") \
   .show(truncate=False)
 
-+-----------------+----------------------------------------------------------+
-|image_name       |result                                                    |
-+-----------------+----------------------------------------------------------+
-|palace.JPEG      |[palace]                                                  |
-|egyptian_cat.jpeg|[Egyptian cat]                                            |
-|hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]|
-|hen.JPEG         |[hen]                                                     |
-|ostrich.JPEG     |[ostrich, Struthio camelus]                               |
-|junco.JPEG       |[junco, snowbird]                                         |
-|bluetick.jpg     |[bluetick]                                                |
-|chihuahua.jpg    |[Chihuahua]                                               |
-|tractor.JPEG     |[tractor]                                                 |
-|ox.JPEG          |[ox]                                                      |
-+-----------------+----------------------------------------------------------+
 ```
 ```scala
-import com.johnsnowlabs.nlp.annotator._
-import com.johnsnowlabs.nlp.ImageAssembler
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotators._
 import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions._
 
-// Load image data into a DataFrame, discarding invalid images
-val imageDF: DataFrame = spark.read
-  .format("image")
-  .option("dropInvalid", value = true)
-  .load("src/test/resources/image/")
-
-// Image Assembler: Prepares image data for further processing
 val imageAssembler = new ImageAssembler()
   .setInputCol("image")
   .setOutputCol("image_assembler")
 
-// Pretrained ViT model for image classification
 val imageClassifier = ViTForImageClassification
   .pretrained()
   .setInputCols("image_assembler")
   .setOutputCol("class")
 
-// Create a pipeline with the image assembler and classifier stages
-val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
+val pipeline = new Pipeline().setStages(Array(
+  imageAssembler,
+  imageClassifier
+))
+
+val imageDF = spark.read
+  .format("image")
+  .option("dropInvalid", true)
+  .load("path/to/images/folder")
 
-// Fit the pipeline on the image DataFrame and apply transformations
-val pipelineDF = pipeline.fit(imageDF).transform(imageDF)
+val model = pipeline.fit(imageDF)
+val result = model.transform(imageDF)
 
-// Select and display the image name and the classification result
-pipelineDF
+result
   .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result")
-  .show(truncate = false)
+  .show(false)
 
+```
+

+
+

+```
 +-----------------+----------------------------------------------------------+
 |image_name       |result                                                    |
 +-----------------+----------------------------------------------------------+
@@ -135,6 +116,7 @@ pipelineDF
 |ox.JPEG          |[ox]                                                      |
 +-----------------+----------------------------------------------------------+
 ```
+

 
 ## Try Real-Time Demos!
 
diff --git a/docs/en/tasks/image_text_to_text.md b/docs/en/tasks/image_text_to_text.md
new file mode 100644
index 00000000000000..7c08120d134967
--- /dev/null
+++ b/docs/en/tasks/image_text_to_text.md
@@ -0,0 +1,151 @@
+---
+layout: docs
+header: true
+seotitle:
+title: Image Text to Text
+permalink: docs/en/tasks/image_text_to_text
+key: docs-tasks-image-text-to-text
+modify_date: "2025-08-23"
+show_nav: true
+sidebar:
+  nav: sparknlp
+---
+
+**Image-text-to-text** models take in an image and text prompt and output text. These models are also called **vision-language models (VLMs)**. The difference from image-to-text models is that these models take an additional text input, not restricting the model to certain use cases like image captioning, and may also be trained to accept a conversation as input.
+
+### Types of Vision Language Models  
+
+- **Base Models**  
+  Pre-trained on large datasets and designed to be fine-tuned for specific tasks, such as Google’s [PaliGemma](https://sparknlp.org/models?q=PaliGemma&sort=downloads&annotator=PaliGemmaForMultiModal&type=model) family.  
+
+- **Instruction-tuned Models**  
+  Base models fine-tuned to better follow written instructions, like [Qwen2.5-7B-Instruct](https://sparknlp.org/models?q=Qwen2.5-7B-Instruct&sort=downloads).  
+
+- **Conversational Models**  
+  Base models fine-tuned on dialogue data to handle multi-turn conversations, such as [DeepSeek-VL-7B-Chat](https://sparknlp.org/models?q=deepseek-vl-7b-chat&type=model&sort=downloads).  
+
+### Common Use Cases  
+
+- **Multimodal Dialogue**
+These models can act as assistants that handle both text and images in a conversation. They remember the context and can respond across multiple turns while referring back to the same image.
+
+- **Object Detection and Image Segmentation**
+Some models can find, outline, or locate objects in an image. For example, you could ask if one object is behind another. They can even provide bounding boxes or segmentation masks directly, unlike older models that were trained only for detection or segmentation.
+
+- **Visual Question Answering**
+By learning from image–text pairs, these models can answer questions about an image or create captions that describe it.
+
+- **Document Question Answering and Retrieval**
+Documents often include tables, charts, and images. Instead of using OCR, these models can read documents directly and pull out the needed information.
+
+- **Image Recognition with Instructions**
+If you give a model detailed descriptions, it can identify or classify specific things in an image, rather than being limited to fixed label sets. For example, instead of just labeling “dog” or “cat,” you could ask it to find “a small brown dog wearing a red collar,” and it would pick out exactly that.
+
+## How to use
+
+![Cat In A Box](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp/master/src/test/resources/images/image1.jpg)
+
+

+{% include programmingLanguageSelectScalaPython.html %}
+```python
+from sparknlp.base import DocumentAssembler, ImageAssembler
+from sparknlp.annotator import AutoGGUFVisionModel
+from pyspark.sql.functions import lit
+from pyspark.ml import Pipeline
+
+images_path = "path/to/images/folder"
+prompt = "Describe this image."
+
+data = ImageAssembler.loadImagesAsBytes(spark, images_path)
+data = data.withColumn("prompt", lit(prompt))
+
+document_assembler = (
+    DocumentAssembler()
+    .setInputCol("prompt")
+    .setOutputCol("document")
+)
+
+image_assembler = (
+    ImageAssembler()
+    .setInputCol("image")
+    .setOutputCol("image_assembler")
+)
+
+auto_gguf_vision_model = (
+    AutoGGUFVisionModel.pretrained("qwen2_vl_2b_instruct_q4_gguf")
+    .setInputCols(["document", "image_assembler"])
+    .setOutputCol("completions")
+)
+
+pipeline = Pipeline(stages=[
+    document_assembler,
+    image_assembler,
+    auto_gguf_vision_model
+])
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.selectExpr(
+    "reverse(split(image.origin, '/'))[0] as image_name",
+    "completions.result"
+).show(truncate=False)
+
+```
+```scala
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotators._
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.ml.Pipeline
+
+val imagesPath = "path/to/images/folder"
+val prompt = "Describe this image."
+
+var data = ImageAssembler.loadImagesAsBytes(spark, imagesPath).withColumn("prompt", lit(prompt))
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("prompt")
+  .setOutputCol("document")
+
+val imageAssembler = new ImageAssembler()
+  .setInputCol("image")
+  .setOutputCol("image_assembler")
+
+val autoGgufVisionModel = AutoGGUFVisionModel.pretrained("qwen2_vl_2b_instruct_q4_gguf")
+  .setInputCols(Array("document", "image_assembler"))
+  .setOutputCol("completions")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  imageAssembler,
+  autoGgufVisionModel
+))
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.selectExpr(
+  "reverse(split(image.origin, '/'))[0] as image_name",
+  "completions.result"
+).show(truncate = false)
+
+```
+

+
+

+```
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|image_name      |result                                                                                                                                                                                                                                                                    |
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|cat_in_a_box.jpg|[The image shows a fluffy gray cat lying inside an open cardboard box on a carpeted floor. The cat appears to be relaxed and is stretched out in a comfortable position, with its paws sticking out of the box. In the background, there is a white couch against a wall.]|
++----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+```
+

+
+## Useful Resources
+
+- [Vision Language Models (Better, Faster, Stronger)](https://huggingface.co/blog/vlms-2025)
+- [Vision Language Models Explained](https://huggingface.co/blog/vlms)
+- [Welcome PaliGemma 2 – New vision language models by Google](https://huggingface.co/blog/paligemma2)
+- [Multimodal RAG using ColPali and Qwen2-VL](https://github.com/merveenoyan/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb)
+- [Preference Optimization for Vision Language Models with TRL](https://huggingface.co/blog/dpo_vlm)
\ No newline at end of file
diff --git a/docs/en/tasks/landing_page.md b/docs/en/tasks/landing_page.md
index c3ee2c147caee7..fa3b476a39392d 100644
--- a/docs/en/tasks/landing_page.md
+++ b/docs/en/tasks/landing_page.md
@@ -13,14 +13,14 @@ sidebar:
 
 

 
-Spark NLP is the central hub for all your State of the Art Natural Language Processing needs. Whether you're looking for demos, use cases, models, or datasets, you'll find the resources you need to begin any NLP task right here!
+Spark NLP is the central hub for all your State of the Art Natural Language Processing needs. Whether you're looking for models, use cases, or demos you'll find the resources you need to begin any NLP task right here!
 
 ## Natural Language Processing
 

 
 

 
-## Text Classification
+### Text Classification
 
 Text classification is the process of automatically categorizing text into predefined labels or categories based on its content.
 
@@ -29,7 +29,7 @@ Text classification is the process of automatically categorizing text into prede
 
 

 
-## Token Classification
+### Token Classification
 
 Token classification is the process of assigning labels to individual tokens (words or subwords) in a text, commonly used for tasks like named entity recognition or part-of-speech tagging.
 
@@ -40,7 +40,7 @@ Token classification is the process of assigning labels to individual tokens (wo
 
 

 
-## Zero-Shot Classification
+### Zero-Shot Classification
 
 Zero-shot classification is the process of categorizing text into labels without the model having seen any examples of those labels during training, using general knowledge and context.
 
@@ -49,7 +49,7 @@ Zero-shot classification is the process of categorizing text into labels without
 
 

 
-## Text Generation
+### Text Generation
 
 Text generation is the process of automatically creating coherent and contextually relevant text based on a given input or prompt using machine learning models.
 
@@ -60,7 +60,7 @@ Text generation is the process of automatically creating coherent and contextual
 
 

 
-## Question Answering
+### Question Answering
 
 Question answering models can retrieve answers from a given text, making them useful for searching documents. Some models can even generate answers independently, without needing any context!
 
@@ -69,7 +69,7 @@ Question answering models can retrieve answers from a given text, making them us
 
 

 
-## Table Question Answering
+### Table Question Answering
 
 Table question answering models can extract answers from structured data in tables, making it easy to query and retrieve specific information.
 
@@ -80,7 +80,7 @@ Table question answering models can extract answers from structured data in tabl
 
 

 
-## Summarization
+### Summarization
 
 Summarization models condense long texts into shorter versions, capturing the main ideas and key points while maintaining the overall meaning of the original content.
 
@@ -89,7 +89,7 @@ Summarization models condense long texts into shorter versions, capturing the ma
 
 

 
-## Translation
+### Translation
 
 Translation models automatically convert text from one language to another while preserving the meaning and context of the original content.
 
@@ -100,21 +100,37 @@ Translation models automatically convert text from one language to another while
 
 

 
-## Text Preprocessing
+### Text Preprocessing
 
 Text Preprocessing is the task of cleaning and transforming raw text into a format suitable for NLP tasks. This includes steps like tokenization, lowercasing, removing stop words, and stemming or lemmatization to prepare text for analysis.
 
 {:.btn-block}
 [Learn More](text_preprocessing){:.button.button--primary.button--rounded.button--md}
 
-

+
+
+

+
+

+
+## Multimodal
+

+
+

+
+### Image-Text-to-Text
+
+Image-Text-to-Text models take both an image and text as input and generate meaningful text outputs.
+
+{:.btn-block}
+[Learn More](image_text_to_text){:.button.button--primary.button--rounded.button--md}
 
 

 
@@ -125,7 +141,7 @@ Dependency Parsing is a syntactic analysis method that examines the grammatical
 
 

 
-## Image Classification
+### Image Classification
 
 Image classification models automatically categorize images into predefined labels or classes based on their visual content.
 
@@ -134,7 +150,7 @@ Image classification models automatically categorize images into predefined labe
 
 

 
-## Image Captioning
+### Image Captioning
 
 Image captioning models generate descriptive text for images, providing context and details about the visual content they depict.
 
@@ -145,7 +161,7 @@ Image captioning models generate descriptive text for images, providing context
 
 

 
-## Zero-Shot Image Classification
+### Zero-Shot Image Classification
 
 Zero-shot image classification is the process of categorizing images into labels without the model having seen any examples of those labels during training, using general knowledge and context.
 
@@ -161,7 +177,7 @@ Zero-shot image classification is the process of categorizing images into labels
 
 

 
-## Automatic Speech Recognition
+### Automatic Speech Recognition
 
 Automatic speech recognition (ASR) is the process of converting spoken language into written text.
 
diff --git a/docs/en/tasks/question_answering.md b/docs/en/tasks/question_answering.md
index 028c092b6996f5..65b79625d536e6 100644
--- a/docs/en/tasks/question_answering.md
+++ b/docs/en/tasks/question_answering.md
@@ -11,123 +11,103 @@ sidebar:
   nav: sparknlp  
 ---
 
-**Question Answering (QA)** is the task of automatically answering questions posed by humans in natural language. It is a fundamental problem in *natural language processing (NLP)*, playing a vital role in applications such as search engines, virtual assistants, customer support systems, and more. Spark NLP provides state-of-the-art (SOTA) models for QA tasks, enabling accurate and context-aware responses to user queries.
+**Question Answering (QA)** is a natural language processing task where models provide answers to questions using a given context, or in some cases, from their own knowledge without any context. For example, given the question *"Which name is also used to describe the Amazon rainforest in English?"* and the context *"The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle"*, a QA model would output *"Amazonia"*. This capability makes QA ideal for searching within documents and for powering systems like **FAQ automation**, **customer support**, and **knowledge-base search**.
 
-QA systems extract relevant information from a given context or knowledge base to answer a question. Depending on the model and input, they can either find exact answers within a text or generate a more comprehensive response.
+### Types of Question Answering
 
-## Types of Question Answering
-
-- **Open-Book QA:** In this approach, the model has access to external documents, passages, or knowledge sources to extract the answer. The system looks for relevant information within the provided text (e.g., "What is the tallest mountain in the world?" answered using a document about mountains).
-  
-- **Closed-Book QA:** Here, the model must rely solely on the knowledge it has been trained on, without access to external sources. The answer is generated from the model's internal knowledge (e.g., answering trivia questions without referring to external material).
-
-Common use cases include:
-
-- **Fact-based QA:** Answering factoid questions such as "What is the capital of France?"
-- **Reading Comprehension:** Extracting answers from a provided context, often used in assessments or educational tools.
-- **Dialogue-based QA:** Supporting interactive systems that maintain context across multiple turns of conversation.
-
-By leveraging QA models, organizations can build robust systems that improve user engagement, provide instant information retrieval, and offer customer support in a more intuitive manner.
-
-
+There are several QA variants: 
+- **Extractive QA**, which pulls the exact answer span from a context and is often solved with BERT-like models
+- **Open generative QA**, which generates natural-sounding answers based on a provided context
+- **Closed generative QA**, where the model answers entirely from its internal knowledge without any context. 
 
+QA systems can also be **open-domain**, covering a wide range of topics, or **closed-domain**, focused on specialized areas such as law or medicine. Together, these variants make QA a core building block for modern applications such as **search engines**, **chatbots**, and **virtual assistants**.
+∂
 ## Picking a Model
 
-When selecting a model for question answering, consider the following important factors. First, assess the **nature of your data** (e.g., structured knowledge base vs. unstructured text) and the **type of QA** needed (open-book or closed-book). Open-book QA requires models that can efficiently search and extract from external sources, while closed-book QA demands models with a large internal knowledge base.
+Depending on the QA variant and domain, different models are typically favored: **BERT**, **RoBERTa**, and **ALBERT** are strong choices for extractive QA; **T5**, **BART**, and newer families like **LLaMA 2** excel at open generative QA; models such as **LLaMA 2** and **Mistral** are well suited for closed generative QA; and domain-specific variants like **BioBERT**, **LegalBERT**, and **SciBERT** deliver the highest performance for specialized fields. A good rule of thumb is to use **extractive QA** when precise span-level answers are needed from a document, **open generative QA** when natural and context-aware responses are desired, and **closed generative QA** when relying on a model’s internal knowledge is sufficient or preferable.
 
-Evaluate the **complexity of the questions**—are they simple factoids or require more reasoning and multi-turn interactions? Metrics such as **Exact Match (EM)** and **F1 score** are commonly used to measure model performance in QA tasks. Finally, take into account the **computational resources** available, as some models, like BERT or T5, may require significant processing power.
+### Recommended Models for Specific QA Tasks  
 
-Explore models tailored for question answering at [Spark NLP Models](https://sparknlp.org/models), where you’ll find various options for different QA tasks.
+- **Extractive QA:** Models such as [`distilbert-base-cased-distilled-squad`](https://sparknlp.org/2023/11/26/distilbert_base_cased_qa_squad2_en.html){:target="_blank"} and [`bert-large-uncased-whole-word-masking-finetuned-squad`](https://sparknlp.org/2024/09/01/bert_large_uncased_whole_word_masking_finetuned_squad_google_bert_en.html){:target="_blank"} are well suited for identifying precise answer spans directly from context.  
 
-#### Recommended Models for Specific QA Tasks
+- **Open Generative QA (context-based):** For generating fluent, context-aware answers, models like [`t5_base`](https://sparknlp.org/2021/01/08/t5_base_en.html){:target="_blank"} and [`bart_base`](https://sparknlp.org/2025/02/08/qa_facebook_bart_base_ibrahimgiki_en.html){:target="_blank"} provide strong performance.  
 
-- **Extractive QA:** Use models like [`distilbert-base-cased-distilled-squad`](https://sparknlp.org/2023/11/26/distilbert_base_cased_qa_squad2_en.html){:target="_blank"} and [`bert-large-uncased-whole-word-masking-finetuned-squad`](https://sparknlp.org/2024/09/01/bert_large_uncased_whole_word_masking_finetuned_squad_google_bert_en.html){:target="_blank"} for extracting answers directly from a provided context.
-- **Generative QA (Closed-Book):** Consider models such as [`roberta-base-squad2`](https://sparknlp.org/2022/12/02/roberta_qa_deepset_base_squad2_en.html){:target="_blank"} or [`t5_base`](https://sparknlp.org/2021/01/08/t5_base_en.html){:target="_blank"} for generating answers based on internal knowledge without external context.
+- **Closed Generative QA (knowledge-based):** When answers must be drawn from the model’s internal knowledge rather than external context, options such as [`roberta-base-squad2`](https://sparknlp.org/2022/12/02/roberta_qa_deepset_base_squad2_en.html){:target="_blank"} and newer families like [`llama_2_7b_chat`](https://sparknlp.org/2024/05/19/llama_2_7b_chat_hf_int8_en.html){:target="_blank"} are effective choices.  
 
-By selecting the appropriate question answering model, you can enhance your ability to deliver accurate and relevant answers tailored to your specific NLP tasks.
+- **Domain-Specific QA:** For specialized use cases, consider domain-adapted models such as [`dmis-lab/biobert-large-cased-v1.1-squad`](https://sparknlp.org/2023/11/14/bert_qa_biobert_large_cased_v1.1_squad_en.html){:target="_blank"} for biomedical tasks, [`Beri/legal-qa`](http://127.0.0.1:4000/docs/en/tasks/question_answering){:target="_blank"} for legal texts, or [`ktrapeznikov/scibert_scivocab_uncased_squad_v2`](https://sparknlp.org/2023/11/15/bert_qa_scibert_scivocab_uncased_squad_v2_en.html){:target="_blank"} for scientific literature.  
 
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# 1. Document Assembler: Prepares the question and context text for further processing
 documentAssembler = MultiDocumentAssembler() \
     .setInputCols(["question", "context"]) \
-    .setOutputCol(["document_question", "document_context"])
+    .setOutputCols(["document_question", "document_context"])
 
-# 2. Question Answering Model: Uses a pretrained RoBERTa model for QA
-spanClassifier = RoBertaForQuestionAnswering.pretrained() \
+spanClassifier = DistilBertForQuestionAnswering.pretrained("distilbert_base_cased_qa_squad2", "en") \
     .setInputCols(["document_question", "document_context"]) \
     .setOutputCol("answer") \
-    .setCaseSensitive(False)
+    .setCaseSensitive(True)
 
-# 3. Pipeline: Combines the stages (DocumentAssembler and RoBERTa model) into a pipeline
-pipeline = Pipeline().setStages([
+pipeline = Pipeline(stages=[
     documentAssembler,
     spanClassifier
 ])
 
-# 4. Sample Data: Creating a DataFrame with a question and context
-data = spark.createDataFrame([["What's my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context")
+data = spark.createDataFrame([
+    ["What is my name?", "My name is Clara and I live in Berkeley."]
+]).toDF("question", "context")
 
-# 5. Running the Pipeline: Fitting the pipeline to the data and generating answers
-result = pipeline.fit(data).transform(data)
+model = pipeline.fit(data)
+result = model.transform(data)
 
-# 6. Displaying the Result: The output is the answer to the question extracted from the context
-result.select("answer.result").show(truncate=False)
+result.select("question", "context", "answer.result").show(truncate=False)
 
-+--------------------+
-|result              |
-+--------------------+
-|[Clara]             |
-+--------------------+
 ```
 
 ```scala
-import spark.implicits._
 import com.johnsnowlabs.nlp.base._
 import com.johnsnowlabs.nlp.annotator._
 import org.apache.spark.ml.Pipeline
 
-// 1. Document Assembler: Prepares the question and context text for further processing
-val document = new MultiDocumentAssembler()
-  .setInputCols("question", "context")
-  .setOutputCols("document_question", "document_context")
+val documentAssembler = new MultiDocumentAssembler()
+  .setInputCols(Array("question", "context"))
+  .setOutputCols(Array("document_question", "document_context"))
 
-// 2. Question Answering Model: Uses a pretrained RoBERTa model for QA
-val questionAnswering = RoBertaForQuestionAnswering.pretrained()
+val spanClassifier = DistilBertForQuestionAnswering.pretrained("distilbert_base_cased_qa_squad2", "en")
   .setInputCols(Array("document_question", "document_context"))
   .setOutputCol("answer")
   .setCaseSensitive(true)
 
-// 3. Pipeline: Combines the stages (DocumentAssembler and RoBERTa model) into a pipeline
 val pipeline = new Pipeline().setStages(Array(
-  document,
-  questionAnswering
+  documentAssembler,
+  spanClassifier
 ))
 
-// 4. Sample Data: Creating a DataFrame with a question and context
-val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
+val data = Seq(
+  ("What is my name?", "My name is Clara and I live in Berkeley.")
+).toDF("question", "context")
 
-// 5. Running the Pipeline: Fitting the pipeline to the data and generating answers
-val result = pipeline.fit(data).transform(data)
+val model = pipeline.fit(data)
+val result = model.transform(data)
 
-// 6. Displaying the Result: The output is the answer to the question extracted from the context
-result.select("answer.result").show(false)
+result.select("question", "context", "answer.result").show(truncate = false)
 
-+---------------------+
-|result               |
-+---------------------+
-|[Clara]              |
-+---------------------+
+```
+

+
+

+```
++----------------+----------------------------------------+-------+
+|question        |context                                 |result |
++----------------+----------------------------------------+-------+
+|What is my name?|My name is Clara and I live in Berkeley.|[Clara]|
++----------------+----------------------------------------+-------+
 ```
 

 
@@ -135,10 +115,10 @@ result.select("answer.result").show(false)
 
 If you want to see the outputs of question answering models in real time, visit our interactive demos:
 
-- **[BERT for Extractive Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-qa){:target="_blank"}** – Extract answers directly from provided context using the BERT model.
-- **[RoBERTa for Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-roberta-qa){:target="_blank"}** – Use RoBERTa for advanced extractive question answering tasks.
-- **[T5 for Abstractive Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-t5-qa){:target="_blank"}** – Generate abstractive answers using Google's T5 model.
-- **[Multihop QA with BERT](https://sparknlp.org/question_answering){:target="_blank"}** – Perform complex multihop question answering by reasoning over multiple pieces of text.
+- **[Multihop QA with BERT](https://sparknlp.org/question_answering){:target="_blank"}**
+- **[BERT for Extractive Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-qa){:target="_blank"}**
+- **[RoBERTa for Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-roberta-qa){:target="_blank"}**
+- **[T5 for Abstractive Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-t5-qa){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/summarization.md b/docs/en/tasks/summarization.md
index f1a5474fea01cc..8027e2f7449da1 100644
--- a/docs/en/tasks/summarization.md
+++ b/docs/en/tasks/summarization.md
@@ -11,123 +11,120 @@ sidebar:
   nav: sparknlp  
 ---
 
-**Summarization** is the task of generating concise and informative summaries from longer documents. This is useful for a wide range of applications, such as summarizing news articles, legal documents, or any large texts where key points need to be extracted. Spark NLP offers advanced summarization models that can create high-quality summaries efficiently.
+**Summarization** is a natural language processing task where models create a shorter version of a text while preserving its key information. Depending on the approach, models may use **extractive summarization**, which selects important sentences or phrases directly from the source, or **abstractive summarization**, which generates entirely new sentences that rephrase the original content. For example, given a passage about the Eiffel Tower’s height and history, a summarization model might produce *“The tower is 324 metres tall, about the height of an 81-storey building, and was the first structure to reach 300 metres.”*
 
-Summarization models take input text and generate shorter versions while preserving essential information. Common use cases include:
+This task is especially valuable for quickly processing large amounts of text in areas like **research paper summarization**, **news aggregation**, **financial reports**, and **legal documents**.
 
-- **News Summaries:** Automatically condensing long news articles into brief, digestible summaries.
-- **Legal Documents:** Summarizing lengthy contracts, case studies, or legal opinions.
-- **Research Papers:** Extracting key insights and conclusions from scientific papers.
+## Picking a Model  
 
-By leveraging summarization models, organizations can efficiently process large amounts of textual data and extract critical information, making it easier to consume and understand complex documents.
+The choice of model for summarization depends on whether the goal is extractive or abstractive. For **extractive summarization**, transformer-based classifiers like **BERTSUM** or lightweight variants such as **DistilBERT** can effectively identify the most important sentences to keep. For **abstractive summarization**, encoder–decoder architectures such as **BART** and **T5** are strong general-purpose options, while more recent families like **LLaMA 2** have shown strong performance when adapted for summarization tasks. In **domain-specific contexts** such as biomedical, legal, or financial texts—fine-tuned models like **BioBART** or **Longformer-based summarizers** often provide more accurate and context-aware results, particularly when working with long or technical documents.  
 
-## Picking a Model
+#### Recommended Models for Summarization Tasks  
 
-When choosing a summarization model, consider factors like the **length of the input text** and the **desired summary style** (e.g., extractive or abstractive). Some models are better suited for shorter inputs, while others excel in handling long documents. Evaluate whether your task requires **sentence-level summaries** or **paragraph-level condensation**.
+- **Extractive Summarization:** Models like [`sshleifer/distilbart-cnn-12-6`](https://sparknlp.org/2025/02/05/distilbart_cnn_12_6_sshleifer_en.html){:target="_blank"} and [`bertsumext`](https://github.com/nlpyang/PreSumm){:target="_blank"} are effective for selecting the most important sentences directly from the source text.  
 
-Consider the **domain** of the text, such as legal, scientific, or general news, as domain-specific models often perform better. Explore the available summarization models at [Spark NLP Models](https://sparknlp.org/models) to find the one that best suits your summarization needs.
+- **Abstractive Summarization:** Encoder–decoder models such as [`bart-large-cnn`](https://sparknlp.org/2025/01/26/bart_large_cnn_facebook_en.html){:target="_blank"} and [`t5-base`](https://sparknlp.org/2021/01/08/t5_base_en.html){:target="_blank"} are strong general-purpose choices for generating fluent, rephrased summaries.  
 
-#### Recommended Models for Summarization Tasks
+- **Domain-Specific Summarization:** Specialized variants like [`biobart`](https://sparknlp.org/2025/01/24/biobart_base_en.html){:target="_blank"} for biomedical literature or fine-tuned **Longformer-based summarizers** for legal and financial texts provide stronger results in technical or domain-focused contexts.  
 
-- **General Summarization:** For most summarization tasks, consider models like [`bart-large-cnn`](https://sparknlp.org/2023/05/11/bart_large_cnn_en.html){:target="_blank"} and [`t5-base`](https://sparknlp.org/2021/01/08/t5_base_en.html){:target="_blank"} are well suited for generating concise summaries.
-
-By selecting the right model, you can efficiently condense long documents into meaningful summaries, saving time and effort.
+Explore the available summarization models at [Spark NLP Models](https://sparknlp.org/models) to find the one that best suits your summarization needs.
 
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
-from sparknlp.base import *
-from sparknlp.annotator import *
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import BartTransformer
 from pyspark.ml import Pipeline
 
-# Step 1: Assemble raw text data into a format that Spark NLP can process
 documentAssembler = DocumentAssembler() \
     .setInputCol("text") \
-    .setOutputCol("documents")
+    .setOutputCol("document")
 
-# Step 2: Load a pretrained BART model for summarization
-bart = BartTransformer.pretrained("distilbart_xsum_12_6") \
+seq2seq = BartTransformer.pretrained("distilbart_cnn_12_6_sshleifer", "en") \
+    .setInputCols(["document"]) \
+    .setOutputCol("generation") \
     .setTask("summarize:") \
-    .setInputCols(["documents"]) \
     .setMaxOutputLength(200) \
-    .setOutputCol("summaries")
-
-# Step 3: Create a pipeline with the document assembler and BART model
-pipeline = Pipeline().setStages([documentAssembler, bart])
-
-# Step 4: Sample data - a long text passage for summarization
-data = spark.createDataFrame([[
-    "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a " +
-    "downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness" +
-    " of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this " +
-    "paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework " +
-    "that converts all text-based language problems into a text-to-text format. Our systematic study compares " +
-    "pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens " +
-    "of language understanding tasks. By combining the insights from our exploration with scale and our new " +
-    "Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering " +
-    "summarization, question answering, text classification, and more. To facilitate future work on transfer " +
-    "learning for NLP, we release our data set, pre-trained models, and code."
-]]).toDF("text")
-
-# Step 5: Apply the pipeline to generate the summary
-result = pipeline.fit(data).transform(data)
-
-# Step 6: Display the summary
-result.select("summaries.result").show(truncate=False)
-
-# +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-# |result                                                                                                                                                                          |
-# +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-# |[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, |
-# |methodologies, and practice .]                                                                                                                                                  |
-# +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+
+pipeline = Pipeline(stages=[
+    documentAssembler, 
+    seq2seq
+])
+
+passage = """
+Artificial intelligence is transforming industries around the world. 
+Healthcare systems are adopting AI to analyze medical images, predict patient outcomes, 
+and accelerate the discovery of new drugs. In finance, machine learning algorithms are 
+used to detect fraudulent transactions and provide personalized investment advice. 
+Transportation is also being reshaped by autonomous vehicles and smarter traffic 
+management systems. Despite these benefits, concerns remain about job displacement, 
+data privacy, and the ethical use of AI technologies. Governments and organizations 
+are working together to create guidelines and regulations that ensure the responsible 
+development of AI, while still fostering innovation and economic growth.
+"""
+
+data = spark.createDataFrame([[passage]]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("generation.result").show(truncate=False)
 
 ```
 ```scala
-import spark.implicits._
-import com.johnsnowlabs.nlp.base.DocumentAssembler
-import com.johnsnowlabs.nlp.annotators.seq2seq.GPT2Transformer
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotators._
 import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions._
 
-// Step 1: Document Assembler to prepare the text data
 val documentAssembler = new DocumentAssembler()
   .setInputCol("text")
-  .setOutputCol("documents")
-
-// Step 2: Load BART model for text generation with customization
-val bart = BartTransformer.pretrained("distilbart_xsum_12_6")
-  .setInputCols(Array("documents"))
-  .setMinOutputLength(10)
-  .setMaxOutputLength(30)
-  .setDoSample(true)
-  .setTopK(50)
-  .setOutputCol("generation")
-
-// Step 3: Define the pipeline stages
-val pipeline = new Pipeline().setStages(Array(documentAssembler, bart))
+  .setOutputCol("document")
 
-// Step 4: Input text data to be summarized
-val data = Seq(
-  "PG&E stated it scheduled the blackouts in response to forecasts for high winds " +
-  "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " +
-  "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
-).toDF("text")
-
-// Step 5: Fit the model and apply the pipeline
-val result = pipeline.fit(data).transform(data)
-
-// Step 6: Show the generated summary
-results.select("generation.result").show(truncate = false)
+val seq2seq = BartTransformer
+  .pretrained("distilbart_cnn_12_6_sshleifer", "en")
+  .setInputCols(Array("document"))
+  .setOutputCol("generation")
+  .setTask("summarize:")
+  .setMaxOutputLength(200)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  seq2seq
+))
+
+val passage =
+  """
+  Artificial intelligence is transforming industries around the world.
+  Healthcare systems are adopting AI to analyze medical images, predict patient outcomes,
+  and accelerate the discovery of new drugs. In finance, machine learning algorithms are
+  used to detect fraudulent transactions and provide personalized investment advice.
+  Transportation is also being reshaped by autonomous vehicles and smarter traffic
+  management systems. Despite these benefits, concerns remain about job displacement,
+  data privacy, and the ethical use of AI technologies. Governments and organizations
+  are working together to create guidelines and regulations that ensure the responsible
+  development of AI, while still fostering innovation and economic growth.
+  """
+
+val data = Seq(passage).toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("generation.result").show(false)
 
-// +--------------------------------------------------------------+
-// |result                                                        |
-// +--------------------------------------------------------------+
-// |[Nearly 800 thousand customers were affected by the shutoffs.]|
-// +--------------------------------------------------------------+
+```
+

 
+

+```
++----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|result                                                                                                                                                                                                                                                                                                                                                                                                    |
++----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[ Artificial intelligence is transforming industries around the world . Healthcare systems are adopting AI to analyze medical images and predict patient outcomes . In finance, machine learning algorithms are used to detect fraudulent transactions and provide personalized investment advice . Transportation is also being reshaped by autonomous vehicles and smarter traffic management systems .]|
++----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 ```
 

 
@@ -135,8 +132,8 @@ results.select("generation.result").show(truncate = false)
 
 If you want to see the outputs of text classification models in real time, visit our interactive demos:
 
-- **[Sparknlp Text Summarization](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}** – A live demo where you can try your inputs on text classification models on the go.
-- **[Text summarization](https://demo.johnsnowlabs.com/public/TEXT_SUMMARIZATION/){:target="_blank"}** – An interactive demo for sentiment and emotion detection.
+- **[Text summarization](https://demo.johnsnowlabs.com/public/TEXT_SUMMARIZATION/){:target="_blank"}**
+- **[Sparknlp Text Summarization](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/table_question_answering.md b/docs/en/tasks/table_question_answering.md
index 4729721b5e5a84..94207ed47d8d7a 100644
--- a/docs/en/tasks/table_question_answering.md
+++ b/docs/en/tasks/table_question_answering.md
@@ -11,63 +11,73 @@ sidebar:
   nav: sparknlp  
 ---
 
-**Table question answering** is the task of answering questions from structured tabular data. This is particularly useful for applications like financial reports, databases, and other contexts where information is stored in tables. Spark NLP provides state-of-the-art solutions for table question answering, enabling accurate extraction and generation of answers from tables in various formats.
+**Table Question Answering (Table QA)** is a natural language processing task where models answer questions using structured data from tables instead of plain text. Given a question and a table as context, the model retrieves or generates the correct answer by understanding the table’s rows, columns, and relationships. For example, if the table lists countries and their capitals, and the question is *“What is the capital of France?”*, the model would output *“Paris”*.  
 
-Table question answering models process tabular data and the question to output the most relevant answer. Common use cases include:
+Table QA is generally approached in two ways:  
 
-- **Financial Reports:** Automatically extracting insights from financial data tables.
-- **Databases:** Querying relational databases or spreadsheet data to extract specific information.
-- **Business Intelligence:** Enabling non-technical users to interact with and extract data from complex tables using natural language.
+- **Retrieval-based Table QA**, where the model selects the exact cell or value directly from the table.  
+- **Reasoning-based (or generative) Table QA**, where the model performs comparisons, aggregations, or generates a natural-language answer that goes beyond a single cell.  
 
-By leveraging table question answering, organizations can build systems capable of understanding tabular structures, making it easier to answer complex queries and automate data extraction.
+## Picking a Model  
 
-## Picking a Model
+The choice of model for Table QA depends on how the task is framed. For **Extractive table QA**, where the answer must be located within the table itself, models like **TaBERT** or **TAPAS** are effective since they are designed to align natural language questions with structured tabular data. For **Generative table QA**, where the model needs to produce more natural responses or combine information across rows and columns, sequence-to-sequence models such as **T5** or **BART** can be adapted with table-aware pretraining. For highly **specialized domains**, fine-tuned variants of TAPAS, TaBERT, or lightweight table-oriented transformers can deliver more accurate results, especially when trained on domain-specific spreadsheets, databases, or reporting formats.
 
-When selecting a model for table question answering, consider factors such as the **complexity of the table** and the **nature of the query**. Some models work better with numerical data, while others may handle textual data or multi-row operations more effectively.
+### Recommended Models for Specific Table QA Tasks  
 
-Evaluate the **format of the tables** you are working with (e.g., CSV, Excel, or SQL tables), and ensure that the model can process the tabular structure accurately. Also, consider the **domain** of your tables, such as finance, healthcare, or retail, as some models may be pre-trained on specific domains.
+- **Retrieval-based Table QA:** Use models like [`tapas-base-finetuned-wtq`](https://sparknlp.org/2022/09/30/table_qa_tapas_base_finetuned_wtq_en.html){:target="_blank"} or [`tabert`](https://github.com/facebookresearch/TaBERT){:target="_blank"} for selecting precise cells or values directly from a table.  
 
-Explore models tailored for table question answering at [Spark NLP Models](https://sparknlp.org/models), where you’ll find various options for different table QA tasks.
+- **Reasoning-based Table QA:** For questions requiring comparisons, aggregations, or multi-row/column reasoning, consider models such as [`tapas-medium-finetuned-wtq`](https://sparknlp.org/2022/09/30/table_qa_tapas_medium_finetuned_wtq_en.html){:target="_blank"} or table-adapted sequence-to-sequence models like [`t5-base`](https://sparknlp.org/2021/01/08/t5_base_en.html){:target="_blank"}.  
 
-#### Recommended Models for Specific Table Question Answering Tasks
-
-- **General Table QA:** Consider models such as [`tapas-large-finetuned-wtq`](https://sparknlp.org/2022/09/30/table_qa_tapas_large_finetuned_wtq_en.html){:target="_blank"} for answering questions across different types of tables.
-- **SQL Query Generation:** Use models like [`t5-small-wikiSQL`](https://sparknlp.org/2022/05/31/t5_small_wikiSQL_en_3_0.html){:target="_blank"} to automatically generate SQL queries from natural language inputs.
-
-By selecting the right model for table question answering, you can extract valuable insights from structured data and answer complex queries efficiently.
+Explore models tailored for table question answering at [Spark NLP Models](https://sparknlp.org/models)
 
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
+from pyspark.sql.functions import *
 from pyspark.ml import Pipeline
 
-# Document Assembler: Assembles table JSON and questions into documents
-document_assembler = MultiDocumentAssembler()\
-    .setInputCols("table_json", "questions")\
+json_data = """
+{
+  "header": ["name", "money", "age"],
+  "rows": [
+    ["Donald Trump", "$100,000,000", "75"],
+    ["Elon Musk", "$20,000,000,000,000", "55"]
+  ]
+}
+"""
+
+queries = [
+    "Who earns less than 200,000,000?",
+    "Who earns 100,000,000?", 
+    "How much money has Donald Trump?",
+    "How old are they?"
+]
+
+data = spark.createDataFrame([
+    [json_data, " ".join(queries)]
+]).toDF("table_json", "questions")
+
+document_assembler = MultiDocumentAssembler() \
+    .setInputCols("table_json", "questions") \
     .setOutputCols("document_table", "document_questions")
 
-# Sentence Detector: Splits the questions into individual sentences
-sentence_detector = SentenceDetector()\
-    .setInputCols(["document_questions"])\
+sentence_detector = SentenceDetector() \
+    .setInputCols(["document_questions"]) \
     .setOutputCol("questions")
 
-# Table Assembler: Converts the table document to the proper format
-table_assembler = TableAssembler()\
-    .setInputCols(["document_table"])\
+table_assembler = TableAssembler() \
+    .setInputCols(["document_table"]) \
     .setOutputCol("table")
 
-# Tapas Model: Loads pretrained Tapas for table question answering
-tapas = TapasForQuestionAnswering\
-    .pretrained()\
-    .setInputCols(["questions", "table"])\
+tapas = TapasForQuestionAnswering \
+    .pretrained("table_qa_tapas_base_finetuned_wtq", "en") \
+    .setInputCols(["questions", "table"]) \
     .setOutputCol("answers")
 
-# Pipeline: Combines all stages
 pipeline = Pipeline(stages=[
     document_assembler,
     sentence_detector,
@@ -75,118 +85,89 @@ pipeline = Pipeline(stages=[
     tapas
 ])
 
-# Sample JSON data for the table
-json_data = """
-{
-    "header": ["name", "money", "age"],
-    "rows": [
-    ["Donald Trump", "$100,000,000", "75"],
-    ["Elon Musk", "$20,000,000,000,000", "55"]
-    ]
- }
- """
-
-# Fit and transform the data with the pipeline
 model = pipeline.fit(data)
-model\
-    .transform(data)\
-    .selectExpr("explode(answers) AS answer")\
-    .select("answer.metadata.question", "answer.result")\
-    .show(truncate=False)
-
-# Expected Output:
-# +-----------------------+----------------------------------------+
-# |question               |result                                  |
-# +-----------------------+----------------------------------------+
-# |Who earns 100,000,000? |Donald Trump                            |
-# |Who has more money?    |Elon Musk                               |
-# |How much they all earn?|COUNT($100,000,000, $20,000,000,000,000)|
-# |How old are they?      |AVERAGE(75, 55)                         |
-# +-----------------------+----------------------------------------+
+result = model.transform(data)
+
+result.select(
+    posexplode("questions.result").alias("pos", "question"),
+    col("answers.result")[col("pos")].alias("answer")
+).select("question", "answer").show(truncate=False)
+
 ```
 ```scala
-import spark.implicits._
 import com.johnsnowlabs.nlp.base._
 import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.sql.functions._
 import org.apache.spark.ml.Pipeline
 
-// Questions: Sample questions about the table data
-val questions =
+val json_data =
   """
-   |Who earns 100,000,000?
-   |Who has more money?
-   |How old are they?
-   |""".stripMargin.trim
-
-// Table Data: JSON format for table with name, money, and age columns
-val jsonData =
+  {
+    "header": ["name", "money", "age"],
+    "rows": [
+      ["Donald Trump", "$100,000,000", "75"],
+      ["Elon Musk", "$20,000,000,000,000", "55"]
+    ]
+  }
   """
-   |{
-   | "header": ["name", "money", "age"],
-   | "rows": [
-   |   ["Donald Trump", "$100,000,000", "75"],
-   |   ["Elon Musk", "$20,000,000,000,000", "55"]
-   | ]
-   |}
-   |""".stripMargin.trim
-
-// DataFrame: Create DataFrame with table data and questions
-val data = Seq((jsonData, questions))
-  .toDF("json_table", "questions")
-  .repartition(1)
-
-// Document Assembler: Assemble the table JSON and questions into documents
-val docAssembler = new MultiDocumentAssembler()
-  .setInputCols("json_table", "questions")
+
+val queries = Array(
+  "Who earns less than 200,000,000?",
+  "Who earns 100,000,000?",
+  "How much money has Donald Trump?",
+  "How old are they?"
+)
+
+val data = Seq((json_data, queries.mkString(" "))).toDF("table_json", "questions")
+
+val documentAssembler = new MultiDocumentAssembler()
+  .setInputCols("table_json", "questions")
   .setOutputCols("document_table", "document_questions")
 
-// Sentence Detector: Detects individual questions from the text
-val sentenceDetector = SentenceDetectorDLModel
-  .pretrained()
-  .setInputCols(Array("document_questions"))
-  .setOutputCol("question")
+val sentenceDetector = new SentenceDetector()
+  .setInputCols("document_questions")
+  .setOutputCol("questions")
 
-// Table Assembler: Converts JSON table data into table format
 val tableAssembler = new TableAssembler()
-  .setInputFormat("json")
-  .setInputCols(Array("document_table"))
+  .setInputCols("document_table")
   .setOutputCol("table")
 
-// Tapas Model: Pretrained model for table question answering
 val tapas = TapasForQuestionAnswering
-  .pretrained()
-  .setInputCols(Array("question", "table"))
-  .setOutputCol("answer")
-
-// Pipeline: Combine all components into a pipeline
-val pipeline = new Pipeline()
-  .setStages(
-    Array(
-      docAssembler,
-      sentenceDetector,
-      tableAssembler,
-      tapas))
-
-// Model: Fit the pipeline to the data
-val pipelineModel = pipeline.fit(data)
-val result = pipeline.fit(data).transform(data)
-
-// Show Results: Explode answers and show the results for each question
+  .pretrained("table_qa_tapas_base_finetuned_wtq", "en")
+  .setInputCols("questions", "table")
+  .setOutputCol("answers")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  sentenceDetector,
+  tableAssembler,
+  tapas
+))
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
 result
-  .selectExpr("explode(answer) as answer")
-  .selectExpr(
-    "answer.metadata.question",
-    "answer.result")
-
-// Expected Output:
-// +-----------------------+----------------------------------------+
-// |question               |result                                  |
-// +-----------------------+----------------------------------------+
-// |Who earns 100,000,000? |Donald Trump                            |
-// |Who has more money?    |Elon Musk                               |
-// |How much they all earn?|COUNT($100,000,000, $20,000,000,000,000)|
-// |How old are they?      |AVERAGE(75, 55)                         |
-// +-----------------------+----------------------------------------+
+  .select(
+    posexplode($"questions.result").as(Seq("pos", "question")),
+    col("answers.result")(col("pos")).as("answer")
+  )
+  .select("question", "answer")
+  .show(false)
+
+```
+

+
+

+```
++--------------------------------+-----------------+
+|question                        |answer           |
++--------------------------------+-----------------+
+|Who earns less than 200,000,000?|Donald Trump     |
+|Who earns 100,000,000?          |Donald Trump     |
+|How much money has Donald Trump?|SUM($100,000,000)|
+|How old are they?               |AVERAGE(75, 55)  |
++--------------------------------+-----------------+
 ```
 

 
@@ -194,9 +175,9 @@ result
 
 If you want to see the outputs of table question answering models in real time, visit our interactive demos:
 
-- **[Tapas for Table Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-tapas){:target="_blank"}** – TAPAS answers questions from tabular data.
-- **[Tapex for Table QA](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-tapex){:target="_blank"}** – TAPEX handles complex table queries and computations.
-- **[SQL Query Generation](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-text-to-sql-t5){:target="_blank"}** – Converts natural language questions into SQL queries from tables.
+- **[Tapas for Table Question Answering](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-tapas){:target="_blank"}**
+- **[Tapex for Table QA](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-tapex){:target="_blank"}**
+- **[SQL Query Generation](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-text-to-sql-t5){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/text_classification.md b/docs/en/tasks/text_classification.md
index 7f643c60f5c67c..cde82c4ce1f97c 100644
--- a/docs/en/tasks/text_classification.md
+++ b/docs/en/tasks/text_classification.md
@@ -11,14 +11,7 @@ sidebar:
   nav: sparknlp
 ---
 
-**Text classification** is the process of assigning a **category** or **label** to a piece of text, such as an email, tweet, or review. It plays a crucial role in *natural language processing (NLP)*, where it is used to automatically organize text into predefined categories. Spark NLP provides various solutions to address text classification challenges effectively.
-
-In this context, text classification involves analyzing a document's content to categorize it into one or more predefined groups. Common use cases include:
-
-- Organizing news articles into categories like **politics**, **sports**, **entertainment**, or **technology**.
-- Conducting sentiment analysis, where customer reviews of products or services are classified as **positive**, **negative**, or **neutral**.
-
-By leveraging text classification, organizations can enhance their ability to process and understand large volumes of text data efficiently.
+**Text classification** is a natural language processing task where entire pieces of text, such as sentences, paragraphs, or documents, are assigned *predefined labels*. Common subtasks include **sentiment analysis** and **topic classification**. For instance, *sentiment analysis* models can determine whether a review is *positive*, *negative*, or *neutral*, while *topic classification* models can categorize news articles into areas like **politics**, **sports**, or **technology**.
 
 

   
@@ -26,116 +19,100 @@ By leveraging text classification, organizations can enhance their ability to pr
 
 ## Picking a Model
 
-When selecting a model for text classification, it’s crucial to evaluate several factors to ensure optimal performance for your specific use case. Start by analyzing the **nature of your data**, considering whether it is formal or informal and its length (e.g., tweets vs. reviews). Determine if your task requires **binary classification** (like spam detection) or **multiclass classification** (such as categorizing news topics), as some models excel in specific scenarios.
-
-Next, assess the **model complexity**; simpler models like Logistic Regression work well for straightforward tasks, while more complex models like BERT are suited for nuanced understanding. Consider the **availability of labeled data**—larger datasets allow for training sophisticated models, whereas smaller datasets may benefit from pre-trained options. Define key **performance metrics** (e.g., accuracy, F1 score) to inform your choice, and ensure the model's interpretability meets your requirements. Finally, account for **resource constraints**, as advanced models will demand more memory and processing power.
+When picking a model for text classification, think about what you’re trying to do—like detecting spam (binary), analyzing sentiment across positive, neutral, and negative (multiclass), or tagging a news article with several topics at once (multi-label). If you only have a small dataset, lightweight options such as **DistilBERT** are quick to train and deploy, while larger transformers like **BERT** or **RoBERTa** generally give better accuracy when fine-tuned on enough data. For specialized fields, domain-trained models like [**BioBERT**](https://sparknlp.org/models?q=biobert&sort=downloads&task=Text+Classification&type=model&annotator=BertForSequenceClassification) for biomedical research or [**FinBERT**](https://sparknlp.org/models?q=FinBERT&sort=downloads&task=Text+Classification&type=model&annotator=BertForSequenceClassification) for finance usually outperform general-purpose ones. Finally, keep in mind practical constraints—how much compute you have, whether you need real-time predictions, how important explainability is, and what balance you want between speed, cost, and accuracy.
 
 To explore and select from a variety of models, visit [Spark NLP Models](https://sparknlp.org/models), where you can find models tailored for different tasks and datasets.
 
-
 #### Recommended Models for Specific Text Classification Tasks
 - **Sentiment Analysis:** Use models specifically designed for sentiment detection, such as [`distilbert_sequence_classifier_sst2`](https://sparknlp.org/2021/11/21/distilbert_sequence_classifier_sst2_en.html){:target="_blank"}.
 - **News Categorization:** Models like [`distilroberta-finetuned-financial-news-sentiment-analysis`](https://sparknlp.org/2023/11/29/roberta_sequence_classifier_distilroberta_finetuned_financial_news_sentiment_analysis_en.html){:target="_blank"} are ideal for classifying news articles into relevant categories.
 - **Review Analysis:** For product reviews, consider using [`distilbert_base_uncased_finetuned_sentiment_amazon`](https://sparknlp.org/2023/11/18/distilbert_base_uncased_finetuned_sentiment_amazon_en.html){:target="_blank"} for more nuanced insights.
 
-If you have specific needs that are not covered by existing models, you can train your own model tailored to your unique requirements. Follow the guidelines provided in the [Spark NLP Training Documentation](https://sparknlp.org/docs/en/training) to get started on creating and training a model suited for your text classification task.
-
-By thoughtfully considering these factors and using the right models, you can enhance your NLP applications significantly.
+If you have specific needs that are not covered by existing models, you can train your own model tailored to your unique requirements. Follow the guidelines provided in the [Spark NLP Training Documentation](https://sparknlp.org/docs/en/training) to get started.
 
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Assembling the document from the input text
 documentAssembler = DocumentAssembler() \
     .setInputCol("text") \
     .setOutputCol("document")
 
-# Tokenizing the text
 tokenizer = Tokenizer() \
     .setInputCols(["document"]) \
     .setOutputCol("token")
 
-# Loading a pre-trained sequence classification model
-# You can replace `BertForSequenceClassification.pretrained()` with your selected model 
-# For example: BertForSequenceClassification.pretrained("distilbert_sequence_classifier_sst2", "en")
 sequenceClassifier = BertForSequenceClassification.pretrained() \
     .setInputCols(["token", "document"]) \
     .setOutputCol("label") \
     .setCaseSensitive(True)
 
-# Defining the pipeline with document assembler, tokenizer, and classifier
 pipeline = Pipeline().setStages([
     documentAssembler,
     tokenizer,
     sequenceClassifier
 ])
 
-# Creating a sample DataFrame
-data = spark.createDataFrame([["I loved this movie when I was a child.", "It was pretty boring."]]).toDF("text")
+data = spark.createDataFrame([
+    ("I loved this movie when I was a child.",),
+    ("It was pretty boring.",)
+]).toDF("text")
 
-# Fitting the pipeline and transforming the data
-result = pipeline.fit(data).transform(data)
+model = pipeline.fit(data)
+result = model.transform(data)
 
-# Showing the classification result
-result.select("label.result").show(truncate=False)
+result.select("text", "label.result").show(truncate=False)
 
-+------+
-|result|
-+------+
-|[pos] |
-|[neg] |
-+------+
 ```
 
 ```scala
-import spark.implicits._
 import com.johnsnowlabs.nlp.base._
 import com.johnsnowlabs.nlp.annotator._
 import org.apache.spark.ml.Pipeline
+import spark.implicits._
 
-// Step 1: Convert raw text into document format
 val documentAssembler = new DocumentAssembler()
   .setInputCol("text")
   .setOutputCol("document")
 
-// Step 2: Tokenize the document into words
 val tokenizer = new Tokenizer()
   .setInputCols("document")
   .setOutputCol("token")
 
-// Step 3: Load a pre-trained BERT model for sequence classification
 val sequenceClassifier = BertForSequenceClassification.pretrained()
   .setInputCols("token", "document")
   .setOutputCol("label")
   .setCaseSensitive(true)
 
-// Step 4: Define the pipeline with stages for document assembly, tokenization, and classification
 val pipeline = new Pipeline().setStages(Array(
   documentAssembler,
   tokenizer,
   sequenceClassifier
 ))
 
-// Step 5: Create sample data and apply the pipeline
 val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
-val result = pipeline.fit(data).transform(data)
 
-// Step 6: Show the classification results
-result.select("label.result").show(false)
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("text", "label.result").show(truncate=False)
 
-+------+
-|result|
-+------+
-|[pos] |
-|[neg] |
-+------+
+```
+

+
+

+```
++--------------------------------------+------+
+|text                                  |result|
++--------------------------------------+------+
+|I loved this movie when I was a child.|[pos] |
+|It was pretty boring.                 |[neg] |
++--------------------------------------+------+
 ```
 

 
@@ -143,8 +120,8 @@ result.select("label.result").show(false)
 
 If you want to see the outputs of text classification models in real time, visit our interactive demos:
 
-- **[BERT Annotators Demo](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}** – A live demo where you can try your inputs on text classification models on the go.
-- **[Sentiment & Emotion Detection Demo](https://nlp.johnsnowlabs.com/detect_sentiment_emotion){:target="_blank"}** – An interactive demo for sentiment and emotion detection.
+- **[Sentiment & Emotion Detection Demo](https://nlp.johnsnowlabs.com/detect_sentiment_emotion){:target="_blank"}**
+- **[BERT Annotators Demo](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/text_generation.md b/docs/en/tasks/text_generation.md
index beb230117f4f03..36ec30bc66974d 100644
--- a/docs/en/tasks/text_generation.md
+++ b/docs/en/tasks/text_generation.md
@@ -11,145 +11,98 @@ sidebar:
   nav: sparknlp  
 ---
 
-**Text generation** is the task of generating meaningful text based on a given input. It is widely used in various *natural language processing (NLP)* applications such as summarization, machine translation, conversational agents, and more. Spark NLP provides SOTA solutions for text generation, enabling you to produce high-quality and contextually relevant text outputs.
+**Text Generation** is a natural language processing task where models create new text based on a given input. Instead of assigning labels, these models expand, complete, or rephrase text in a coherent way. For example, given *“Once upon a time,”* a text generation model might continue with *“we knew that our ancestors were on the verge of extinction...”*. This task includes both **completion models**, which predict the next word in a sequence to build longer passages, and **text-to-text models**, which map one piece of text to another for tasks like *translation*, *summarization*, or *classification*.
 
-Text generation models create text sequences by predicting the next word or sequence of words based on the input prompt. Common use cases include:
+Depending on how they are trained, text generation models come in different variants: **base models** (e.g., Mistral 7B, Llama 3 70B) suited for fine-tuning, **instruction-tuned models** (e.g., Qwen 2, Yi 1.5, Llama 70B Instruct) that follow prompts like *“Write a recipe for chocolate cake”*, and **human feedback models**, which use RLHF to align outputs with human preferences. These capabilities make text generation useful for a wide range of applications, from **chatbots and creative writing** to **code generation and summarization**, with larger models typically producing more fluent and context-aware outputs.
 
-- **Summarization:** Automatically generating concise summaries from longer text.
-- **Machine Translation:** Translating text from one language to another while maintaining meaning and fluency.
-- **Conversational Agents:** Building intelligent systems that can hold natural and coherent conversations with users.
+## Picking a Model  
 
-By leveraging text generation, organizations can build systems capable of generating human-like text, making it useful for content creation, automated writing, and more.
+When picking a model for text generation, start by clarifying your goal—whether you need completions, rephrasings, translations, summaries, or creative writing. Base models like **Mistral 7B** or **Llama 3 70B** are good for fine-tuning, while instruction-tuned ones such as **Qwen 2** or **Llama 70B Instruct** work better out of the box for prompts like “Write a recipe for chocolate cake.” Human-feedback models trained with RLHF usually give the most user-aligned responses. For quick or lightweight tasks, smaller models are efficient, while larger ones generally produce more fluent, context-aware text suited for chatbots, code generation, and long-form writing. For specific tasks, **Pegasus**, **BART**, or **Llama 3 Instruct** are strong for summarization; **MarianMT**, **M2M-100**, or **NLLB** excel at translation; **GPT-based models**, **Llama 3 70B Instruct**, and **Yi 1.5** are strong for creative writing; **Code Llama**, **StarCoder**, and **GPT-4 Turbo (Code)** are well-suited for code generation; and for dialogue, **Llama 3 Instruct**, **Qwen 2**, and **GPT-4** provide reliable conversational performance.  
 
-
-
-## Picking a Model
-
-When selecting a model for text generation, consider several important factors. First, determine the **type of output** you require (e.g., summarization, translation, or free-form generation). Decide whether your task needs **structured output** like summaries or **creative text generation**.
-
-Next, evaluate the **style and language** of the data you'll be working with—are you dealing with formal language (e.g., research papers) or informal language (e.g., social media)? Model performance metrics such as **perplexity**, **BLEU score**, or **ROUGE score** are also crucial for understanding the quality of the generated text. Finally, take into account the **computational resources** available, as some models (e.g., GPT or T5) may require significant memory and processing power.
-
-Explore models tailored for text generation at [Spark NLP Models](https://sparknlp.org/models), where you’ll find various options for different text generation tasks.
+Explore models tailored for text generation at [Spark NLP Models](https://sparknlp.org/models)
 
+
 
-By selecting the appropriate text generation model, you can enhance your ability to produce contextually rich and meaningful text outputs tailored to your specific NLP tasks.
-  
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
-from sparknlp.base import *
-from sparknlp.annotator import *
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import AutoGGUFModel
 from pyspark.ml import Pipeline
 
-# Assembling the document from the input text
-documentAssembler = DocumentAssembler() \
-    .setInputCol("text") \
-    .setOutputCol("documents")
-
-# Loading a pre-trained text generation model
-# You can replace `T5Transformer.pretrained("t5_small", "xx")` with your selected model and the transformer it's based on
-# For example: BartTransformer.pretrained("bart_large_cnn")
-t5 = T5Transformer.pretrained("t5_small", "xx") \
-    .setTask("summarize:") \
-    .setInputCols(["documents"]) \
-    .setMaxOutputLength(200) \
-    .setOutputCol("summaries")
-
-# Defining the pipeline with document assembler, tokenizer, and classifier
-pipeline = Pipeline().setStages([documentAssembler, t5])
-
-# Creating a sample DataFrame
-data = spark.createDataFrame([[
-    "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a " +
-    "downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness" +
-    " of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this " +
-    "paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework " +
-    "that converts all text-based language problems into a text-to-text format. Our systematic study compares " +
-    "pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens " +
-    "of language understanding tasks. By combining the insights from our exploration with scale and our new " +
-    "Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering " +
-    "summarization, question answering, text classification, and more. To facilitate future work on transfer " +
-    "learning for NLP, we release our data set, pre-trained models, and code."
-]]).toDF("text")
-
-# Fitting the pipeline and transforming the data
-result = pipeline.fit(data).transform(data)
-
-# Showing the results
-result.select("summaries.result").show(truncate=False)
-
-+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-|result                                                                                                                                                                          |
-+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-|[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, |
-| methodologies, and practice .]                                                                                                                                                 |
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 
-```
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+auto_gguf_model = AutoGGUFModel.pretrained("qwen3_4b_q4_k_m_gguf", "en") \
+    .setInputCols(["document"]) \
+    .setOutputCol("completions") \
+    .setBatchSize(4) \
+    .setNPredict(20) \
+    .setNGpuLayers(99) \
+    .setTemperature(0.4) \
+    .setTopK(40) \
+    .setTopP(0.9) \
+    .setPenalizeNl(True)
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    auto_gguf_model
+])
+
+data = spark.createDataFrame([
+    ["A farmer has 17 sheep. All but 9 run away. How many sheep does the farmer have left?"]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
 
+```
 ```scala
-import spark.implicits._
-import com.johnsnowlabs.nlp.base.DocumentAssembler
-import com.johnsnowlabs.nlp.annotators.seq2seq.T5Transformer
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotators._
 import org.apache.spark.ml.Pipeline
 
-// Step 1: Assembling the document from the input text
-// Converts the input 'text' column into a 'document' column, required for NLP tasks
 val documentAssembler = new DocumentAssembler()
   .setInputCol("text")
-  .setOutputCol("documents")
-
-// Step 3: Loading a pre-trained BERT model for token classification
-// Applies a pre-trained BERT model for Named Entity Recognition (NER) to classify tokens
-// `T5Transformer.pretrained()` loads the model, and `setInputCols` defines the input columns
-val t5 = T5Transformer.pretrained("t5_small")
-  .setTask("summarize:")
-  .setInputCols(Array("documents"))
-  .setMaxOutputLength(200)
-  .setOutputCol("summaries")
-
-// Step 4: Defining the pipeline
-// The pipeline stages are document assembler, tokenizer, and token classifier
-val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))
-
-// Step 5: Creating a sample DataFrame
-// Creates a DataFrame with a sample sentence that will be processed by the pipeline
-val data = Seq(
-  "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a " +
-  "downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness" +
-  " of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this " +
-  "paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework " +
-  "that converts all text-based language problems into a text-to-text format. Our systematic study compares " +
-  "pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens " +
-  "of language understanding tasks. By combining the insights from our exploration with scale and our new " +
-  "Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering " +
-  "summarization, question answering, text classification, and more. To facilitate future work on transfer " +
-  "learning for NLP, we release our data set, pre-trained models, and code."
-).toDF("text")
-
-// Step 6: Fitting the pipeline and transforming the data
-// The pipeline is fitted on the input data, then it performs the transformation to generate token labels
-val result = pipeline.fit(data).transform(data)
-
-// Step 7: Showing the results
-// Displays the 'label.result' column, which contains the Named Entity Recognition (NER) labels for each token
-result.select("summaries.result").show(false)
-
-+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-|result                                                                                                                                                                          |
-+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-|[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, |
-|methodologies, and practice .]                                                                                                                                                  |
-+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+  .setOutputCol("document")
+
+val autoGGUFModel = AutoGGUFModel.pretrained("qwen3_4b_q4_k_m_gguf", "en")
+  .setInputCols("document")
+  .setOutputCol("completions")
+  .setBatchSize(4)
+  .setNPredict(20)
+  .setNGpuLayers(99)
+  .setTemperature(0.4f)
+  .setTopK(40)
+  .setTopP(0.9f)
+  .setPenalizeNl(true)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  autoGGUFModel
+))
+
+val data = Seq("A farmer has 17 sheep. All but 9 run away. How many sheep does the farmer have left?").toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+```
+

+
+

+```
+Explanation:
+The phrase "all but 9 run away" means that 9 sheep did not run away, while the remaining (17 - 9 = 8) did. Therefore, the farmer still has the 9 sheep that stayed behind.
+Answer: 9.
 ```
 

 
@@ -157,10 +110,10 @@ result.select("summaries.result").show(false)
 
 If you want to see the outputs of text generation models in real time, visit our interactive demos:
 
-- **[Generative Pre-trained Transformer 2 (OpenAI GPT2)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-gpt2){:target="_blank"}** – GPT-2 generates human-like text from prompts.
-- **[Text-To-Text Transfer Transformer (Google T5)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-t5){:target="_blank"}** – T5 performs text tasks like summarization and translation.
-- **[SQL Query Generation](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-text-to-sql-t5){:target="_blank"}** – Converts natural language commands into SQL queries.
-- **[Multilingual Text Translation with MarianMT](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-MarianMT){:target="_blank"}** – Translates text between multiple languages.
+- **[Generative Pre-trained Transformer 2 (OpenAI GPT2)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-gpt2){:target="_blank"}**
+- **[Text-To-Text Transfer Transformer (Google T5)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-t5){:target="_blank"}**
+- **[SQL Query Generation](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-text-to-sql-t5){:target="_blank"}**
+- **[Multilingual Text Translation with MarianMT](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-MarianMT){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/text_preprocessing.md b/docs/en/tasks/text_preprocessing.md
index 5135ffab9ac7d6..4ea900bf16667d 100644
--- a/docs/en/tasks/text_preprocessing.md
+++ b/docs/en/tasks/text_preprocessing.md
@@ -11,7 +11,7 @@ sidebar:
   nav: sparknlp  
 ---
 
-**Text Preprocessing** is the foundational task of cleaning and transforming raw text data into a structured format that can be used in NLP tasks. It involves a series of steps to normalize text, remove noise, and prepare it for deeper analysis. Spark NLP provides a range of tools for efficient and scalable text preprocessing.
+**Text preprocessing** is a critical step in **Natural Language Processing (NLP)** that converts raw, unstructured text into a clean and analyzable form. It typically includes operations such as **tokenization**, **lowercasing**, **stopword removal**, **lemmatization or stemming**, and **handling of punctuation or special characters**. These steps reduce noise, ensure uniformity, and improve the performance of downstream NLP models.
 
 ## Key Preprocessing Steps
 
@@ -23,123 +23,117 @@ When preprocessing text, consider the following key steps along with the recomme
 4. [`Stopword Removal:`](https://sparknlp.org/docs/en/annotators#stopwordscleaner){:target="_blank"} Remove common, non-informative words (e.g., "the," "is," "and").
 5. [`Lemmatization:`](https://sparknlp.org/docs/en/annotators#lemmatizer){:target="_blank"} Reduce words to their base form (e.g., "running" → "run").
 
-These steps and annotators will help ensure your text data is clean, consistent, and ready for analysis.
-
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Document Assembler: Converts input text into a suitable format for NLP processing
 documentAssembler = DocumentAssembler() \
     .setInputCol("text") \
     .setOutputCol("document")
 
-# Tokenizer: Splits text into individual tokens (words)
 tokenizer = Tokenizer() \
     .setInputCols(["document"]) \
     .setOutputCol("tokens")
 
-# SpellChecker: Corrects misspelled words
 spellChecker = NorvigSweetingModel.pretrained() \
     .setInputCols(["tokens"]) \
     .setOutputCol("corrected")
 
-# Normalizer: Cleans and standardizes text data
 normalizer = Normalizer() \
     .setInputCols(["corrected"]) \
     .setOutputCol("normalized")
 
-# StopWordsCleaner: Removes stopwords
 stopwordsCleaner = StopWordsCleaner() \
     .setInputCols(["normalized"]) \
     .setOutputCol("cleanTokens")
 
-# Lemmatizer: Reduces words to their base form
 lemmatizer = LemmatizerModel.pretrained() \
     .setInputCols(["cleanTokens"]) \
     .setOutputCol("lemmas")
 
-# Pipeline: Assembles the document assembler and preprocessing stages
 pipeline = Pipeline().setStages([
     documentAssembler, tokenizer, spellChecker, normalizer, stopwordsCleaner, lemmatizer
 ])
 
-# Input Data: A small example dataset is created and converted to a DataFrame
-data = spark.createDataFrame([["Text preprocessing is essential in NLP!"]]).toDF("text")
-
-# Running the Pipeline: Fits the pipeline to the data and preprocesses the text
-result = pipeline.fit(data).transform(data)
+data = spark.createDataFrame([["Dr. Emily Johnson visited New York's Mount Sinai Hospital on September 21, 2023, to evaluate patients suffering from chronic migraines."]]).toDF("text")
 
-# Output: Displays the processed tokens and lemmas
-result.select("lemmas.result").show(truncate=False)
+model = pipeline.fit(data)
+result = model.transform(data)
 
-+----------------------------------------------------+
-|lemmas.result                                       |
-+----------------------------------------------------+
-|[text, preprocess, essential, in, NLP]              |
-+----------------------------------------------------+
 ```
 ```scala
-import com.johnsnowlabs.nlp.DocumentAssembler
+import com.johnsnowlabs.nlp.base._
 import com.johnsnowlabs.nlp.annotator._
 import org.apache.spark.ml.Pipeline
-import spark.implicits._
 
-// Document Assembler: Converts input text into a suitable format for NLP processing
 val documentAssembler = new DocumentAssembler()
   .setInputCol("text")
   .setOutputCol("document")
 
-// Tokenizer: Splits text into individual tokens (words)
 val tokenizer = new Tokenizer()
-  .setInputCols(Array("document"))
+  .setInputCols("document")
   .setOutputCol("tokens")
 
-// SpellChecker: Corrects misspelled words
 val spellChecker = NorvigSweetingModel.pretrained()
-  .setInputCols(Array("tokens"))
+  .setInputCols("tokens")
   .setOutputCol("corrected")
 
-// Normalizer: Cleans and standardizes text data
 val normalizer = new Normalizer()
-  .setInputCols(Array("corrected"))
+  .setInputCols("corrected")
   .setOutputCol("normalized")
 
-// StopWordsCleaner: Removes stopwords
 val stopwordsCleaner = new StopWordsCleaner()
-  .setInputCols(Array("normalized"))
+  .setInputCols("normalized")
   .setOutputCol("cleanTokens")
 
-// Lemmatizer: Reduces words to their base form
 val lemmatizer = LemmatizerModel.pretrained()
-  .setInputCols(Array("cleanTokens"))
+  .setInputCols("cleanTokens")
   .setOutputCol("lemmas")
 
-// Pipeline: Assembles the document assembler and preprocessing stages
 val pipeline = new Pipeline().setStages(Array(
   documentAssembler, tokenizer, spellChecker, normalizer, stopwordsCleaner, lemmatizer
 ))
 
-// Input Data: A small example dataset is created and converted to a DataFrame
-val data = Seq("Text preprocessing is essential in NLP!").toDF("text")
+val data = Seq("Dr. Emily Johnson visited New York's Mount Sinai Hospital on September 21, 2023, to evaluate patients suffering from chronic migraines.")
+  .toDF("text")
 
-// Running the Pipeline: Fits the pipeline to the data and preprocesses the text
-val result = pipeline.fit(data).transform(data)
+val model = pipeline.fit(data)
+val result = model.transform(data)
 
-// Display the results
-result.select("lemmas.result").show(false)
+```
+

 
-+----------------------------------------------------+
-|result                                              |
-+----------------------------------------------------+
-|[text, preprocess, essential, in, NLP]              |
-+----------------------------------------------------+
+

+```
++---------+---------+----------+----------+---------+
+|Token    |Corrected|Normalized|CleanToken|Lemma    |
++---------+---------+----------+----------+---------+
+|Dr       |Dr       |Dr        |Dr        |Dr       |
+|.        |.        |Emily     |Emily     |Emily    |
+|Emily    |Emily    |Johnson   |Johnson   |Johnson  |
+|Johnson  |Johnson  |visited   |visited   |visit    |
+|visited  |visited  |New       |New       |New      |
+|New      |New      |Yorks     |Yorks     |Yorks    |
+|York's   |Yorks    |Mount     |Mount     |Mount    |
+|Mount    |Mount    |Sinai     |Sinai     |Sinai    |
+|Sinai    |Sinai    |Hospital  |Hospital  |Hospital |
+|Hospital |Hospital |on        |September |September|
+|on       |on       |September |evaluate  |evaluate |
+|September|September|to        |patients  |patient  |
+|21       |21       |evaluate  |suffering |suffer   |
+|,        |,        |patients  |chronic   |chronic  |
+|2023     |2023     |suffering |migraines |migraine |
+|,        |,        |from      |NULL      |NULL     |
+|to       |to       |chronic   |NULL      |NULL     |
+|evaluate |evaluate |migraines |NULL      |NULL     |
+|patients |patients |NULL      |NULL      |NULL     |
+|suffering|suffering|NULL      |NULL      |NULL     |
++---------+---------+----------+----------+---------+
 ```
 

 
@@ -147,8 +141,8 @@ result.select("lemmas.result").show(false)
 
 If you want to see text preprocessing in real-time, check out our interactive demos:
 
-- **[Text Preprocessing with Spark NLP](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-text-preprocessing){:target="_blank"}** – Explore how Spark NLP preprocesses raw text data.
-- **[Stopwords Removing with Spark NLP](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-stop-words-removal){:target="_blank"}** – Explore how Spark NLP removes stop words from text.
+- **[Text Preprocessing with Spark NLP](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-text-preprocessing){:target="_blank"}**
+- **[Stopwords Removing with Spark NLP](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-stop-words-removal){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/token_classification.md b/docs/en/tasks/token_classification.md
index b2703e057f50a8..22d1e3cf7a6068 100644
--- a/docs/en/tasks/token_classification.md
+++ b/docs/en/tasks/token_classification.md
@@ -11,14 +11,7 @@ sidebar:
   nav: sparknlp  
 ---
 
-**Token classification** is the task of assigning a **label** to each token (word or sub-word) in a given text sequence. It is fundamental in various *natural language processing (NLP)* tasks like named entity recognition (NER), part-of-speech tagging (POS), and more. Spark NLP provides state of the art solutions to tackle token classification challenges effectively, helping you analyze and label individual tokens in a document.
-
-Token classification involves processing text at a granular level, labeling each token for its role or entity. Typical use cases include:
-
-- **Named Entity Recognition (NER):** Identifying proper names, locations, organizations, etc., within text.
-- **Part-of-Speech Tagging (POS):** Labeling each token with its grammatical category (e.g., noun, verb, adjective).
-
-By utilizing token classification, organizations can enhance their ability to extract detailed insights from text data, enabling applications like information extraction, text annotation, and more.
+**Token classification** is a natural language understanding task where labels are assigned to individual tokens in a text. Common subtasks include **Named Entity Recognition (NER)** and **Part-of-Speech (PoS)** tagging. For example, NER models can be trained to detect entities like dates, people, and locations, while PoS tagging identifies whether a word functions as a noun, verb, punctuation mark, or another grammatical category.
 
 

   
@@ -26,11 +19,9 @@ By utilizing token classification, organizations can enhance their ability to ex
 
 ## Picking a Model
 
-When selecting a model for token classification, it's important to consider various factors that impact performance. First, analyze the **type of entities or tags** you want to classify (e.g., named entities, parts of speech). Determine if your task requires **fine-grained tagging** (such as multiple types of named entities) or a simpler tag set.
+When picking a model for token classification, start with the type of task you need—such as **Named Entity Recognition (NER)** for tagging names of people, places, or organizations, **Part-of-Speech (POS) tagging** for grammatical structure, or **slot filling** in chatbots. For small or less complex datasets, lighter models like **DistilBERT** or **pretrained pipelines** can give fast and practical results. If you have more data or need higher accuracy, larger models like **BERT**, **RoBERTa**, or **XLM-R** are strong baselines, and domain-specialized versions like **BioBERT** (for biomedical text) or **Legal-BERT** (for legal text) often perform best in their fields. Keep in mind trade-offs: smaller models are faster and easier to deploy, while larger transformers provide richer context understanding but come with higher compute costs.
 
-Next, assess the **complexity of your data**—does it involve formal text like news articles, or informal text like social media posts? **Model performance metrics** (e.g., precision, recall, F1 score) are also key to determining whether a model is suitable. Lastly, evaluate your **computational resources**, as more complex models like BERT may require greater memory and processing power.
-
-You can explore and select models for your token classification tasks at [Spark NLP Models](https://sparknlp.org/models), where you'll find various models for specific datasets and challenges.
+You can explore and select models for your token classification tasks at [Spark NLP Models](https://sparknlp.org/models)
 
 #### Recommended Models for Specific Token Classification Tasks
 
@@ -40,59 +31,41 @@ You can explore and select models for your token classification tasks at [Spark
 
 If existing models do not meet your requirements, you can train your own custom model using the [Spark NLP Training Documentation](https://sparknlp.org/docs/en/training).
 
-By selecting the appropriate model, you can optimize token classification performance for your specific NLP tasks.
-
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Assembling the document from the input text
 documentAssembler = DocumentAssembler() \
     .setInputCol("text") \
     .setOutputCol("document")
 
-# Tokenizing the text
 tokenizer = Tokenizer() \
     .setInputCols(["document"]) \
     .setOutputCol("token")
 
-# Loading a pre-trained sequence classification model
-# You can replace `BertForTokenClassification.pretrained()` with your selected model and the transformer it's based on
-# For example: XlmRoBertaForTokenClassification.pretrained("xlmroberta_ner_large_finetuned_conll03_english","xx")
 tokenClassifier = BertForTokenClassification.pretrained() \
     .setInputCols(["token", "document"]) \
     .setOutputCol("label") \
     .setCaseSensitive(True)
 
-# Defining the pipeline with document assembler, tokenizer, and classifier
 pipeline = Pipeline().setStages([
     documentAssembler,
     tokenizer,
     tokenClassifier
 ])
 
-# Creating a sample DataFrame
 data = spark.createDataFrame([["John Lenon was born in London and lived in Paris. My name is Sarah and I live in London"]]).toDF("text")
 
-# Fitting the pipeline and transforming the data
-result = pipeline.fit(data).transform(data)
+model = pipeline.fit(data)
+result = model.transform(data)
 
-# Showing the results
 result.select("label.result").show(truncate=False)
 
-
 ```
 
 ```scala
@@ -101,52 +74,42 @@ import com.johnsnowlabs.nlp.base._
 import com.johnsnowlabs.nlp.annotator._
 import org.apache.spark.ml.Pipeline
 
-// Step 1: Assembling the document from the input text
-// Converts the input 'text' column into a 'document' column, required for NLP tasks
 val documentAssembler = new DocumentAssembler()
   .setInputCol("text")
   .setOutputCol("document")
 
-// Step 2: Tokenizing the text
-// Splits the 'document' column into tokens (words), creating the 'token' column
 val tokenizer = new Tokenizer()
   .setInputCols("document")
   .setOutputCol("token")
 
-// Step 3: Loading a pre-trained BERT model for token classification
-// Applies a pre-trained BERT model for Named Entity Recognition (NER) to classify tokens
-// `BertForTokenClassification.pretrained()` loads the model, and `setInputCols` defines the input columns
 val tokenClassifier = BertForTokenClassification.pretrained()
   .setInputCols("token", "document")
   .setOutputCol("label")
   .setCaseSensitive(true)
 
-// Step 4: Defining the pipeline
-// The pipeline stages are document assembler, tokenizer, and token classifier
 val pipeline = new Pipeline().setStages(Array(
   documentAssembler,
   tokenizer,
   tokenClassifier
 ))
 
-// Step 5: Creating a sample DataFrame
-// Creates a DataFrame with a sample sentence that will be processed by the pipeline
 val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
 
-// Step 6: Fitting the pipeline and transforming the data
-// The pipeline is fitted on the input data, then it performs the transformation to generate token labels
-val result = pipeline.fit(data).transform(data)
+val model = pipeline.fit(data)
+val result = model.transform(data)
 
-// Step 7: Showing the results
-// Displays the 'label.result' column, which contains the Named Entity Recognition (NER) labels for each token
 result.select("label.result").show(false)
 
-// Output:
-// +------------------------------------------------------------------------------------+
-// |result                                                                              |
-// +------------------------------------------------------------------------------------+
-// |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
-// +------------------------------------------------------------------------------------+
+```
+

+
+

+```
++------------------------------------------------------------------------------------+
+|result                                                                              |
++------------------------------------------------------------------------------------+
+|[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
++------------------------------------------------------------------------------------+
 ```
 

 
@@ -154,10 +117,10 @@ result.select("label.result").show(false)
 
 If you want to see the outputs of text classification models in real time, visit our interactive demos:
 
-- **[BERT Annotators Demo](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}** – A live demo where you can try your inputs on classification models on the go.
-- **[Named Entity Recognition (NER)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-named-entity-recognition){:target="_blank"}** – A live demo where you can try your inputs on NER models on the go.
-- **[POS Tagging](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-POS-tagging){:target="_blank"}** – A live demo where you can try your inputs on preception models on the go.
-- **[Recognize Entities - Live Demos & Notebooks](https://sparknlp.org/recognize_entitie){:target="_blank"}** – An interactive demo for Recognizing Entities in text
+- **[Recognize Entities - Live Demos & Notebooks](https://sparknlp.org/recognize_entitie){:target="_blank"}**
+- **[BERT Annotators Demo](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}**
+- **[Named Entity Recognition (NER)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-named-entity-recognition){:target="_blank"}**
+- **[POS Tagging](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-POS-tagging){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/translation.md b/docs/en/tasks/translation.md
index ad379da7ce5101..bfa53880bfe5d9 100644
--- a/docs/en/tasks/translation.md
+++ b/docs/en/tasks/translation.md
@@ -11,43 +11,39 @@ sidebar:
   nav: sparknlp  
 ---
 
-**Translation** is the task of converting text from one language into another. This is essential for multilingual applications such as content localization, cross-language communication, and more. Spark NLP offers advanced translation models that provide high-quality translations between multiple languages.
+**Translation** is a natural language processing task where models convert text from one language into another while preserving its meaning, grammar, and context. For example, given the input *“My name is Omar and I live in Zürich”*, a translation model might output *“Mein Name ist Omar und ich wohne in Zürich”*. Modern translation models, especially **multilingual neural models** like **mBART**, can handle a wide variety of language pairs and can also be fine-tuned on custom data to improve accuracy for specific domains or dialects.
 
-Translation models process input text in the source language and generate a corresponding translation in the target language. Common use cases include:
+Translation models are widely used to build **multilingual conversational agents** and cross-lingual applications. They can either translate datasets of user intents and responses to train a new model in the target language or translate live user inputs and chatbot outputs for real-time interaction. These capabilities make translation essential for **global communication, content localization, cross-border business, and international customer support**, enabling systems to operate seamlessly across multiple languages.
 
-- **Cross-Language Communication:** Enabling communication across different languages for global teams.
-- **Document Translation:** Translating long-form content such as reports, articles, or manuals.
+## Picking a Model  
 
-By using Spark NLP translation models, you can build scalable translation systems to meet your multilingual needs efficiently and accurately.
+The choice of model for translation depends on the languages, domain, and whether real-time or batch translation is required. For **general-purpose multilingual translation**, encoder–decoder architectures like **mBART**, **M2M100**, and **MarianMT** perform well across a wide range of language pairs. For **high-quality domain-specific translation**, fine-tuned versions of these models can be used, such as models trained on legal, medical, or technical corpora. **Lightweight or faster models** like **DistilMarianMT** or distilled versions of **mBART** are suitable for real-time applications or deployment in resource-constrained environments. Finally, when **rare or low-resource languages** are involved, models like **NLLB-200** or language-adapted versions of **M2M100** provide improved coverage and accuracy.
 
-## Picking a Model
+#### Recommended Models for Translation Tasks
 
-When choosing a translation model, consider factors such as the **source and target languages** and the **size of the input text**. Some models may specialize in specific language pairs or offer better performance for certain types of text (e.g., formal versus informal content). Evaluate whether you need **document-level translation** or **sentence-level translation** based on the use case.
+- **General-Purpose Multilingual Translation:** Models such as [`mbart-large-50-many-to-many-mmt`](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt){:target="_blank"}, [`m2m100_418M`](https://sparknlp.org/2024/05/19/m2m100_418M_xx.html){:target="_blank"}, and [`Helsinki-NLP/opus-mt`](https://sparknlp.org/models?q=Helsinki-NLP%2Fopus-mt&type=model&sort=downloads&annotator=MarianTransformer){:target="_blank"} handle a wide variety of language pairs effectively.  
 
-Explore the available translation models at [Spark NLP Models](https://sparknlp.org/models) to find the one that best suits your translation tasks.
+- **Domain-Specific Translation:** For legal, medical, technical, or other specialized texts, fine-tuned variants of **mBART**, **M2M100**, or **MarianMT** trained on domain-specific corpora provide higher accuracy.  
 
-#### Recommended Models for Translation Tasks
+- **Lightweight or Real-Time Translation:** Distilled or smaller models like [`Helsinki-NLP/opus-mt`](https://sparknlp.org/models?q=Helsinki-NLP%2Fopus-mt&type=model&sort=downloads&annotator=MarianTransformer){:target="_blank"} and distilled **mBART** versions are optimized for low-latency, resource-constrained deployment.  
 
-- **General Translation:** Consider models such as [`t5_base`](https://sparknlp.org/2021/01/08/t5_base_en.html){:target="_blank"} and [`m2m100_418M`](https://sparknlp.org/2024/05/19/m2m100_418M_xx.html){:target="_blank"} you can also consider searching models with the [`Marian Transformer`](https://sparknlp.org/models?annotator=MarianTransformer){:target="_blank"} Annotator class for translating between non-english languages.
+- **Low-Resource Languages:** Models such as [`NLLB-200`](https://sparknlp.org/2024/11/27/nllb_distilled_600M_8int_xx.html){:target="_blank"} or language-adapted versions of **M2M100** are recommended for improved performance on rare or low-resource language pairs.  
 
-Selecting the appropriate model will ensure you produce accurate and fluent translations, tailored to your specific language pair and domain.
+Explore the available translation models at [Spark NLP Models](https://sparknlp.org/models) to find the one that best suits your translation tasks.
 
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Document Assembler: Converts input text into a suitable format for NLP processing
 documentAssembler = DocumentAssembler() \
     .setInputCol("text") \
     .setOutputCol("documents")
 
-# M2M100 Transformer: Loads the pretrained translation model for English to French
 m2m100 = M2M100Transformer.pretrained("m2m100_418M") \
     .setInputCols(["documents"]) \
     .setMaxOutputLength(50) \
@@ -55,37 +51,30 @@ m2m100 = M2M100Transformer.pretrained("m2m100_418M") \
     .setSrcLang("zh") \   # Source language: Chinese
     .setTgtLang("en")     # Target language: English
 
-# Pipeline: Assembles the document assembler and the M2M100 translation model
-pipeline = Pipeline().setStages([documentAssembler, m2m100])
+pipeline = Pipeline().setStages([
+  documentAssembler, 
+  m2m100
+])
 
-# Input Data: A small example dataset is created and converted to a DataFrame
 data = spark.createDataFrame([["生活就像一盒巧克力。"]]).toDF("text")
 
-# Running the Pipeline: Fits the pipeline to the data and generates translations
-result = pipeline.fit(data).transform(data)
+model = pipeline.fit(data)
+result = model.transform(data)
 
-# Output: Displays the translated result
 result.select("summaries.generation").show(truncate=False)
 
-+-------------------------------------------------------------------------------------------+
-|result                                                                                     |
-+-------------------------------------------------------------------------------------------+
-|[ Life is like a box of chocolate.]                                                        |
-+-------------------------------------------------------------------------------------------+
 ```
 
 ```scala
 import spark.implicits._
-import com.johnsnowlabs.nlp.base.DocumentAssembler
-import com.johnsnowlabs.nlp.annotators.seq2seq.M2M100Transformer
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotators._
 import org.apache.spark.ml.Pipeline
 
-// Document Assembler: Converts input text into a suitable format for NLP processing
 val documentAssembler = new DocumentAssembler()
   .setInputCol("text")
   .setOutputCol("documents")
 
-// M2M100 Transformer: Loads the pretrained translation model for Chinese to English
 val m2m100 = M2M100Transformer.pretrained("m2m100_418M")
   .setInputCols(Array("documents"))
   .setSrcLang("zh")          // Source language: Chinese
@@ -94,18 +83,23 @@ val m2m100 = M2M100Transformer.pretrained("m2m100_418M")
   .setDoSample(false)        
   .setOutputCol("generation")
 
-// Pipeline: Assembles the document assembler and the M2M100 translation model
-val pipeline = new Pipeline().setStages(Array(documentAssembler, m2m100))
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler, 
+  m2m100
+))
 
-// Input Data: A small example dataset is created and converted to a DataFrame
 val data = Seq("生活就像一盒巧克力。").toDF("text")
 
-// Running the Pipeline: Fits the pipeline to the data and generates translations
-val result = pipeline.fit(data).transform(data)
+val model = pipeline.fit(data)
+val result = model.transform(data)
 
-// Output: Displays the translated result
 result.select("generation.result").show(truncate = false)
 
+```
+

+
+

+```
 +-------------------------------------------------------------------------------------------+
 |result                                                                                     |
 +-------------------------------------------------------------------------------------------+
@@ -118,9 +112,9 @@ result.select("generation.result").show(truncate = false)
 
 If you want to see the outputs of text generation models in real time, visit our interactive demos:
 
-- **[Text-To-Text Transfer Transformer (Google T5)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-t5){:target="_blank"}** – T5 performs text tasks like summarization and translation.
-- **[Multilingual Text Translation with MarianMT](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-MarianMT){:target="_blank"}** – Translates text between multiple languages.
-- **[M2M100 Multilingual Translation Model](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-M2M100){:target="_blank"}** – Translates text between multiple languages.
+- **[Text-To-Text Transfer Transformer (Google T5)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-t5){:target="_blank"}**
+- **[Multilingual Text Translation with MarianMT](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-MarianMT){:target="_blank"}**
+- **[M2M100 Multilingual Translation Model](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-M2M100){:target="_blank"}**
 
 ## Useful Resources
 
diff --git a/docs/en/tasks/zero_shot_classification.md b/docs/en/tasks/zero_shot_classification.md
index 221493b8741048..df15191446f5b7 100644
--- a/docs/en/tasks/zero_shot_classification.md
+++ b/docs/en/tasks/zero_shot_classification.md
@@ -11,128 +11,109 @@ sidebar:
   nav: sparknlp
 ---
 
-**Zero-Shot Classification** is a method of classifying unseen labels in text without needing any prior training data for those labels. This technique is especially useful for scenarios where pre-defined categories are not available, allowing for flexibility in categorizing text based on descriptions of labels alone. Spark NLP offers state-of-the-art solutions for zero-shot classification, enabling users to classify texts into various categories even when no labeled data is available.
+**Zero-Shot Text Classification** is a natural language processing task where a model can assign text to *categories it was never explicitly trained on*. Instead of relying only on predefined classes, it uses general language understanding to match new inputs to a list of **candidate labels** given at inference time. For example, given the text *“Dune is the best movie ever”* and labels *CINEMA, ART, MUSIC*, a zero-shot model might output *CINEMA (0.900)*, *ART (0.100)*, and *MUSIC (0.000)*. This approach is especially useful when labeled data is limited or when new categories appear after training.
 
-Zero-shot classification processes text at a broader level, where the system predicts the most relevant labels based on their descriptions. Typical use cases include:
-
-- **Text Categorization:** Automatically classifying text into a set of predefined or custom categories based on label descriptions.
-
-By leveraging zero-shot classification, organizations can classify large volumes of text data without the need to curate annotated datasets for each possible label, significantly reducing manual efforts in text annotation and data preparation.
+Zero-shot classification works by pairing a **natural language prompt** with candidate labels, without needing any examples of the task itself. This differs from single-shot or few-shot classification, which provide one or a few examples of the task. The ability to perform zero-, single-, and few-shot tasks emerges in **large language models** (typically with 100M+ parameters), with performance improving as models scale. Thanks to this, zero-shot classification is widely used for tasks like **sentiment analysis, topic labeling, and intent detection**, making it a flexible solution for real-world applications without retraining.
 
 ## Picking a Model
 
-When selecting a model for zero-shot classification, it is important to consider several factors that impact performance. First, analyze the **range of labels or categories** you want to classify. Zero-shot classification is versatile, but choosing models trained on broader datasets often yields better results.
-
-Next, consider the **complexity of your text**. Is it formal or informal? Does it involve domain-specific language such as legal or healthcare text? **Performance metrics** (e.g., accuracy, precision, recall) help assess whether a model fits your requirements. Additionally, ensure you evaluate your **computational resources**, as larger models, like those based on transformer architectures, may require significant memory and processing power.
-
-You can explore and select models for your zero-shot classification tasks at [Spark NLP Models](https://sparknlp.org/models), where you'll find a variety of models for specific datasets and classification challenges.
+When picking a model for zero-shot classification, first check if a task-specific model already exists—because trained models usually outperform zero-shot ones. Smaller models like **DistilBART-MNLI** are fast and efficient, while larger ones such as **RoBERTa-MNLI** or **GPT-based classifiers** deliver higher accuracy and handle nuance better. In specialized domains, domain-adapted or prompt-tuned models are usually worth exploring.
 
 #### Recommended Models for Zero-Shot Classification
 
 - **Zero-Shot Text Classification:** Consider using models like [`bart-large-mnli`](https://sparknlp.org/2024/08/27/bart_large_zero_shot_classifier_mnli_en.html){:target="_blank"} for general-purpose multilingual text data classification across various domains.
 - **Zero-Shot Named Entity Recognition (NER):** Use models like [`zero_shot_ner_roberta`](https://sparknlp.org/2023/02/08/zero_shot_ner_roberta_en.html){:target="_blank"} for identifying entities across various domains and languages without requiring task-specific labeled data.
 
-If pre-trained models don't match your exact needs, you can train your own custom model using the [Spark NLP Training Documentation](https://sparknlp.org/docs/en/training).
-
-By selecting the appropriate zero-shot classification model, you can expand your ability to analyze text data without predefined labels, providing flexibility for dynamic and evolving classification tasks.
+You can explore and select models for your zero-shot classification tasks at [Spark NLP Models](https://sparknlp.org/models)
 
 ## How to use
 
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# 1. Document Assembler: Converts raw input text into a document format.
 documentAssembler = DocumentAssembler() \
     .setInputCol("text") \
     .setOutputCol("document")
 
-# 2. Tokenizer: Splits the document into individual tokens (words).
 tokenizer = Tokenizer() \
     .setInputCols(["document"]) \
     .setOutputCol("token")
 
-# 3. Pre-trained Sequence Classifier (Zero-Shot Classification): Loads a pre-trained BART model for zero-shot classification.
-sequenceClassifier = BartForZeroShotClassification.pretrained() \
+sequenceClassifier = BertForZeroShotClassification.pretrained() \
     .setInputCols(["token", "document"]) \
     .setOutputCol("label") \
-    .setCaseSensitive(True)
+    .setCandidateLabels(["technology", "health", "finance"])
 
-# 4. Pipeline: Defines a pipeline with three stages - document assembler, tokenizer, and zero-shot classifier.
 pipeline = Pipeline().setStages([
     documentAssembler,
     tokenizer,
     sequenceClassifier
 ])
 
-# 5. Sample Data: Creating a DataFrame with sample text data to test zero-shot classification.
-data = spark.createDataFrame([["I loved this movie when I was a child.", "It was pretty boring."]]).toDF("text")
+data = spark.createDataFrame([
+  ("The new iPhone release has sparked debates about innovation in technology.",),
+  ("Doctors recommend regular exercise to maintain good health.",),
+  ("The stock market experienced strong gains this week as investor confidence boosted financial markets.",)
+], ["text"])
 
-# 6. Fit and Transform: Fits the pipeline to the data and applies the model for classification.
-result = pipeline.fit(data).transform(data)
+model = pipeline.fit(data)
+result = model.transform(data)
 
-# 7. Displaying Results: Shows the classification labels assigned to each text (e.g., positive or negative sentiment).
-result.select("label.result").show(truncate=False)
+result.select("text", "label.result").show(truncate=False)
 
-
 ```
 
 ```scala
-import spark.implicits._
 import com.johnsnowlabs.nlp.base._
 import com.johnsnowlabs.nlp.annotator._
 import org.apache.spark.ml.Pipeline
 
-// Assembling the document from the input text
 val documentAssembler = new DocumentAssembler()
   .setInputCol("text")
   .setOutputCol("document")
 
-// Tokenizing the text
 val tokenizer = new Tokenizer()
   .setInputCols("document")
   .setOutputCol("token")
 
-// Loading the pre-trained zero-shot classification model (BERT)
 val sequenceClassifier = BertForZeroShotClassification.pretrained()
-  .setInputCols("token", "document")
+  .setInputCols(Array("token", "document"))
   .setOutputCol("label")
-  .setCaseSensitive(true)
+  .setCandidateLabels(Array("technology", "health", "finance"))
 
-// Creating a pipeline with document assembler, tokenizer, and classifier
 val pipeline = new Pipeline().setStages(Array(
   documentAssembler,
   tokenizer,
   sequenceClassifier
 ))
 
-// Creating a sample DataFrame
-val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
+val data = Seq(
+  ("The new iPhone release has sparked debates about innovation in technology."),
+  ("Doctors recommend regular exercise to maintain good health."),
+  ("The stock market experienced strong gains this week as investor confidence boosted financial markets.")
+).toDF("text")
 
-// Fitting the pipeline and transforming the data
-val result = pipeline.fit(data).transform(data)
+val model = pipeline.fit(data)
+val result = model.transform(data)
 
-// Showing the results
-result.select("label.result").show(false)
+result.select("text", "label.result").show(false)
 
-// Sample Output:
-// +------+
-// |result|
-// +------+
-// |[pos] |
-// |[neg] |
-// +------+
+```
+

+
+

+```
++-----------------------------------------------------------------------------------------------------+------------+
+|text                                                                                                 |result      |
++-----------------------------------------------------------------------------------------------------+------------+
+|The new iPhone release has sparked debates about innovation in technology.                           |[technology]|
+|Doctors recommend regular exercise to maintain good health.                                          |[health]    |
+|The stock market experienced strong gains this week as investor confidence boosted financial markets.|[finance]   |
++-----------------------------------------------------------------------------------------------------+------------+
 ```
 

 
@@ -140,13 +121,11 @@ result.select("label.result").show(false)
 
 If you want to see the outputs of text classification models in real time, visit our interactive demos:
 
-- **[BERT Annotators Demo](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}** – A live demo where you can try your labels and inputs on zero shot classification models on the go.
-- **[Zero-Shot Named Entity Recognition (NER)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-Zero-Shot-NER){:target="_blank"}** – A live demo where you can try your labels and inputs on zero shot classification models on the go.
+- **[BERT Annotators Demo](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-bert-annotators){:target="_blank"}**
+- **[Zero-Shot Named Entity Recognition (NER)](https://huggingface.co/spaces/abdullahmubeen10/sparknlp-Zero-Shot-NER){:target="_blank"}**
 
 ## Useful Resources
 
-Want to dive deeper into text classification with Spark NLP? Here are some curated resources to help you get started and explore further:
-
 **Notebooks**
 - *[Zero-Shot Text Classification in Spark NLP](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Public/5.4_ZeroShot_Text_Classification.ipynb){:target="_blank"}*
 - *[Zero-Shot for Named Entity Recognition](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Public/4.2_ZeroShot_NER.ipynb){:target="_blank"}*
diff --git a/docs/en/tasks/zero_shot_image_classification.md b/docs/en/tasks/zero_shot_image_classification.md
index d3744a2de7e463..72c088bf3d01df 100644
--- a/docs/en/tasks/zero_shot_image_classification.md
+++ b/docs/en/tasks/zero_shot_image_classification.md
@@ -11,29 +11,15 @@ sidebar:
   nav: sparknlp
 ---
 
-**Zero-shot image classification** is a technique in computer vision where a model can classify images into categories that it has never seen before during training. This is achieved by leveraging semantic relationships between the image data and textual descriptions of classes, enabling models to predict labels without specific training on each category.
+Zero-shot image classification is a technique that allows a model to recognize and categorize images into classes it has **never seen during training**. Instead of relying on labeled examples for every category, the model understands the relationship between images and text descriptions, enabling it to generalize to new concepts. For example, even if the model has never been trained on the class "red panda," it can still correctly identify it by understanding how the phrase "red panda" relates to its visual features.
 
-This task is particularly useful for scenarios where obtaining labeled data for every possible category is challenging or expensive, such as real-world applications in e-commerce, media, or biology. Zero-shot classification can help scale image recognition systems without constantly retraining them for new categories.
-
-## How Zero-shot Image Classification Works
-
-The key idea behind zero-shot learning is the generalization capability of models. Instead of being restricted to the labels encountered during training, the model uses external knowledge—typically in the form of text or word embeddings—to make predictions about new classes.
-
-In Spark NLP, zero-shot image classification leverages models like CLIP (Contrastive Language–Image Pretraining), which are trained to understand both visual and textual data. These models align the visual representations of images with the semantic representations of text, allowing them to match unseen image categories based on their descriptions.
-
-Some common use cases include:
-
-- **Classifying new product images** in an e-commerce platform without retraining the model for every new product.
-- **Detecting rare or new species of animals** using images in wildlife research.
-- **Media categorization** for content recommendation engines where new labels continuously emerge.
+This capability is made possible by **multimodal models** such as **CLIP** (Contrastive Language–Image Pretraining), which learn to connect images and textual descriptions through joint training on large image–text pairs. When given a new image and a set of text labels, the model compares how closely the image aligns with each label’s description and assigns the most relevant one.
 
 ## Picking a Model
 
-When choosing a model for zero-shot image classification, you need to consider several factors:
+When picking a model for zero-shot image classification, focus on those that are designed to understand both **images and text** in a shared representation space. The most popular and effective option is **CLIP** (Contrastive Language–Image Pretraining), developed by OpenAI. CLIP connects images with natural language descriptions, allowing it to recognize objects and concepts it was never directly trained on.  
 
-- **Text and Image Alignment:** Choose models that are good at matching visual features to text-based descriptions.
-- **Task Complexity:** Depending on the complexity of the task, a larger pre-trained model like CLIP or a fine-tuned ViT model might perform better.
-- **Efficiency:** While zero-shot classification saves time by avoiding retraining, some models are more resource-intensive than others. Make sure the model is efficient enough for your computational setup.
+Other strong choices include **ALIGN**, **BLIP**, and **BLIP-2**, which build on similar multimodal principles and often deliver more fluent and adaptable results. These models differ mainly in how they pair vision and text encoders—some use **Vision Transformers (ViT)** or **ResNet** for image understanding, and **Transformer-based text encoders** for processing textual labels or prompts.  
 
 You can explore a variety of pre-trained zero-shot models on the [Spark NLP Models](https://sparknlp.org/models){:target="_blank"}, where models suited for different tasks and datasets are available.
 
@@ -45,23 +31,19 @@ You can explore a variety of pre-trained zero-shot models on the [Spark NLP Mode
 

 {% include programmingLanguageSelectScalaPython.html %}
 ```python
-import sparknlp
 from sparknlp.base import *
 from sparknlp.annotator import *
 from pyspark.ml import Pipeline
 
-# Loading images into a Spark DataFrame, with an option to discard invalid images
 imageDF = spark.read \
     .format("image") \
     .option("dropInvalid", value=True) \
     .load("src/test/resources/image/")
 
-# Assembling image data using the ImageAssembler, preparing the input images for further processing
 imageAssembler = ImageAssembler() \
     .setInputCol("image") \
     .setOutputCol("image_assembler")
 
-# Defining candidate labels for zero-shot classification
 candidateLabels = [
     "a photo of a bird",
     "a photo of a cat",
@@ -74,56 +56,37 @@ candidateLabels = [
     "a photo of an ox"
 ]
 
-# Initializing the CLIPForZeroShotClassification model
 imageClassifier = CLIPForZeroShotClassification \
     .pretrained("clip_vit_large_patch14", "en") \
     .setInputCols(["image_assembler"]) \
     .setOutputCol("label") \
     .setCandidateLabels(candidateLabels)
 
-# Defining a Spark ML pipeline with two stages: the ImageAssembler and the CLIP image classifier
 pipeline = Pipeline().setStages([imageAssembler, imageClassifier])
 
-# Fitting the pipeline on the image DataFrame and transforming the data to apply classification
-pipelineDF = pipeline.fit(imageDF).transform(imageDF)
+model = pipeline.fit(imageDF)
+result = model.transform(imageDF)
 
-# Selecting the image file name and the predicted label result, displaying the output in a readable format
-pipelineDF \
+result \
   .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "label.result") \
   .show(truncate=False)
 
-+-----------------+-----------------------+
-|image_name       |result                 |
-+-----------------+-----------------------+
-|palace.JPEG      |[a photo of a room]    |
-|egyptian_cat.jpeg|[a photo of a cat]     |
-|hippopotamus.JPEG|[a photo of a hippo]   |
-|hen.JPEG         |[a photo of a hen]     |
-|ostrich.JPEG     |[a photo of an ostrich]|
-|junco.JPEG       |[a photo of a bird]    |
-|bluetick.jpg     |[a photo of a dog]     |
-|chihuahua.jpg    |[a photo of a dog]     |
-|tractor.JPEG     |[a photo of a tractor] |
-|ox.JPEG          |[a photo of an ox]     |
-+-----------------+-----------------------+
 ```
 ```scala
-import com.johnsnowlabs.nlp.ImageAssembler
-import com.johnsnowlabs.nlp.annotator._
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotators._
 import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions._
 
-// Loading image data into a Spark DataFrame, removing any invalid images
-val imageDF = ResourceHelper.spark.read
+val imageDF = spark.read
   .format("image")
-  .option("dropInvalid", value = true)
+  .option("dropInvalid", true)
   .load("src/test/resources/image/")
 
-// Assembling the images with the ImageAssembler, which prepares image data for processing
-val imageAssembler: ImageAssembler = new ImageAssembler()
+val imageAssembler = new ImageAssembler()
   .setInputCol("image")
   .setOutputCol("image_assembler")
 
-// Defining an array of candidate labels for zero-shot image classification
 val candidateLabels = Array(
   "a photo of a bird",
   "a photo of a cat",
@@ -136,23 +99,26 @@ val candidateLabels = Array(
   "a photo of an ox"
 )
 
-// Initializing the CLIPForZeroShotClassification model, setting input and output columns
-// The model classifies images based on comparison to the candidate labels
 val imageClassifier = CLIPForZeroShotClassification
-  .pretrained()  // Loading a pretrained CLIP model 
+  .pretrained("clip_vit_large_patch14", "en")
   .setInputCols("image_assembler")
   .setOutputCol("label")
   .setCandidateLabels(candidateLabels)
 
-// Creating and running the Spark ML pipeline with the image assembler and classifier
-val pipeline =
-  new Pipeline().setStages(Array(imageAssembler, imageClassifier)).fit(imageDF).transform(imageDF)
+val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
+
+val model = pipeline.fit(imageDF)
+val result = model.transform(imageDF)
 
-// Selecting and displaying the image file name and classification result
-pipeline
-  .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "label.result")  // Extracting image names and their classification labels
-  .show(truncate = false)
+result
+  .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "label.result")
+  .show(false)
 
+```
+

+
+

+```
 +-----------------+-----------------------+
 |image_name       |result                 |
 +-----------------+-----------------------+
@@ -178,9 +144,5 @@ Explore zero-shot image classification with our interactive demos:
 
 ## Useful Resources
 
-Learn zero-shot image classification with Spark NLP:
-
 **Notebooks**
 - *[CLIP Classification Notebook](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/image/CLIPForZeroShotClassification.ipynb){:target="_blank"}*
-
-Discover how to classify images without labeled data.
diff --git a/docs/en/transformer_entries/BLIPForQuestionAnswering.md b/docs/en/transformer_entries/BLIPForQuestionAnswering.md
new file mode 100644
index 00000000000000..c758f59d86053a
--- /dev/null
+++ b/docs/en/transformer_entries/BLIPForQuestionAnswering.md
@@ -0,0 +1,133 @@
+{%- capture title -%}
+BLIPForQuestionAnswering
+{%- endcapture -%}
+
+{%- capture description -%}
+BLIP (Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation) Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text decoder. The vision encoder will encode the input image, the text encoder will encode the input question together with the encoding of the image, and the text decoder will output the answer to the question.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+```scala
+val loadModel = BLIPForQuestionAnswering.pretrained()
+  .setInputCols("image_assembler")
+  .setOutputCol("answer")
+```
+The default model is `"blip_vqa_base"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?task=Question+Answering&annotator=BLIPForQuestionAnswering).
+
+Spark NLP also supports Hugging Face transformer-based code generation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Sources** :
+
+- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086)
+- [BLIP on GitHub](https://github.com/salesforce/BLIP)
+
+**Paper abstract**
+
+*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at this https URL.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+IMAGE_ASSEMBLER
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+ANSWER
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[BLIPForQuestionAnswering](/api/com/johnsnowlabs/nlp/annotators/cv/BLIPForQuestionAnswering.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[BLIPForQuestionAnswering](/api/python/reference/autosummary/sparknlp/annotator/cv/blip_for_question_answering/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[BLIPForQuestionAnswering](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/cv/BLIPForQuestionAnswering.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import ImageAssembler
+from sparknlp.annotator import BLIPForQuestionAnswering
+from pyspark.sql.functions import lit
+from pyspark.ml import Pipeline
+
+# To proceed please create a DataFrame with two required columns:
+# - 'image': contains file paths of input images
+# - 'text' : contains the visual question to be asked about each image
+
+images_path = "./images/"
+image_df = spark.read.format("image").load(images_path)
+
+question_prompt = "What's this picture about?"
+input_df = image_df.withColumn("text", lit(question_prompt))
+
+image_assembler = ImageAssembler() \
+    .setInputCol("image") \
+    .setOutputCol("image_assembler")
+
+vqa_model = BLIPForQuestionAnswering.pretrained() \
+    .setInputCols(["image_assembler", "text"]) \
+    .setOutputCol("answer") \
+    .setSize(384)
+
+pipeline = Pipeline(stages=[
+    image_assembler,
+    vqa_model
+])
+
+fitted_model = pipeline.fit(input_df)
+result_df = fitted_model.transform(input_df)
+
+result_df.select("image_assembler.origin", "answer.result").show(truncate=False)
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.ImageAssembler
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions.lit
+
+// To proceed please create a DataFrame with two required columns:
+// - 'image': contains file paths of input images
+// - 'text' : contains the visual question to be asked about each image
+
+val imagesPath = "./images/"
+val imageDF = spark.read.format("image").load(imagesPath)
+
+val questionPrompt = "What's this picture about?"
+val inputDF = imageDF.withColumn("text", lit(questionPrompt))
+
+val imageAssembler = new ImageAssembler()
+  .setInputCol("image")
+  .setOutputCol("image_assembler")
+
+val vqaModel = BLIPForQuestionAnswering.pretrained()
+  .setInputCols("image_assembler", "text")
+  .setOutputCol("answer")
+  .setSize(384)
+
+val pipeline = new Pipeline().setStages(Array(
+  imageAssembler,
+  vqaModel
+))
+
+val fittedModel = pipeline.fit(inputDF)
+val resultDF = fittedModel.transform(inputDF)
+
+resultDF.select("image_assembler.origin", "answer.result").show(false)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/CPMTransformer.md b/docs/en/transformer_entries/CPMTransformer.md
new file mode 100644
index 00000000000000..1c124f6bbf3087
--- /dev/null
+++ b/docs/en/transformer_entries/CPMTransformer.md
@@ -0,0 +1,135 @@
+{%- capture title -%}
+CPMTransformer
+{%- endcapture -%}
+
+{%- capture description -%}
+The CPM model was proposed in [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://huggingface.co/papers/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
+
+This model was contributed by [canwenxu](https://huggingface.co/canwenxu). The original implementation can be found here: [https://github.com/TsinghuaAI/CPM-Generate](TsinghuaAI/CPM-Generate on GitHub)
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+```scala
+val seq2seq = CPMTransformer.pretrained("mini_cpm_2b_8bit","xx") 
+    .setInputCols(Array("documents")) 
+    .setOutputCol("generation")
+```
+The default model is `"mini_cpm_2b_8bit"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=CPMTransformer).
+
+Spark NLP also supports Hugging Face transformer-based code generation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Sources** :
+
+- [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://huggingface.co/papers/2012.00413)
+- [CPM-Generate on GitHub](https://github.com/TsinghuaAI/CPM-Generate)
+
+**Paper abstract**
+
+*Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in the settings of few-shot (even zero-shot) learning.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[CPMTransformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/CPMTransformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[CPMTransformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/cpm_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[CPMTransformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/CPMTransformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import CPMTransformer
+from pyspark.ml import Pipeline
+
+test_data = spark.createDataFrame([
+    (1, "Leonardo Da Vinci invented the microscope?")
+], ["id", "text"])
+
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+mini_cpm = CPMTransformer.pretrained() \
+    .setInputCols(["documents"]) \
+    .setOutputCol("generation") \
+    .setMaxOutputLength(50) \
+    .setDoSample(True)
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    mini_cpm
+])
+
+model = pipeline.fit(test_data)
+results = model.transform(test_data)
+
+results.select("generation.result").show(truncate=False)
+
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|result                                                                                                                                                                                                                                                                           |
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[Leonardo Da Vinci invented the microscope?\n\n  - Leonardo da Vinci, a renowned Italian polymath, is often credited with inventing the microscope. However, this claim is not accurate.\n\n  - The microscope was actually invented by a Dutch scientist named Antonie van Leeu]|
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators.generative.CPMTransformer
+import org.apache.spark.ml.Pipeline
+
+val testData = Seq(
+  (1, "Leonardo Da Vinci invented the microscope?")
+).toDF("id", "text")
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("documents")
+
+val miniCpm = CPMTransformer.pretrained()
+  .setInputCols("documents")
+  .setOutputCol("generation")
+  .setMaxOutputLength(50)
+  .setDoSample(true)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  miniCpm
+))
+
+val model = pipeline.fit(testData)
+val results = model.transform(testData)
+
+results.select("generation.result").show(false)
+
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|result                                                                                                                                                                                                                                                                           |
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[Leonardo Da Vinci invented the microscope?\n\n  - Leonardo da Vinci, a renowned Italian polymath, is often credited with inventing the microscope. However, this claim is not accurate.\n\n  - The microscope was actually invented by a Dutch scientist named Antonie van Leeu]|
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/DeBertaForZeroShotClassification.md b/docs/en/transformer_entries/DeBertaForZeroShotClassification.md
new file mode 100644
index 00000000000000..519315d8921940
--- /dev/null
+++ b/docs/en/transformer_entries/DeBertaForZeroShotClassification.md
@@ -0,0 +1,178 @@
+{%- capture title -%}
+DeBertaForZeroShotClassification
+{%- endcapture -%}
+
+{%- capture description -%}
+The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019.
+
+It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in RoBERTa.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+```scala
+val zeroShotClassifier = DeBertaForZeroShotClassification.pretrained()
+    .setInputCols("document", "token")
+    .setOutputCol("class")
+    .setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"))
+```
+The default model is `"deberta_base_zero_shot_classifier_mnli_anli_v3"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=DeBertaForZeroShotClassification).
+
+Spark NLP also supports Hugging Face transformer-based code generation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Sources** :
+
+- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654)  
+- [DeBERTa on GitHub](https://github.com/microsoft/DeBERTa)   
+- [DeBERTa on SuperGLUE Leaderboard](https://super.gluebenchmark.com/leaderboard)
+
+**Paper abstract**
+
+*Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models' generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understanding (NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, out performing the human baseline by a decent margin (90.3 versus 89.8).*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT, TOKEN
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+CLASS
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[DeBertaForZeroShotClassification](/api/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForZeroShotClassification.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[DeBertaForZeroShotClassification](/api/python/reference/autosummary/sparknlp/annotator/classifier_dl/deberta_for_zero_shot_classification/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[DeBertaForZeroShotClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/DeBertaForZeroShotClassification.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import Tokenizer, DeBertaForZeroShotClassification
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols(["document"]) \
+    .setOutputCol("token")
+
+zero_shot_classifier = DeBertaForZeroShotClassification.pretrained() \
+    .setInputCols(['token', 'document']) \
+    .setOutputCol('class') \
+    .setCaseSensitive(True) \
+    .setMaxSentenceLength(512) \
+    .setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"])
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    tokenizer,
+    zero_shot_classifier
+])
+
+text_data = [
+    ["I have a problem with my iphone that needs to be resolved asap!!"],
+    ["Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app."],
+    ["I have a phone and I love it!"],
+    ["I really want to visit Germany and I am planning to go there next year."],
+    ["Let's watch some movies tonight! I am in the mood for a horror movie."],
+    ["Have you watched the match yesterday? It was a great game!"],
+    ["We need to harry up and get to the airport. We are going to miss our flight!"]
+]
+
+input_df = spark.createDataFrame(text_data, ["text"])
+
+model = pipeline.fit(input_df)
+result = model.transform(input_df)
+
+result.select("class.result").show(truncate=False)
+
++---------+
+|result   |
++---------+
+|[music]  |
+|[weather]|
+|[sport]  |
+|[sport]  |
+|[music]  |
+|[sport]  |
+|[weather]|
++---------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators.Tokenizer
+import com.johnsnowlabs.nlp.annotators.classifier.dl.DeBertaForZeroShotClassification
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+  .setInputCols("document")
+  .setOutputCol("token")
+
+val zeroShotClassifier = DeBertaForZeroShotClassification.pretrained()
+  .setInputCols("token", "document")
+  .setOutputCol("class")
+  .setCaseSensitive(true)
+  .setMaxSentenceLength(512)
+  .setCandidateLabels(Array(
+    "urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"
+  ))
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  tokenizer,
+  zeroShotClassifier
+))
+
+val textData = Seq(
+  "I have a problem with my iphone that needs to be resolved asap!!",
+  "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.",
+  "I have a phone and I love it!",
+  "I really want to visit Germany and I am planning to go there next year.",
+  "Let's watch some movies tonight! I am in the mood for a horror movie.",
+  "Have you watched the match yesterday? It was a great game!",
+  "We need to harry up and get to the airport. We are going to miss our flight!"
+).toDF("text")
+
+val model = pipeline.fit(textData)
+val result = model.transform(textData)
+
+result.select("class.result").show(false)
+
++---------+
+|result   |
++---------+
+|[music]  |
+|[weather]|
+|[sport]  |
+|[sport]  |
+|[music]  |
+|[sport]  |
+|[weather]|
++---------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/LLAMA2Transformer.md b/docs/en/transformer_entries/LLAMA2Transformer.md
new file mode 100644
index 00000000000000..daaf0fe8f1c3e0
--- /dev/null
+++ b/docs/en/transformer_entries/LLAMA2Transformer.md
@@ -0,0 +1,169 @@
+{%- capture title -%}
+LLAMA2Transformer
+{%- endcapture -%}
+
+{%- capture description -%}
+[Llama 2](https://huggingface.co/papers/2307.09288) is a family of large language models, Llama 2 and Llama 2-Chat, available in 7B, 13B, and 70B parameters. The Llama 2 model mostly keeps the same architecture as Llama, but it is pretrained on more tokens, doubles the context length, and uses grouped-query attention (GQA) in the 70B model to improve inference.
+
+Llama 2-Chat is trained with supervised fine-tuning (SFT), and reinforcement learning with human feedback (RLHF) - rejection sampling and proximal policy optimization (PPO) - is applied to the fine-tuned model to align the chat model with human preferences.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+```scala
+val llama2 = LLAMA2Transformer.pretrained("llama_2_7b_chat_hf_int4") 
+    .setMaxOutputLength(50) 
+    .setDoSample(False) 
+    .setInputCols(["document"]) 
+    .setOutputCol(["generation"])
+```
+The default model is `"llama_2_7b_chat_hf_int4"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=LLAMA2Transformer).
+
+Spark NLP also supports Hugging Face transformer-based code generation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resource**:
+
+- [LLaMA 2 Paper on HuggingFace](https://huggingface.co/papers/2307.09288)
+- [LLaMA 2 – Every Resource You Need (Philipp Schmid)](https://www.philschmid.de/llama-2)
+- [Meta AI "5 Steps to Getting Started with Llama 2"](https://ai.meta.com/blog/5-steps-to-getting-started-with-llama-2/)
+- [Awesome‑llama‑resources on GitHub](https://github.com/MIBlue119/awesome-llama-resources)
+- [Fine‑Tuning Llama 2: Step‑by‑Step Guide (DataCamp)](https://www.datacamp.com/tutorial/fine-tuning-llama-2)
+- [LLM Benchmarking with LLaMA2 (2025 Paper)](https://arxiv.org/abs/2503.19217)
+
+
+**Paper abstract**
+
+*In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[LLAMA2Transformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/LLAMA2Transformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[LLAMA2Transformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/llama2_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[LLAMA2Transformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/LLAMA2Transformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import LLAMA2Transformer
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+llama2 = LLAMA2Transformer.pretrained() \
+    .setInputCols(["documents"]) \
+    .setOutputCol("generation") \
+    .setMinOutputLength(50) \
+    .setMaxOutputLength(250) \
+    .setDoSample(True) \
+    .setTemperature(0.7) \
+    .setTopK(50) \
+    .setTopP(0.9) \
+    .setRepetitionPenalty(1.1) \
+    .setNoRepeatNgramSize(3) \
+    .setIgnoreTokenIds([])
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    llama2
+])
+
+prompt = spark.createDataFrame([("""
+### System:
+You are a concise assistant who explains machine learning in simple terms.
+
+### User:
+Explain the difference between supervised and unsupervised learning with examples.
+
+### Assistant:
+""",)], ["text"])
+
+model = pipeline.fit(prompt)
+results = model.transform(prompt)
+
+results.select("generation.result").show(truncate=False)
+
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|result                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[_\n### System:\nYou are a concise assistant who explains machine learning in simple terms.\n\n### User:\nExplain the difference between supervised and unsupervised learning with examples.\n\n### Assistant:\nOf course! Supervised learning is when you train a machine learning model on labeled data, where the correct output is already known. The model learns to predict the output based on the input data. For example, if you want to build a model that can recognize dogs and cats based on their pictures, you would need to provide the model with a dataset of labeled images of dogs and cat, where each image is associated with the correct label (dog or cat).\n\nOn the other hand, unsuperived learning is When you train machine learning models on unlabeled data. The goal is to identify patterns or relationships in the data without any prior knowledge of the expected output. For instance, if we want to analyze customer buying behavior, we can use unsupervise learning to identify common characteristics of customers who buy more products.\nIn summary, supervised learning relies on labeled data to train the model, while unsupervisied learning relied on unlabelled data.\nIs there anything else you would like to know?]|
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators._
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions._
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("documents")
+
+val llama2 = LLaMA2Transformer.pretrained()
+  .setInputCols("documents")
+  .setOutputCol("generation")
+  .setMinOutputLength(50)
+  .setMaxOutputLength(250)
+  .setDoSample(true)
+  .setTemperature(0.7f)
+  .setTopK(50)
+  .setTopP(0.9f)
+  .setRepetitionPenalty(1.1f)
+  .setNoRepeatNgramSize(3)
+  .setIgnoreTokenIds(Array.emptyIntArray)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  llama2
+))
+
+val prompt = Seq("""
+### System:
+You are a concise assistant who explains machine learning in simple terms.
+
+### User:
+Explain the difference between supervised and unsupervised learning with examples.
+
+### Assistant:
+""").toDF("text")
+
+val model = pipeline.fit(prompt)
+val results = model.transform(prompt)
+
+results.select("generation.result").show(false)
+
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|result                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[_\n### System:\nYou are a concise assistant who explains machine learning in simple terms.\n\n### User:\nExplain the difference between supervised and unsupervised learning with examples.\n\n### Assistant:\nOf course! Supervised learning is when you train a machine learning model on labeled data, where the correct output is already known. The model learns to predict the output based on the input data. For example, if you want to build a model that can recognize dogs and cats based on their pictures, you would need to provide the model with a dataset of labeled images of dogs and cat, where each image is associated with the correct label (dog or cat).\n\nOn the other hand, unsuperived learning is When you train machine learning models on unlabeled data. The goal is to identify patterns or relationships in the data without any prior knowledge of the expected output. For instance, if we want to analyze customer buying behavior, we can use unsupervise learning to identify common characteristics of customers who buy more products.\nIn summary, supervised learning relies on labeled data to train the model, while unsupervisied learning relied on unlabelled data.\nIs there anything else you would like to know?]|
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/LLAMA3Transformer.md b/docs/en/transformer_entries/LLAMA3Transformer.md
new file mode 100644
index 00000000000000..675ee02416a4ad
--- /dev/null
+++ b/docs/en/transformer_entries/LLAMA3Transformer.md
@@ -0,0 +1,153 @@
+{%- capture title -%}
+LLAMA3Transformer
+{%- endcapture -%}
+
+{%- capture description -%}
+[Llama 3](https://www.llama.com/models/llama-3/) is the next generation of Meta's large language models, available in 8B and 70B parameter sizes. Llama 3 introduces improvements in model architecture, training data scale, and context length, resulting in enhanced reasoning, code generation, and instruction-following capabilities compared to Llama 2.
+
+Llama 3-Chat is fine-tuned for dialogue and assistant use cases, leveraging supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align outputs with human preferences.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+```scala
+val seq2seq = LLAMA3Transformer.pretrained("llama_3_7b_instruct_hf_int4","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("generation")
+```
+The default model is `"llama_3_7b_instruct_hf_int4"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=LLAMA3Transformer).
+
+Spark NLP also supports Hugging Face transformer-based code generation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resource**:
+
+- [Meta Llama 3 on HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
+- [Meta AI Llama 3 Announcement](https://ai.meta.com/blog/meta-llama-3/)
+- [Meta Llama 3 Technical Report (2024)](https://arxiv.org/abs/2407.21783)
+- [Awesome Llama Resources (GitHub)](https://github.com/MIBlue119/awesome-llama-resources#llama-3)
+- [Fine-Tuning Llama 3: Guide (DataCamp)](https://www.datacamp.com/tutorial/llama3-fine-tuning-locally)
+
+**Paper abstract**
+
+*Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[LLAMA3Transformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/LLAMA3Transformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[LLAMA3Transformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/llama3_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[LLAMA3Transformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/LLAMA3Transformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import LLAMA3Transformer
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+llama3 = LLAMA3Transformer.pretrained() \
+    .setInputCols(["documents"]) \
+    .setOutputCol("generation") \
+    .setMinOutputLength(50) \
+    .setMaxOutputLength(250) \
+    .setDoSample(True) \
+    .setTemperature(0.7) \
+    .setTopK(50) \
+    .setTopP(0.9) \
+    .setRepetitionPenalty(1.1) \
+    .setNoRepeatNgramSize(3) \
+    .setIgnoreTokenIds([])
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    llama3
+])
+
+prompt = spark.createDataFrame([("""
+### System:
+You are a concise assistant who explains machine learning in simple terms.
+
+### User:
+Explain the difference between supervised and unsupervised learning with examples.
+
+### Assistant:
+""",)], ["text"])
+
+model = pipeline.fit(prompt)
+results = model.transform(prompt)
+
+results.select("generation.result").show(truncate=False)
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators._
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions._
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("documents")
+
+val llama3 = LLAMA3Transformer.pretrained()
+  .setInputCols("documents")
+  .setOutputCol("generation")
+  .setMinOutputLength(50)
+  .setMaxOutputLength(250)
+  .setDoSample(true)
+  .setTemperature(0.7f)
+  .setTopK(50)
+  .setTopP(0.9f)
+  .setRepetitionPenalty(1.1f)
+  .setNoRepeatNgramSize(3)
+  .setIgnoreTokenIds(Array.emptyIntArray)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  llama3
+))
+
+val prompt = Seq("""
+### System:
+You are a concise assistant who explains machine learning in simple terms.
+
+### User:
+Explain the difference between supervised and unsupervised learning with examples.
+
+### Assistant:
+""").toDF("text")
+
+val model = pipeline.fit(prompt)
+val results = model.transform(prompt)
+
+results.select("generation.result").show(false)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/M2M100Transformer.md b/docs/en/transformer_entries/M2M100Transformer.md
new file mode 100644
index 00000000000000..4e738775a02574
--- /dev/null
+++ b/docs/en/transformer_entries/M2M100Transformer.md
@@ -0,0 +1,130 @@
+{%- capture title -%}
+M2M100Transformer
+{%- endcapture -%}
+
+{%- capture description -%}
+[M2M100](https://huggingface.co/facebook/m2m100_418M) is a multilingual machine translation model developed by Facebook AI (Meta AI). Unlike previous models that require English as an intermediate language, M2M100 can directly translate between any pair of 100 languages, enabling true many-to-many translation. It is trained on a large-scale dataset covering 7.5 billion sentence pairs across 100 languages.
+
+M2M100 supports both text translation and zero-shot translation for language pairs not seen during training. The model is available in several sizes, including 418M and 1.2B parameters.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+```scala
+val m2m100 = M2M100Transformer.pretrained("m2m100_418M","xx") 
+    .setInputCols(Array("documents"))
+    .setMaxOutputLength(50) 
+    .setOutputCol("generation") 
+    .setSrcLang("en") 
+    .setTgtLang("zh")
+```
+The default model is `"m2m100_418M"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=M2M100Transformer).
+
+Spark NLP also supports Hugging Face transformer-based code generation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resource**:
+
+- [M2M100 on HuggingFace](https://huggingface.co/facebook/m2m100_418M)
+- [Meta AI M2M100 Announcement](https://ai.meta.com/blog/introducing-m2m-100-first-multilingual-machine-translation-model/)
+- [M2M-100: Massively Multilingual Machine Translation (Paper)](https://arxiv.org/abs/2010.11125)
+- [Awesome Multilingual NLP (GitHub)](https://github.com/csebuetnlp/awesome-multilingual-nlp#machine-translation)
+- [Fine-Tuning M2M100: Guide (DataCamp)](https://www.datacamp.com/tutorial/m2m100-fine-tuning)
+
+**Paper abstract**
+
+*We introduce M2M-100, the first many-to-many multilingual translation model that can translate directly between any pair of 100 languages. Previous multilingual models rely on English-centric data and require English as an intermediate language. M2M-100 is trained on a large-scale dataset of 7.5B sentence pairs from 100 languages, enabling direct translation between any language pair. We show that M2M-100 outperforms previous models on several benchmarks and enables zero-shot translation for language pairs not seen during training.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+TRANSLATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[M2M100Transformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/M2M100Transformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[M2M100Transformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/m2m100_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[M2M100Transformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/M2M100Transformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import M2M100Transformer
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+m2m100 = M2M100Transformer.pretrained() \
+    .setInputCols(["documents"]) \
+    .setOutputCol("translation") \
+    .setSourceLang("en") \
+    .setTargetLang("fr") \
+    .setMinOutputLength(10) \
+    .setMaxOutputLength(100)
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    m2m100
+])
+
+data = spark.createDataFrame([("Machine translation is a challenging task.",)], ["text"])
+
+model = pipeline.fit(data)
+results = model.transform(data)
+
+results.select("translation.result").show(truncate=False)
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators._
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions._
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("documents")
+
+val m2m100 = M2M100Transformer.pretrained()
+  .setInputCols("documents")
+  .setOutputCol("translation")
+  .setSourceLang("en")
+  .setTargetLang("fr")
+  .setMinOutputLength(10)
+  .setMaxOutputLength(100)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  m2m100
+))
+
+val data = Seq("Machine translation is a challenging task.").toDF("text")
+
+val model = pipeline.fit(data)
+val results = model.transform(data)
+
+results.select("translation.result").show(false)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/MPNetForTokenClassification.md b/docs/en/transformer_entries/MPNetForTokenClassification.md
new file mode 100644
index 00000000000000..248b10fcc44284
--- /dev/null
+++ b/docs/en/transformer_entries/MPNetForTokenClassification.md
@@ -0,0 +1,125 @@
+{%- capture title -%}
+MPNetForTokenClassification
+{%- endcapture -%}
+
+{%- capture description -%}
+The MPNet model was proposed in [MPNet: Masked and Permuted Pre-training for Language Understanding](https://huggingface.co/papers/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
+
+MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of masked language modeling and permuted language modeling for natural language understanding.
+
+`MPNetForTokenClassification` in Spark NLP is a token classification annotator based on the MPNet architecture. It can be used for tasks such as Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and other token-level classification problems.  
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+```scala
+val tokenClassifier = MPNetForTokenClassification.pretrained("mpnet_base_token_classifier", "en")
+    .setInputCols(Array("documents","token")) 
+    .setOutputCol("ner")
+```
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=MPNetForTokenClassification).
+
+Spark NLP supports a variety of Hugging Face Transformers for token classification. To learn how to import and use them, check out the following thread:
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+
+- [MPNet: Masked and Permuted Pre-training for Language Understanding (Paper)](https://arxiv.org/abs/2004.09297)  
+
+**Paper abstract**
+
+*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models. Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g., BERT, XLNet, RoBERTa) under the same model setting.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+TOKEN, DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+CATEGORY
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[MPNetForTokenClassification](/api/com/johnsnowlabs/nlp/annotators/classifier/dl/MPNetForTokenClassification.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[MPNetForTokenClassification](api/python/reference/autosummary/sparknlp/annotator/classifier_dl/mpnet_for_token_classification/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[MPNetForTokenClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/MPNetForTokenClassification.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import Tokenizer, MPNetForTokenClassification
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \\
+    .setInputCol("text") \\
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \\
+    .setInputCols(["document"]) \\
+    .setOutputCol("token")
+
+tokenClassifier = MPNetForTokenClassification.pretrained("token_classifier_mpnet_base", "en") \\
+    .setInputCols(["token", "document"]) \\
+    .setOutputCol("ner")
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    tokenizer,
+    tokenClassifier
+])
+
+data = spark.createDataFrame([["John lives in New York."]], ["text"])
+
+model = pipeline.fit(data)
+results = model.transform(data)
+
+results.selectExpr("explode(ner.result) as ner").show(truncate=False)
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators.{Tokenizer, MPNetForTokenClassification}
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+  .setInputCols("document")
+  .setOutputCol("token")
+
+val tokenClassifier = MPNetForTokenClassification.pretrained("token_classifier_mpnet_base", "en")
+  .setInputCols("token", "document")
+  .setOutputCol("ner")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  tokenizer,
+  tokenClassifier
+))
+
+val data = Seq("John lives in New York.").toDF("text")
+
+val model = pipeline.fit(data)
+val results = model.transform(data)
+
+results.selectExpr("explode(ner.result) as ner").show(false)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/MistralTransformer.md b/docs/en/transformer_entries/MistralTransformer.md
new file mode 100644
index 00000000000000..dfa9926950c0c3
--- /dev/null
+++ b/docs/en/transformer_entries/MistralTransformer.md
@@ -0,0 +1,115 @@
+{%- capture title -%}
+MistralTransformer
+{%- endcapture -%}
+
+{%- capture description -%}
+[Mistral](https://huggingface.co/papers/2310.06825) is a 7B parameter language model, available as a pretrained and instruction-tuned variant, focused on balancing the scaling costs of large models with performance and efficient inference. This model uses sliding window attention (SWA) trained with a 8K context length and a fixed cache size to handle longer sequences more effectively. Grouped-query attention (GQA) speeds up inference and reduces memory requirements. Mistral also features a byte-fallback BPE tokenizer to improve token handling and efficiency by ensuring characters are never mapped to out-of-vocabulary tokens.
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+```scala
+val mistral = MistralTransformer.pretrained()
+    .setMaxOutputLength(50)
+    .setDoSample(False)
+    .setInputCols(["document"])
+    .setOutputCol("generation")
+```
+The default model is `"mistral_7b"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=MistralTransformer).
+
+Spark NLP also supports importing Hugging Face Mistral models. See:
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+
+- [Mistral 7B: Efficient open-weight language model (Paper)](https://arxiv.org/abs/2310.06825)  
+- [Mistral AI on Hugging Face](https://huggingface.co/mistralai)  
+
+**Paper abstract**
+
+*We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B -- Instruct, that surpasses the Llama 2 13B -- Chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license. *
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[MistralTransformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/MistralTransformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[MistralTransformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/mistral_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[MistralTransformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/MistralTransformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import MistralTransformer
+from pyspark.ml import Pipeline
+
+document_assembler = DocumentAssembler() \\
+    .setInputCol("text") \\
+    .setOutputCol("document")
+
+mistral = MistralTransformer.pretrained("mistral_7b", "en") \\
+    .setInputCols(["document"]) \\
+    .setOutputCol("embeddings")
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    mistral
+])
+
+data = spark.createDataFrame([["Mistral models are efficient and powerful for NLP tasks."]], ["text"])
+
+model = pipeline.fit(data)
+results = model.transform(data)
+
+results.selectExpr("explode(embeddings.embeddings) as vector").show(truncate=False)
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators.seq2seq.MistralTransformer
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val mistral = MistralTransformer.pretrained("mistral_7b", "en")
+  .setInputCols("document")
+  .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  mistral
+))
+
+val data = Seq("Mistral models are efficient and powerful for NLP tasks.").toDF("text")
+
+val model = pipeline.fit(data)
+val results = model.transform(data)
+
+results.selectExpr("explode(embeddings.embeddings) as vector").show(false)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
diff --git a/docs/en/transformer_entries/MxbaiEmbeddings.md b/docs/en/transformer_entries/MxbaiEmbeddings.md
new file mode 100644
index 00000000000000..01c84c4e6d98da
--- /dev/null
+++ b/docs/en/transformer_entries/MxbaiEmbeddings.md
@@ -0,0 +1,124 @@
+{%- capture title -%} MxbaiEmbeddings {%- endcapture -%} 
+{%- capture description -%} 
+Mxbai Embeddings are a family of models by Mixedbread AI that convert text into vector representations for tasks like semantic search, document retrieval, clustering, classification, and recommendation. They feature binary quantization (up to ~32× smaller with minimal performance loss), Matryoshka Representation Learning (MRL) (front-loaded dimensions for efficient truncation), long context support (up to 4096 tokens in the xsmall model), are open-source under Apache-2.0, and achieve strong MTEB benchmark scores (~64.68 for the large model).
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+```scala
+val mxbai = MxbaiEmbeddings.pretrained("mxbai_large_v1", "en")
+    .setInputCols("documents")
+    .setOutputCol("embeddings")
+```
+The default model is `"mxbai_large_v1"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=MxbaiEmbeddings).  
+
+Spark NLP supports a variety of Hugging Face embedding models. To learn how to import and use them, check out the following thread:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [Mxbai Models on Hugging Face](https://huggingface.co/mixedbread-ai)  
+- [Mixedbread Docs — Our Embedding Models](https://www.mixedbread.com/docs/models/embedding)  
+- [GitHub: binary-embeddings (quantization showcase)](https://github.com/mixedbread-ai/binary-embeddings)
+
+**Paper abstract**
+
+*We present Mxbai Embeddings, a family of open-source text embedding models developed by Mixedbread AI for high-performance semantic representation learning. The flagship model, mxbai-embed-large-v1, is trained on over 700 million text pairs and fine-tuned with 30 million high-quality triplets using the AnglE loss function. These models are designed for a broad range of applications including semantic search, retrieval, clustering, classification, and recommendation. Key innovations include binary quantization, which reduces embedding storage size by up to 32× and accelerates retrieval speeds with minimal accuracy loss, and Matryoshka Representation Learning (MRL), which prioritizes information in earlier embedding dimensions to enable efficient truncation. Selected models also support extended context lengths (up to 4096 tokens). All models are released under the Apache-2.0 license, ensuring broad usability. On the Massive Text Embedding Benchmark (MTEB), mxbai-embed-large-v1 achieves an average score of ~64.68, demonstrating competitive performance compared to proprietary systems while offering efficiency and transparency for the community.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENTS
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+EMBEDDINGS
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[MxbaiEmbeddings](/api/com/johnsnowlabs/nlp/embeddings/MxbaiEmbeddings.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[MxbaiEmbeddings](/api/python/reference/autosummary/sparknlp/annotator/embeddings/mxbai_embeddings/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[MxbaiEmbeddings](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/embeddings/MxbaiEmbeddings.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import MxbaiEmbeddings
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+mxbai = MxbaiEmbeddings.pretrained("mxbai_large_v1","en") \
+    .setInputCols("document") \
+    .setOutputCol("embeddings") \
+
+pipeline = Pipeline().setStages([
+    documentAssembler, 
+    mxbai
+])
+
+data = spark.createDataFrame([
+    ["I love spark-nlp"]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("embeddings.embeddings").show()
+
++--------------------+
+|          embeddings|
++--------------------+
+|[[-0.26401705, 0....|
++--------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.embeddings.MxbaiEmbeddings
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val mxbai = MxbaiEmbeddings.pretrained("mxbai_large_v1", "en")
+  .setInputCols("document")
+  .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  mxbai
+))
+
+val data = Seq("I love spark-nlp").toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("embeddings.embeddings").show()
+
++--------------------+
+|          embeddings|
++--------------------+
+|[[-0.26401705, 0....|
++--------------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/NLLBTransformer.md b/docs/en/transformer_entries/NLLBTransformer.md
new file mode 100644
index 00000000000000..c94eea172e4434
--- /dev/null
+++ b/docs/en/transformer_entries/NLLBTransformer.md
@@ -0,0 +1,130 @@
+{%- capture title -%} NLLBTransformer {%- endcapture -%} 
+{%- capture description -%} 
+NLLBTransformer is a Spark NLP annotator that leverages the No Language Left Behind (NLLB) models by Meta AI. These models are designed to provide high-quality machine translation for over 200 languages, especially low-resource ones. They are trained on massive multilingual datasets and optimized for efficient inference, making them suitable for real-world translation systems at scale.
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+```scala
+val seq2seq = NLLBTransformer.pretrained("nllb_distilled_600M_8int","xx") 
+    .setInputCols(Array("documents")) 
+    .setOutputCol("generation")
+```
+The default model is `"nllb_distilled_600M_8int"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=NLLBTransformer).
+
+Spark NLP also supports Hugging Face transformer-based translation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [NLLB Project on GitHub](https://github.com/facebookresearch/fairseq/tree/nllb)  
+- [NLLB: No Language Left Behind Paper](https://arxiv.org/abs/2207.04672)  
+- [Meta AI NLLB Blog Post](https://ai.meta.com/research/no-language-left-behind/)  
+
+**Paper abstract**
+
+*Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system. Finally, we open source all contributions described in this work, accessible at [this https URL.](https://github.com/facebookresearch/fairseq/tree/nllb)*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENTS
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[NLLBTransformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/NLLBTransformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[NLLBTransformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/nllb_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[NLLBTransformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/NLLBTransformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import NLLBTransformer
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("documents")   
+
+nllb = NLLBTransformer.pretrained("nllb_distilled_600M_8int", "xx") \
+    .setInputCols(["documents"]) \
+    .setOutputCol("generation") \
+    .setSrcLang("eng_Latn") \
+    .setTgtLang("zho_Hans") \
+    .setMaxOutputLength(50)
+
+pipeline = Pipeline().setStages([
+    documentAssembler, 
+    nllb
+])
+
+data = spark.createDataFrame([
+    ["Artificial intelligence is transforming the way people communicate, learn, and work across the world."]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("generation.result").show()
+
++-----------------------------------------------+
+|result                                         |
++-----------------------------------------------+
+|[人工智能正在改变人们在世界各地的沟通,学习和工作方式.] |
++-----------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.annotator.NLLBTransformer
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("documents")
+
+val nllb = NLLBTransformer.pretrained("nllb_distilled_600M_8int", "xx")
+  .setInputCols("documents")
+  .setOutputCol("generation")
+  .setSrcLang("eng_Latn")
+  .setTgtLang("zho_Hans")
+  .setMaxOutputLength(50)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  nllb
+))
+
+val data = Seq("Artificial intelligence is transforming the way people communicate, learn, and work across the world.").toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("generation.result").show(truncate = false)
+
++-----------------------------------------------+
+|result                                         |
++-----------------------------------------------+
+|[人工智能正在改变人们在世界各地的沟通,学习和工作方式.] |
++-----------------------------------------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/NomicEmbeddings.md b/docs/en/transformer_entries/NomicEmbeddings.md
new file mode 100644
index 00000000000000..c1befcb01c5d69
--- /dev/null
+++ b/docs/en/transformer_entries/NomicEmbeddings.md
@@ -0,0 +1,129 @@
+{%- capture title -%} NomicEmbeddings {%- endcapture -%} 
+{%- capture description -%} 
+Nomic Embeddings are open-source text embedding models released by Nomic AI designed for semantic tasks such as search, clustering, classification, and retrieval. These models are optimized for high performance on the Massive Text Embedding Benchmark (MTEB) while remaining efficient and production-ready. Nomic provides different embedding sizes for balancing accuracy and compute efficiency, and the models are widely used in open-source vector databases like Atlas.  
+
+They are trained on large, diverse datasets with careful curation to maximize generalization and multilingual coverage. 
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+```scala
+val embeddings = NomicEmbeddings.pretrained("nomic_embed_v1","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("embeddings")
+```
+The default model is `"nomic_embed_v1"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=NomicEmbeddings).
+
+Spark NLP also supports Hugging Face transformer-based translation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [Nomic Embeddings on Hugging Face](https://huggingface.co/nomic-ai)  
+- [Nomic AI Official Site](https://nomic.ai/)  
+- [Nomic Atlas Vector Database](https://atlas.nomic.ai)  
+- [Nomic Embed: Training a Reproducible Long Context Text Embedder (Arxiv)](https://arxiv.org/abs/2402.01613)  
+- [Training Sparse Mixture Of Experts Text Embedding Models (Arxiv)](https://arxiv.org/abs/2502.07972)  
+- [Nomic Embed Vision: Expanding the Latent Space (Arxiv)](https://arxiv.org/abs/2406.18587)  
+
+**Paper abstract**
+
+*This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on the short-context MTEB benchmark and the long context LoCo benchmark. We release the training code and model weights under an Apache 2.0 license. In contrast with other open-source models, we release the full curated training data and code that allows for full replication of nomic-embed-text-v1. You can find code and data to replicate the model at [this https URL.](https://github.com/nomic-ai/contrastors)*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+EMBEDDINGS
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[NLLBTransformer](/api/com/johnsnowlabs/nlp/embeddings/NomicEmbeddings.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[NLLBTransformer](/api/python/reference/autosummary/sparknlp/annotator/embeddings/nomic_embeddings/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[NLLBTransformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/embeddings/NomicEmbeddings.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import NomicEmbeddings
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("document")   
+
+embeddings = NomicEmbeddings.pretrained("nomic_embed_v1","en") \
+      .setInputCols(["document"]) \
+      .setOutputCol("embeddings") 
+
+pipeline = Pipeline().setStages([
+    documentAssembler, 
+    embeddings
+])
+
+data = spark.createDataFrame([
+    ["Artificial intelligence is transforming the way people communicate, learn, and work across the world."]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("embeddings.embeddings").show()
+
++--------------------+
+|          embeddings|
++--------------------+
+|[[-0.004530675, 0...|
++--------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.embeddings.NomicEmbeddings
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val embeddings = NomicEmbeddings.pretrained("nomic_embed_v1", "en")
+  .setInputCols("document")
+  .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  embeddings
+))
+
+val data = Seq("Artificial intelligence is transforming the way people communicate, learn, and work across the world.").toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("embeddings.embeddings").show()
+
++--------------------+
+|          embeddings|
++--------------------+
+|[[-0.004530675, 0...|
++--------------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/Phi2Transformer.md b/docs/en/transformer_entries/Phi2Transformer.md
new file mode 100644
index 00000000000000..0e3d6a117c9822
--- /dev/null
+++ b/docs/en/transformer_entries/Phi2Transformer.md
@@ -0,0 +1,127 @@
+{%- capture title -%} Phi2Transformer {%- endcapture -%} 
+{%- capture description -%} 
+Phi-2 Transformer is a small, high-performance, open-source causal language model developed by Microsoft. With 2.7 billion parameters, it is trained on a mixture of synthetic and web datasets designed to improve commonsense reasoning and language understanding. Phi-2 demonstrates strong performance across reasoning, language comprehension, and benchmark evaluations, often outperforming models many times larger.  
+
+It is particularly efficient for research, prototyping, and production tasks where small but powerful models are preferred.  
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+```scala
+val phi2 = Phi2Transformer.pretrained()
+     .setInputCols("document")
+     .setOutputCol("generation")
+```
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=Phi2Transformer).  
+
+Spark NLP also supports a variety of Hugging Face transformer-based language models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [Phi-2 on Hugging Face](https://huggingface.co/microsoft/phi-2)  
+- [Microsoft Research Blog on Phi-2](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/)   
+- [Arxiv: Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463)  
+
+**Paper abstract**
+
+*Over the past few months, our Machine Learning Foundations team at Microsoft Research has released a suite of small language models (SLMs) called “Phi” that achieve remarkable performance on a variety of benchmarks. Our first model, the 1.3 billion parameter Phi-1 (opens in new tab), achieved state-of-the-art performance on Python coding among existing SLMs (specifically on the HumanEval and MBPP benchmarks). We then extended our focus to common sense reasoning and language understanding and created a new 1.3 billion parameter model named Phi-1.5 (opens in new tab), with performance comparable to models 5x larger. We are now releasing Phi-2 (opens in new tab), a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities, showcasing state-of-the-art performance among base language models with less than 13 billion parameters. On complex benchmarks Phi-2 matches or outperforms models up to 25x larger, thanks to new innovations in model scaling and training data curation. With its compact size, Phi-2 is an ideal playground for researchers, including for exploration around mechanistic interpretability, safety improvements, or fine-tuning experimentation on a variety of tasks. We have made Phi-2 (opens in new tab) available in the Azure AI Studio model catalog to foster research and development on language models.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[Phi2Transformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/Phi2Transformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[Phi2Transformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi2_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[Phi2Transformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/Phi2Transformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import Phi2Transformer
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("document")
+
+phi2 = Phi2Transformer.pretrained() \
+      .setMaxOutputLength(50) \
+      .setDoSample(False) \
+      .setInputCols(["document"]) \
+      .setOutputCol("generation")
+
+pipeline = Pipeline().setStages([
+    documentAssembler,
+    phi2
+])
+
+data = spark.createDataFrame([
+    ["What is the capital of France?"]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("generation.result").show()
+
++------------+
+|      result|
++------------+
+|     [Paris]|
++------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators.seq2seq.Phi2Transformer
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val phi2 = Phi2Transformer.pretrained()
+  .setInputCols("document")
+  .setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  phi2
+))
+
+val data = Seq("What is the capital of France?").toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("generation.result").show()
+
++------------+
+|      result|
++------------+
+|     [Paris]|
++------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
diff --git a/docs/en/transformer_entries/Phi3Transformer.md b/docs/en/transformer_entries/Phi3Transformer.md
new file mode 100644
index 00000000000000..fafdf3749d0a7e
--- /dev/null
+++ b/docs/en/transformer_entries/Phi3Transformer.md
@@ -0,0 +1,125 @@
+{%- capture title -%} Phi3Transformer {%- endcapture -%} 
+{%- capture description -%} 
+Phi-3 is a family of small language models (SLMs) optimized for efficient reasoning, code generation, and general NLP tasks. The models range from 3.8B to 14B parameters, with context lengths up to 128K tokens, and are best suited for instruction-following and chat-style prompts. Trained on curated public, educational, and synthetic data, Phi-3 emphasizes strong performance in math, coding, and logical reasoning while remaining lightweight and cost-effective. The design enables fast, affordable deployment across cloud and edge environments, with a primary focus on English (limited multilingual support).
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+```scala
+val phi3 = Phi3Transformer.pretrained()
+     .setInputCols("document")
+     .setOutputCol("generation")
+```
+The default model is `"phi_3.5_mini_instruct_int4"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=Phi3Transformer).
+
+Spark NLP also supports Hugging Face transformer-based translation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [Introducing Phi-3: Redefining What’s Possible with SLMs (Microsoft Blog)](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/)  
+- [Phi-3 Small Language Models with Big Potential (Microsoft Source)](https://news.microsoft.com/source/features/ai/the-phi-3-small-language-models-with-big-potential/)  
+- [Phi-3 Tutorial and Overview (DataCamp)](https://www.datacamp.com/tutorial/phi-3-tutorial)  
+- [Evaluation of Phi-3 Models (Arxiv)](https://arxiv.org/abs/2404.14219)  
+
+**Paper abstract**
+
+*We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench).*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[Phi3Transformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/Phi3Transformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[Phi3Transformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi3_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[Phi3Transformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/Phi3Transformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import Phi3Transformer
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("document")   
+
+phi3 = Phi3Transformer.pretrained() \
+      .setInputCols(["document"]) \
+      .setOutputCol("generation") 
+
+pipeline = Pipeline().setStages([
+    documentAssembler, 
+    embephi3ddings
+])
+
+data = spark.createDataFrame([
+    ["What is 7 × 8?"]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("generation.result").show()
+
++-------------------------------+
+|result                         |
++-------------------------------+
+|[The product of 7 and 8 is 56.]|
++-------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val phi3 = Phi3Transformer.pretrained()
+  .setInputCols("document")
+  .setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  phi3
+))
+
+val data = Seq("What is 7 × 8?").toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("generation.result").show()
+
++-------------------------------+
+|result                         |
++-------------------------------+
+|[The product of 7 and 8 is 56.]|
++-------------------------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
\ No newline at end of file
diff --git a/docs/en/transformer_entries/QwenTransformer.md b/docs/en/transformer_entries/QwenTransformer.md
new file mode 100644
index 00000000000000..01e9f16c149720
--- /dev/null
+++ b/docs/en/transformer_entries/QwenTransformer.md
@@ -0,0 +1,142 @@
+{%- capture title -%} QwenTransformer {%- endcapture -%} 
+{%- capture description -%} 
+Qwen is a family of decoder-only large language models developed by the Qwen team (Alibaba) and optimized for advanced reasoning, code generation, chat and general NLP tasks. The series includes multiple sizes (from ~0.5B up to 72B parameters) with stable support for long contexts (commonly 32K tokens for released models) and specialized variants for chat, code, and multimodal workloads. 
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+
+```scala
+val qwen = QwenTransformer.pretrained()
+     .setInputCols("document")
+     .setOutputCol("generation")
+```
+
+The default model is `"qwen_7.5b_chat"`, if no name is provided. For available pretrained models please see the Models Hub. 
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=Phi3Transformer).
+
+Spark NLP also supports Hugging Face transformer-based translation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [Qwen official site (Qwen Chat)](https://qwen.ai/)  
+- [Qwen GitHub (Qwen / Qwen)](https://github.com/QwenLM/Qwen)  
+- [Qwen on Hugging Face (organization)](https://huggingface.co/Qwen)  
+
+**Paper abstract**
+
+*In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been significantly improved during both the pre-training and post-training stages. In terms of pre-training, we have scaled the high-quality pre-training datasets from the previous 7 trillion tokens to 18 trillion tokens. This provides a strong foundation for common sense, expert knowledge, and reasoning capabilities. In terms of post-training, we implement intricate supervised finetuning with over 1 million samples, as well as multistage reinforcement learning. Post-training techniques enhance human preference, and notably improve long text generation, structural data analysis, and instruction following. To handle diverse and varied use cases effectively, we present Qwen2.5 LLM series in rich sizes. Open-weight offerings include base and instruction-tuned models, with quantized versions available. In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2.5-Turbo and Qwen2.5-Plus, both available from Alibaba Cloud Model Studio. Qwen2.5 has demonstrated top-tier performance on a wide range of benchmarks evaluating language understanding, reasoning, mathematics, coding, human preference alignment, etc. Specifically, the open-weight flagship Qwen2.5-72B-Instruct outperforms a number of open and proprietary models and demonstrates competitive performance to the state-of-the-art open-weight model, Llama-3-405B-Instruct, which is around 5 times larger. Qwen2.5-Turbo and Qwen2.5-Plus offer superior cost-effectiveness while performing competitively against GPT-4o-mini and GPT-4o respectively. Additionally, as the foundation, Qwen2.5 models have been instrumental in training specialized models such as Qwen2.5-Math, Qwen2.5-Coder, QwQ, and multimodal models.*
+
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[QwenTransformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/QwenTransformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[QwenTransformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/qwen_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[QwenTransformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/QwenTransformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import QwenTransformer
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \
+  .setInputCol("text") \
+  .setOutputCol("document")   
+
+qwen = QwenTransformer.pretrained("qwen_7.5b_chat") \
+  .setInputCols(["document"]) \
+  .setMaxOutputLength(100) \
+  .setOutputCol("generation")
+
+pipeline = Pipeline().setStages([
+    documentAssembler,
+    qwen
+])
+
+data = spark.createDataFrame([
+    ["What is 7 × 8?"]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("generation.result").show(truncate=False)
+
++--------------------------------+
+|result                          |
++--------------------------------+
+|[The product of 7 and 8 is 56.] |
++--------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val qwen = QwenTransformer.pretrained("qwen_7.5b_chat")
+  .setInputCols("document")
+  .setMaxOutputLength(100)
+  .setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  qwen
+))
+
+val data = Seq("What is 7 × 8?").toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("generation.result").show(false)
+
++--------------------------------+
+|result                          |
++--------------------------------+
+|[The product of 7 and 8 is 56.] |
++--------------------------------+
+{%- endcapture -%}
+
+{%- capture resources -%}
+**Resources**:
+- [Qwen official site (Qwen Chat)](https://qwen.ai/)  
+- [Qwen GitHub (Qwen / Qwen)](https://github.com/QwenLM/Qwen)  
+- [Qwen on Hugging Face (organization)](https://huggingface.co/Qwen)  
+- [Qwen technical report / papers (arXiv)](https://arxiv.org/search/?query=Qwen&searchtype=all)  
+- [Spark NLP QwenTransformer docs](https://sparknlp.org/api/python/reference/autosummary/sparknlp/annotator/seq2seq/qwen_transformer/index.html)  
+{%- endcapture -%}
+
+{%- capture paper_abstract -%}
+*We introduce Qwen, a comprehensive language model series consisting of base and chat models across multiple sizes. The series demonstrates strong performance on a wide range of downstream tasks, with chat variants aligned via human feedback and specialized models for coding and math. Models support extended context lengths (commonly up to 32K tokens) and show competitive results compared to contemporaneous open-source models.*  
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
diff --git a/docs/en/transformer_entries/SnowFlakeEmbeddings.md b/docs/en/transformer_entries/SnowFlakeEmbeddings.md
new file mode 100644
index 00000000000000..87799979883881
--- /dev/null
+++ b/docs/en/transformer_entries/SnowFlakeEmbeddings.md
@@ -0,0 +1,143 @@
+{%- capture title -%} SnowFlakeEmbeddings {%- endcapture -%} 
+{%- capture description -%} 
+Snowflake Embeddings are vector representations of text and images that let you measure *semantic similarity*, powering tasks like search, clustering, classification, and retrieval-augmented generation (RAG).  
+The main family, called Arctic-embed, comes in different sizes for a balance of speed, cost, and accuracy, and supports *multilingual* and *long-context* inputs.  
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+
+```scala
+val snowflake = SnowFlakeEmbeddings.pretrained("snowflake_artic_m", "en")
+    .setInputCols("documents")
+    .setOutputCol("embeddings")
+```
+
+The default model is `"snowflake_artic_m"`, if no name is provided. For available pretrained models please see the Models Hub. 
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=SnowFlakeEmbeddings).
+
+Spark NLP also supports Hugging Face transformer-based translation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [Snowflake Arctic Embed announcement blog](https://www.snowflake.com/en/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/)  
+- [Snowflake-Labs / arctic-embed (GitHub)](https://github.com/Snowflake-Labs/arctic-embed)  
+- [Snowflake on Hugging Face (organization)](https://huggingface.co/Snowflake)  
+
+**Paper abstract**
+
+*This report describes the training dataset creation and recipe behind the family of arctic-embed text embedding models (a set of five models ranging from 22 to 334 million parameters with weights open-sourced under an Apache-2 license). At the time of their release, each model achieved state-of-the-art retrieval accuracy for models of their size on the MTEB Retrieval leaderboard,1 with the largest model, arctic-embed-l outperforming closed source embedding models such as Cohere’s embed-v3 and Open AI’s text-embed-3-large. In addition to the details of our training recipe, we have provided several informative ablation studies, which we believe are the cause of our model performance.*
+
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+EMBEDDINGS
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[SnowFlakeEmbeddings](/api/com/johnsnowlabs/nlp/embeddings/SnowFlakeEmbeddings.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[SnowFlakeEmbeddings](/api/python/reference/autosummary/sparknlp/annotator/embeddings/snowflake_embeddings/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[SnowFlakeEmbeddings](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/embeddings/SnowFlakeEmbeddings.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import SnowFlakeEmbeddings
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \
+  .setInputCol("text") \
+  .setOutputCol("document")   
+
+snowflake = SnowFlakeEmbeddings.pretrained("snowflake_artic_m","en") \
+    .setInputCols("document") \
+    .setOutputCol("embeddings") \
+
+pipeline = Pipeline().setStages([
+    documentAssembler,
+    snowflake
+])
+
+data = spark.createDataFrame([
+    ["I love spark-nlp"]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("embeddings.embeddings").show()
+
++--------------------+
+|          embeddings|
++--------------------+
+|[[-0.6112396, 0.2...|
++--------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.embeddings.SnowflakeEmbeddings
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val snowflake = SnowflakeEmbeddings.pretrained("snowflake_artic_m", "en")
+  .setInputCols("document")
+  .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  snowflake
+))
+
+val data = spark.createDataFrame(Seq(
+  Tuple1("I love spark-nlp")
+)).toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("embeddings.embeddings").show()
+
++--------------------+
+|          embeddings|
++--------------------+
+|[[-0.6112396, 0.2...|
++--------------------+
+{%- endcapture -%}
+
+{%- capture resources -%}
+Resources:
+- [Qwen official site (Qwen Chat)](https://qwen.ai/)  
+- [Qwen GitHub (Qwen / Qwen)](https://github.com/QwenLM/Qwen)  
+- [Qwen on Hugging Face (organization)](https://huggingface.co/Qwen)  
+- [Qwen technical report / papers (arXiv)](https://arxiv.org/search/?query=Qwen&searchtype=all)  
+- [Spark NLP QwenTransformer docs](https://sparknlp.org/api/python/reference/autosummary/sparknlp/annotator/seq2seq/qwen_transformer/index.html)  
+{%- endcapture -%}
+
+{%- capture paper_abstract -%}
+*We introduce Qwen, a comprehensive language model series consisting of base and chat models across multiple sizes. The series demonstrates strong performance on a wide range of downstream tasks, with chat variants aligned via human feedback and specialized models for coding and math. Models support extended context lengths (commonly up to 32K tokens) and show competitive results compared to contemporaneous open-source models.*  
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
diff --git a/docs/en/transformer_entries/StarCoderTransformer.md b/docs/en/transformer_entries/StarCoderTransformer.md
new file mode 100644
index 00000000000000..1bbe21f846b907
--- /dev/null
+++ b/docs/en/transformer_entries/StarCoderTransformer.md
@@ -0,0 +1,128 @@
+{%- capture title -%} StarCoderTransformer {%- endcapture -%} 
+{%- capture description -%} 
+StarCoder is a family of code generation models trained on permissively licensed code from GitHub, optimized for program synthesis, completion, and chat-based coding assistance. The models are based on the Fill-in-the-Middle (FIM) training objective, enabling them to generate code not just from left-to-right but also in the middle of existing code snippets.  
+
+Pretrained models can be loaded with the `pretrained` method of the companion object:
+
+```scala
+val starcoder = StarCoderTransformer.pretrained("starcoder2_3b_int4", "en")
+    .setInputCols("documents")
+    .setOutputCol("generation")
+```
+
+The default model is `"starcoder2_3b_int4"`, if no name is provided. For available pretrained models please see the Models Hub. 
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models?annotator=StarCoderTransformer).
+
+Spark NLP also supports Hugging Face transformer-based code generation models. Learn more here:  
+- [Import models into Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/discussions/5669)
+
+**Resources**:
+- [BigCode StarCoder announcement blog](https://huggingface.co/blog/starcoder)  
+- [bigcode-project / starcoder (GitHub)](https://github.com/bigcode-project/starcoder)  
+- [StarCoder on Hugging Face](https://huggingface.co/bigcode/starcoder)  
+
+**Paper abstract**
+
+*The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40\% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.*
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+GENERATION
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[StarCoderTransformer](/api/com/johnsnowlabs/nlp/annotators/seq2seq/StarCoderTransformer.html)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[StarCoderTransformer](/api/python/reference/autosummary/sparknlp/annotator/seq2seq/starcoder_transformer/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[StarCoderTransformer](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/seq2seq/StarCoderTransformer.scala)
+{%- endcapture -%}
+
+{%- capture python_example -%}
+from sparknlp.base import DocumentAssembler
+from sparknlp.annotator import StarCoderTransformer
+from pyspark.ml import Pipeline
+
+documentAssembler = DocumentAssembler() \ 
+    .setInputCol("text") \ 
+    .setOutputCol("document") \
+
+starcoder = StarCoderTransformer.pretrained("starcoder2_3b_int4","en") \    
+    .setInputCols("document") \     
+    .setOutputCol("generation") \
+
+pipeline = Pipeline().setStages([
+    documentAssembler,
+    starcoder
+])
+
+data = spark.createDataFrame([
+    ["def fibonacci(n):"]
+]).toDF("text")
+
+model = pipeline.fit(data)
+result = model.transform(data)
+
+result.select("generation.result").show(truncate=False)
+
++--------------------------------------------+
+|result                                      |
++--------------------------------------------+
+|[def fibonacci(n):\n    if n <= 1: return n]|
++--------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators.seq2seq.StarCoderTransformer
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val starcoder = StarCoderTransformer.pretrained("starcoder2_3b_int4", "en")
+  .setInputCols("document")
+  .setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  starcoder
+))
+
+val data = spark.createDataFrame(Seq(
+  Tuple1("def fibonacci(n):")
+)).toDF("text")
+
+val model = pipeline.fit(data)
+val result = model.transform(data)
+
+result.select("generation.result").show(false)
+
++--------------------------------------------+
+|result                                      |
++--------------------------------------------+
+|[def fibonacci(n):\n    if n <= 1: return n]|
++--------------------------------------------+
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}
diff --git a/docs/index.md b/docs/index.md
index 729f27cdc76b84..ffee62c9e5d5a9 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -382,7 +382,7 @@ data:
       actions:
         - text: View Demo
           type: trans
-          url: /demo
+          url: /demos
         - text: Examples
           type: trans
           url: https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples
diff --git a/python/README.md b/python/README.md
index 22ef0f4f0d4844..d6bb96deb9e73e 100644
--- a/python/README.md
+++ b/python/README.md
@@ -267,7 +267,7 @@ Please check [these instructions](https://sparknlp.org/docs/en/install#s3-integr
 Need more **examples**? Check out our dedicated [Spark NLP Examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples)
 repository to showcase all Spark NLP use cases!
 
-Also, don't forget to check [Spark NLP in Action](https://sparknlp.org/demo) built by Streamlit.
+Also, don't forget to check [Spark NLP in Action](https://sparknlp.org/demos) built by Streamlit.
 
 #### All examples: [spark-nlp/examples](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples)
 

From 5889860ea02965709f7366a0a0033d7f2b4397ed Mon Sep 17 00:00:00 2001
From: Devin Ha 
Date: Tue, 21 Oct 2025 16:57:13 +0200
Subject: [PATCH 12/13] Bump Version [run doc]

---
 CHANGELOG                                       | 17 +++++++++++++++++
 README.md                                       |  8 ++++----
 build.sbt                                       |  2 +-
 conda/meta.yaml                                 |  4 ++--
 docs/_config.yml                                |  2 +-
 docs/_config_local.yml                          |  2 +-
 python/README.md                                |  8 ++++----
 python/setup.py                                 |  2 +-
 python/sparknlp/__init__.py                     |  2 +-
 scripts/colab_setup.sh                          |  2 +-
 scripts/kaggle_setup.sh                         |  2 +-
 scripts/sagemaker_setup.sh                      |  2 +-
 .../scala/com/johnsnowlabs/util/Build.scala     |  2 +-
 13 files changed, 36 insertions(+), 19 deletions(-)

diff --git a/CHANGELOG b/CHANGELOG
index e8d6be0ba7ccbb..3e1fda151b3616 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -1,3 +1,20 @@
+=======
+6.2.0
+=======
+---------------------------
+New Features & Enhancements
+---------------------------
+
+* [SPARKNLP-1288] AutoGGUF close model #14671
+* [SPARKNLP-1283] Add remove thinking flag #14672
+* [SPARKNLP-1293] Enhancements EntityRuler and DocumentNormalizer #14674
+* [SPARKNLP-1299] Add Hierarchical Element Identification to HTMLReader #14675
+
+---------
+Bug Fixes
+---------
+* [SPARKNLP-1300] RobertaEmbeddings: changing token sequence in warmup test #14677
+
 =======
 6.1.5
 =======
diff --git a/README.md b/README.md
index d6bb96deb9e73e..2a54df6aa8a31d 100644
--- a/README.md
+++ b/README.md
@@ -63,7 +63,7 @@ $ java -version
 $ conda create -n sparknlp python=3.7 -y
 $ conda activate sparknlp
 # spark-nlp by default is based on pyspark 3.x
-$ pip install spark-nlp==6.1.5 pyspark==3.3.1
+$ pip install spark-nlp==6.2.0 pyspark==3.3.1
 ```
 
 In Python console or Jupyter `Python3` kernel:
@@ -129,7 +129,7 @@ For a quick example of using pipelines and models take a look at our official [d
 
 ### Apache Spark Support
 
-Spark NLP *6.1.5* has been built on top of Apache Spark 3.4 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, 3.4.x, and 3.5.x
+Spark NLP *6.2.0* has been built on top of Apache Spark 3.4 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, 3.4.x, and 3.5.x
 
 | Spark NLP | Apache Spark 3.5.x | Apache Spark 3.4.x | Apache Spark 3.3.x | Apache Spark 3.2.x | Apache Spark 3.1.x | Apache Spark 3.0.x | Apache Spark 2.4.x | Apache Spark 2.3.x |
 |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
@@ -159,7 +159,7 @@ Find out more about 4.x `SparkNLP` versions in our official [documentation](http
 
 ### Databricks Support
 
-Spark NLP 6.1.5 has been tested and is compatible with the following runtimes:
+Spark NLP 6.2.0 has been tested and is compatible with the following runtimes:
 
 | **CPU**            | **GPU**            |
 |--------------------|--------------------|
@@ -177,7 +177,7 @@ We are compatible with older runtimes. For a full list check databricks support
 
 ### EMR Support
 
-Spark NLP 6.1.5 has been tested and is compatible with the following EMR releases:
+Spark NLP 6.2.0 has been tested and is compatible with the following EMR releases:
 
 | **EMR Release**    |
 |--------------------|
diff --git a/build.sbt b/build.sbt
index 9d52b973822792..7335b726e1e1d7 100644
--- a/build.sbt
+++ b/build.sbt
@@ -6,7 +6,7 @@ name := getPackageName(is_silicon, is_gpu, is_aarch64)
 
 organization := "com.johnsnowlabs.nlp"
 
-version := "6.1.5"
+version := "6.2.0"
 
 (ThisBuild / scalaVersion) := scalaVer
 
diff --git a/conda/meta.yaml b/conda/meta.yaml
index 1d922001a8fb0b..69925534f92567 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "spark-nlp" %}
-{% set version = "6.1.5" %}
+{% set version = "6.2.0" %}
 
 package:
   name: {{ name|lower }}
@@ -7,7 +7,7 @@ package:
 
 source:
   url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/spark_nlp-{{ version }}.tar.gz
-  sha256: 834e5b785d6f1c6deb48195d88d11ae45433bb398dc632a63106e08fbe6f9273
+  sha256: 7cbeafc7d01afcda6f7dbb76cfb7fd34893fd2c98c4301d4d692a8962cd69f70
 
 build:
   noarch: python
diff --git a/docs/_config.yml b/docs/_config.yml
index 8ebdb84d0da1c7..2e95635c1f1a50 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -24,7 +24,7 @@ baseurl : # does not include hostname
 title   : Spark NLP
 description: > # this means to ignore newlines until "Language & timezone"
   High Performance NLP with Apache Spark
-sparknlp_version: 6.1.5 # Version to be substituted in the documentation
+sparknlp_version: 6.2.0 # Version to be substituted in the documentation
 
 
 ## => Language and Timezone
diff --git a/docs/_config_local.yml b/docs/_config_local.yml
index 2f6470c0a5435e..6df7579ee8aab8 100644
--- a/docs/_config_local.yml
+++ b/docs/_config_local.yml
@@ -27,7 +27,7 @@ baseurl : # does not include hostname
 title   : Spark NLP
 description: > # this means to ignore newlines until "Language & timezone"
   High Performance NLP with Apache Spark
-sparknlp_version: 6.1.5 # Version to be substituted in the documentation
+sparknlp_version: 6.2.0 # Version to be substituted in the documentation
 
 
 ## => Language and Timezone
diff --git a/python/README.md b/python/README.md
index d6bb96deb9e73e..2a54df6aa8a31d 100644
--- a/python/README.md
+++ b/python/README.md
@@ -63,7 +63,7 @@ $ java -version
 $ conda create -n sparknlp python=3.7 -y
 $ conda activate sparknlp
 # spark-nlp by default is based on pyspark 3.x
-$ pip install spark-nlp==6.1.5 pyspark==3.3.1
+$ pip install spark-nlp==6.2.0 pyspark==3.3.1
 ```
 
 In Python console or Jupyter `Python3` kernel:
@@ -129,7 +129,7 @@ For a quick example of using pipelines and models take a look at our official [d
 
 ### Apache Spark Support
 
-Spark NLP *6.1.5* has been built on top of Apache Spark 3.4 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, 3.4.x, and 3.5.x
+Spark NLP *6.2.0* has been built on top of Apache Spark 3.4 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, 3.4.x, and 3.5.x
 
 | Spark NLP | Apache Spark 3.5.x | Apache Spark 3.4.x | Apache Spark 3.3.x | Apache Spark 3.2.x | Apache Spark 3.1.x | Apache Spark 3.0.x | Apache Spark 2.4.x | Apache Spark 2.3.x |
 |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
@@ -159,7 +159,7 @@ Find out more about 4.x `SparkNLP` versions in our official [documentation](http
 
 ### Databricks Support
 
-Spark NLP 6.1.5 has been tested and is compatible with the following runtimes:
+Spark NLP 6.2.0 has been tested and is compatible with the following runtimes:
 
 | **CPU**            | **GPU**            |
 |--------------------|--------------------|
@@ -177,7 +177,7 @@ We are compatible with older runtimes. For a full list check databricks support
 
 ### EMR Support
 
-Spark NLP 6.1.5 has been tested and is compatible with the following EMR releases:
+Spark NLP 6.2.0 has been tested and is compatible with the following EMR releases:
 
 | **EMR Release**    |
 |--------------------|
diff --git a/python/setup.py b/python/setup.py
index 1bede4d6ce92d4..8a87a4e018189d 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -41,7 +41,7 @@
     # project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
 
-    version='6.1.5',  # Required
+    version='6.2.0',  # Required
 
     # This is a one-line description or tagline of what your project does. This
     # corresponds to the 'Summary' metadata field:
diff --git a/python/sparknlp/__init__.py b/python/sparknlp/__init__.py
index b3bc25c079b9b5..3daef62ee60290 100644
--- a/python/sparknlp/__init__.py
+++ b/python/sparknlp/__init__.py
@@ -66,7 +66,7 @@
 annotators = annotator
 embeddings = annotator
 
-__version__ = "6.1.5"
+__version__ = "6.2.0"
 
 
 def start(gpu=False,
diff --git a/scripts/colab_setup.sh b/scripts/colab_setup.sh
index ddd39d6d73c097..f37ba416d20ba5 100644
--- a/scripts/colab_setup.sh
+++ b/scripts/colab_setup.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
 #default values for pyspark, spark-nlp, and SPARK_HOME
-SPARKNLP="6.1.5"
+SPARKNLP="6.2.0"
 PYSPARK="3.4.4"
 
 while getopts s:p:g option; do
diff --git a/scripts/kaggle_setup.sh b/scripts/kaggle_setup.sh
index 1eaab5a8f6e7ff..8a6ea553cd534d 100644
--- a/scripts/kaggle_setup.sh
+++ b/scripts/kaggle_setup.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
 #default values for pyspark, spark-nlp, and SPARK_HOME
-SPARKNLP="6.1.5"
+SPARKNLP="6.2.0"
 PYSPARK="3.2.3"
 
 while getopts s:p:g option
diff --git a/scripts/sagemaker_setup.sh b/scripts/sagemaker_setup.sh
index 518b1b0cdefa63..7b948a31b2b970 100644
--- a/scripts/sagemaker_setup.sh
+++ b/scripts/sagemaker_setup.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Default values for pyspark, spark-nlp, and SPARK_HOME
-SPARKNLP="6.1.5"
+SPARKNLP="6.2.0"
 PYSPARK="3.2.3"
 
 echo "Setup SageMaker for PySpark $PYSPARK and Spark NLP $SPARKNLP"
diff --git a/src/main/scala/com/johnsnowlabs/util/Build.scala b/src/main/scala/com/johnsnowlabs/util/Build.scala
index 03ea3565ebd9c8..e39a70b96bedf8 100644
--- a/src/main/scala/com/johnsnowlabs/util/Build.scala
+++ b/src/main/scala/com/johnsnowlabs/util/Build.scala
@@ -17,5 +17,5 @@
 package com.johnsnowlabs.util
 
 object Build {
-  val version: String = "6.1.5"
+  val version: String = "6.2.0"
 }

From 5a3ece246d1b962bf99ec0c14fe98246f8be9993 Mon Sep 17 00:00:00 2001
From: github-actions 
Date: Wed, 22 Oct 2025 10:43:34 +0000
Subject: [PATCH 13/13] Update Scala and Python APIs

---
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 .../seq2seq/AutoGGUFVisionModel.scala         |   2 +-
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 2229 files changed, 12086 insertions(+), 8915 deletions(-)
 create mode 100644 docs/api/python/_api/sparknlp.common.completion_post_processing.html
 create mode 100644 docs/api/python/modules/sparknlp/common/completion_post_processing.html
 create mode 100644 docs/api/python/reference/autosummary/sparknlp/common/completion_post_processing/index.html
 create mode 100644 python/docs/_api/sparknlp.common.completion_post_processing.rst

diff --git a/docs/api/com/index.html b/docs/api/com/index.html
index 57f19700536fda..3a83d1bd75cc67 100644
--- a/docs/api/com/index.html
+++ b/docs/api/com/index.html
@@ -3,9 +3,9 @@
         
           
           
-          Spark NLP 6.1.5 ScalaDoc  - com
-          
-          
+          Spark NLP 6.2.0 ScalaDoc  - com
+          
+          
           
           
       
@@ -28,7 +28,7 @@
  • - + @@ -940,7 +940,7 @@

    Type Members

    class     - AutoGGUFVisionModel extends AnnotatorModel[AutoGGUFVisionModel] with HasBatchedAnnotateTextImage[AutoGGUFVisionModel] with HasEngine with HasLlamaCppModelProperties with HasLlamaCppInferenceProperties with HasProtectedParams + AutoGGUFVisionModel extends AnnotatorModel[AutoGGUFVisionModel] with HasBatchedAnnotateTextImage[AutoGGUFVisionModel] with HasEngine with HasLlamaCppModelProperties with HasLlamaCppInferenceProperties with HasProtectedParams with CompletionPostProcessing

    Multimodal annotator that uses the llama.cpp library to generate text completions with large diff --git a/docs/api/com/johnsnowlabs/nlp/annotators/similarity/DocumentSimilarityRankerApproach$.html b/docs/api/com/johnsnowlabs/nlp/annotators/similarity/DocumentSimilarityRankerApproach$.html index 926cff85360e97..014ed856b270f9 100644 --- a/docs/api/com/johnsnowlabs/nlp/annotators/similarity/DocumentSimilarityRankerApproach$.html +++ b/docs/api/com/johnsnowlabs/nlp/annotators/similarity/DocumentSimilarityRankerApproach$.html @@ -3,9 +3,9 @@ - Spark NLP 6.1.5 ScalaDoc - com.johnsnowlabs.nlp.annotators.similarity.DocumentSimilarityRankerApproach - - + Spark NLP 6.2.0 ScalaDoc - com.johnsnowlabs.nlp.annotators.similarity.DocumentSimilarityRankerApproach + + @@ -28,7 +28,7 @@

  • + + + + + + + + + def + + + close(): Unit + + +

    Closes the llama.cpp model backend freeing resources.

    Closes the llama.cpp model backend freeing resources. The model is reloaded when used again. +

  • diff --git a/docs/api/com/johnsnowlabs/nlp/embeddings/BGEEmbeddings$.html b/docs/api/com/johnsnowlabs/nlp/embeddings/BGEEmbeddings$.html index ca55abe3b502ab..b1bdf3a81f7d7b 100644 --- a/docs/api/com/johnsnowlabs/nlp/embeddings/BGEEmbeddings$.html +++ b/docs/api/com/johnsnowlabs/nlp/embeddings/BGEEmbeddings$.html @@ -3,9 +3,9 @@ - Spark NLP 6.1.5 ScalaDoc - com.johnsnowlabs.nlp.embeddings.BGEEmbeddings - - + Spark NLP 6.2.0 ScalaDoc - com.johnsnowlabs.nlp.embeddings.BGEEmbeddings + + @@ -28,7 +28,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotation_audio.html b/docs/api/python/_api/sparknlp.annotation_audio.html index 2c5c3a15b43219..a115c2173961f4 100644 --- a/docs/api/python/_api/sparknlp.annotation_audio.html +++ b/docs/api/python/_api/sparknlp.annotation_audio.html @@ -8,7 +8,7 @@ - sparknlp.annotation_audio module — Spark NLP 6.1.5 documentation + sparknlp.annotation_audio module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotation_image.html b/docs/api/python/_api/sparknlp.annotation_image.html index 9cc8c567f0ccda..2a69d5febdd8da 100644 --- a/docs/api/python/_api/sparknlp.annotation_image.html +++ b/docs/api/python/_api/sparknlp.annotation_image.html @@ -8,7 +8,7 @@ - sparknlp.annotation_image module — Spark NLP 6.1.5 documentation + sparknlp.annotation_image module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.audio.html b/docs/api/python/_api/sparknlp.annotator.audio.html index 40b8a87728a002..25ea4b34a6ed07 100644 --- a/docs/api/python/_api/sparknlp.annotator.audio.html +++ b/docs/api/python/_api/sparknlp.annotator.audio.html @@ -8,7 +8,7 @@ - sparknlp.annotator.audio package — Spark NLP 6.1.5 documentation + sparknlp.annotator.audio package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.audio.hubert_for_ctc.html b/docs/api/python/_api/sparknlp.annotator.audio.hubert_for_ctc.html index 85b9fe73c6c828..612f2da4411972 100644 --- a/docs/api/python/_api/sparknlp.annotator.audio.hubert_for_ctc.html +++ b/docs/api/python/_api/sparknlp.annotator.audio.hubert_for_ctc.html @@ -8,7 +8,7 @@ - sparknlp.annotator.audio.hubert_for_ctc module — Spark NLP 6.1.5 documentation + sparknlp.annotator.audio.hubert_for_ctc module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.audio.wav2vec2_for_ctc.html b/docs/api/python/_api/sparknlp.annotator.audio.wav2vec2_for_ctc.html index a167992fb418da..301182883ec6d8 100644 --- a/docs/api/python/_api/sparknlp.annotator.audio.wav2vec2_for_ctc.html +++ b/docs/api/python/_api/sparknlp.annotator.audio.wav2vec2_for_ctc.html @@ -8,7 +8,7 @@ - sparknlp.annotator.audio.wav2vec2_for_ctc module — Spark NLP 6.1.5 documentation + sparknlp.annotator.audio.wav2vec2_for_ctc module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.audio.whisper_for_ctc.html b/docs/api/python/_api/sparknlp.annotator.audio.whisper_for_ctc.html index e0fa13cda1da7d..aa9b834b7bae25 100644 --- a/docs/api/python/_api/sparknlp.annotator.audio.whisper_for_ctc.html +++ b/docs/api/python/_api/sparknlp.annotator.audio.whisper_for_ctc.html @@ -8,7 +8,7 @@ - sparknlp.annotator.audio.whisper_for_ctc module — Spark NLP 6.1.5 documentation + sparknlp.annotator.audio.whisper_for_ctc module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.chunk2_doc.html b/docs/api/python/_api/sparknlp.annotator.chunk2_doc.html index 272d8c97df33ac..bf3ff6e86feb83 100644 --- a/docs/api/python/_api/sparknlp.annotator.chunk2_doc.html +++ b/docs/api/python/_api/sparknlp.annotator.chunk2_doc.html @@ -8,7 +8,7 @@ - sparknlp.annotator.chunk2_doc module — Spark NLP 6.1.5 documentation + sparknlp.annotator.chunk2_doc module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.chunker.html b/docs/api/python/_api/sparknlp.annotator.chunker.html index c68dc7c58ec090..8b3c28e98dff68 100644 --- a/docs/api/python/_api/sparknlp.annotator.chunker.html +++ b/docs/api/python/_api/sparknlp.annotator.chunker.html @@ -8,7 +8,7 @@ - sparknlp.annotator.chunker module — Spark NLP 6.1.5 documentation + sparknlp.annotator.chunker module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_multiple_choice.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_multiple_choice.html index b9a65fbd9aa4c3..91ac6927f59c7c 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_multiple_choice.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_multiple_choice.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.albert_for_multiple_choice module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.albert_for_multiple_choice module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_question_answering.html index f6b70521d371d4..830792a33ef441 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.albert_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.albert_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_sequence_classification.html index 11afb003ee192f..22eb235732de54 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.albert_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.albert_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_token_classification.html index 8d61b78a367fc0..302d0bedee5f87 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.albert_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.albert_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification.html index ddc435b1590d05..03d71e7eb63a83 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bart_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bart_for_zero_shot_classification.html index 4dd84519ce6fe7..e62f5147cd9b18 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bart_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bart_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.bart_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.bart_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_multiple_choice.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_multiple_choice.html index 10a0b3981978e0..143d0180f8d088 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_multiple_choice.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_multiple_choice.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.bert_for_multiple_choice module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.bert_for_multiple_choice module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_question_answering.html index 2d0dc4e75c41e8..56929def123290 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.bert_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.bert_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_sequence_classification.html index 7d9d52eb551d08..bee2736eeab03e 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.bert_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.bert_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_token_classification.html index cf8c6a94b732c6..62e9aa9aa8bb80 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.bert_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.bert_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification.html index 2e0a1e8ba8e122..6c9d1087e67251 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_question_answering.html index 22ed94081cb98b..ba064536ef8033 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.camembert_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.camembert_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_sequence_classification.html index 1802b4178fee95..44bf4638eeb018 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.camembert_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.camembert_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_token_classification.html index 1cb2c616f8c57f..31cfc6eeacc9ff 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.camembert_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.camembert_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification.html index 8d61ab7c693f46..164ad73a4471a7 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.classifier_dl.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.classifier_dl.html index f47d7fbbb1a2e8..37f3cc98e53a62 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.classifier_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.classifier_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.classifier_dl module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.classifier_dl module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_question_answering.html index 8254e8223521c5..29262963fc5bd5 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.deberta_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.deberta_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_sequence_classification.html index 61f29c5d1464d1..b63e187012850f 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.deberta_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.deberta_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_token_classification.html index 98674130133e32..159d4164aeb679 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.deberta_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.deberta_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification.html index 3f50bc2f4bc78a..568dfcbaad528b 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_question_answering.html index d1fdd7da4afb06..2b66e55306219d 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.distil_bert_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.distil_bert_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_sequence_classification.html index 066f1c64c3c767..bbd43b16d1c054 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.distil_bert_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.distil_bert_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_token_classification.html index 97c6b5fdaea730..49ad4a81e9ca2c 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.distil_bert_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.distil_bert_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_zero_shot_classification.html index 3e37973ca6eaee..30a43294e37ae0 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distil_bert_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.distil_bert_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.distil_bert_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distilbert_for_multiple_choice.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distilbert_for_multiple_choice.html index 01c65df7928596..1473fea8e6604b 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.distilbert_for_multiple_choice.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.distilbert_for_multiple_choice.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.distilbert_for_multiple_choice module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.distilbert_for_multiple_choice module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.html index c7e348949311db..92e1e492079042 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl package — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_question_answering.html index bb092a2ee59ace..f7965e5dd22e62 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.longformer_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.longformer_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_sequence_classification.html index 5aabb77df07aaa..ca66a3a8914c22 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.longformer_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.longformer_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_token_classification.html index d15b6ae2689fef..a90b2a32f6f390 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.longformer_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.longformer_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.longformer_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_question_answering.html index b834463152146b..8c4fcdfa9d1253 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.mpnet_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.mpnet_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification.html index 36e40e6769e6b5..3c98425021366c 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_token_classification.html index 04ac73456707fb..096a7094e3223b 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.mpnet_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.mpnet_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.mpnet_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.multi_classifier_dl.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.multi_classifier_dl.html index d67cdd9ad2846f..5a3bd93ae5bb63 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.multi_classifier_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.multi_classifier_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.multi_classifier_dl module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.multi_classifier_dl module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_multiple_choice.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_multiple_choice.html index 5fb5e2fb2200b5..bb68962a787329 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_multiple_choice.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_multiple_choice.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.roberta_for_multiple_choice module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.roberta_for_multiple_choice module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_question_answering.html index 007534efd57bb5..0cfc9c732c2a6c 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.roberta_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.roberta_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_sequence_classification.html index 630484035f4ece..355bf244359dd2 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.roberta_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.roberta_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_token_classification.html index 1b621047d810d3..318426ff366732 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.roberta_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.roberta_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_zero_shot_classification.html index 3c3dcbaff97d71..b987e26a97922a 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.roberta_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.roberta_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.roberta_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.sentiment_dl.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.sentiment_dl.html index 82198df856e7c8..d3095bf07db4ae 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.sentiment_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.sentiment_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.sentiment_dl module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.sentiment_dl module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.tapas_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.tapas_for_question_answering.html index e5c753f2385906..cce2d433170ca1 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.tapas_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.tapas_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.tapas_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.tapas_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_multiple_choice.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_multiple_choice.html index ac7c50f79b97c1..0e6c37ed646812 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_multiple_choice.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_multiple_choice.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.xlm_roberta_for_multiple_choice module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.xlm_roberta_for_multiple_choice module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering.html index 5f0447b7f96fb0..bba6405b4a62ac 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification.html index 723e892c33d277..cc17033578ab8c 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification.html index 9dff1c9103e724..eb97992c3990b9 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification.html index 4265b1bb60ed83..e1358affc7e008 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification.html index 642774f16f905a..e7002c287ecf16 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_token_classification.html b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_token_classification.html index cb8b4914606409..bf0cbe04613481 100644 --- a/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_token_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.classifier_dl.xlnet_for_token_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.classifier_dl.xlnet_for_token_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.classifier_dl.xlnet_for_token_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cleaners.cleaner.html b/docs/api/python/_api/sparknlp.annotator.cleaners.cleaner.html index 435ddbcfbd6ee6..f1460443187e8a 100644 --- a/docs/api/python/_api/sparknlp.annotator.cleaners.cleaner.html +++ b/docs/api/python/_api/sparknlp.annotator.cleaners.cleaner.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cleaners.cleaner module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cleaners.cleaner module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cleaners.extractor.html b/docs/api/python/_api/sparknlp.annotator.cleaners.extractor.html index 793480f0ec78d8..a9a72fbf634e63 100644 --- a/docs/api/python/_api/sparknlp.annotator.cleaners.extractor.html +++ b/docs/api/python/_api/sparknlp.annotator.cleaners.extractor.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cleaners.extractor module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cleaners.extractor module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cleaners.html b/docs/api/python/_api/sparknlp.annotator.cleaners.html index 3046a0b9c2e660..7e74e660a5ce1e 100644 --- a/docs/api/python/_api/sparknlp.annotator.cleaners.html +++ b/docs/api/python/_api/sparknlp.annotator.cleaners.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cleaners package — Spark NLP 6.1.5 documentation + sparknlp.annotator.cleaners package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.coref.html b/docs/api/python/_api/sparknlp.annotator.coref.html index fbc86ea81788da..f65aa884cc9f30 100644 --- a/docs/api/python/_api/sparknlp.annotator.coref.html +++ b/docs/api/python/_api/sparknlp.annotator.coref.html @@ -8,7 +8,7 @@ - sparknlp.annotator.coref package — Spark NLP 6.1.5 documentation + sparknlp.annotator.coref package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.coref.spanbert_coref.html b/docs/api/python/_api/sparknlp.annotator.coref.spanbert_coref.html index a57b448114c003..2b50a7728e0273 100644 --- a/docs/api/python/_api/sparknlp.annotator.coref.spanbert_coref.html +++ b/docs/api/python/_api/sparknlp.annotator.coref.spanbert_coref.html @@ -8,7 +8,7 @@ - sparknlp.annotator.coref.spanbert_coref module — Spark NLP 6.1.5 documentation + sparknlp.annotator.coref.spanbert_coref module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.blip_for_question_answering.html b/docs/api/python/_api/sparknlp.annotator.cv.blip_for_question_answering.html index 45ce1b1d20833b..329e7acc27e339 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.blip_for_question_answering.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.blip_for_question_answering.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.blip_for_question_answering module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.blip_for_question_answering module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.clip_for_zero_shot_classification.html b/docs/api/python/_api/sparknlp.annotator.cv.clip_for_zero_shot_classification.html index 7d63d2b483a7fa..87869839ed8df4 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.clip_for_zero_shot_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.clip_for_zero_shot_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.clip_for_zero_shot_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.clip_for_zero_shot_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.convnext_for_image_classification.html b/docs/api/python/_api/sparknlp.annotator.cv.convnext_for_image_classification.html index 76432946335d2a..a410a75f225637 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.convnext_for_image_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.convnext_for_image_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.convnext_for_image_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.convnext_for_image_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.florence2_transformer.html b/docs/api/python/_api/sparknlp.annotator.cv.florence2_transformer.html index 5b49c7a05dea09..e2c8a502efcb55 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.florence2_transformer.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.florence2_transformer.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.florence2_transformer module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.florence2_transformer module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.gemma3_for_multimodal.html b/docs/api/python/_api/sparknlp.annotator.cv.gemma3_for_multimodal.html index 54712e000f3c5e..9445149233f0d1 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.gemma3_for_multimodal.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.gemma3_for_multimodal.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.gemma3_for_multimodal module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.gemma3_for_multimodal module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.html b/docs/api/python/_api/sparknlp.annotator.cv.html index dd71d5259f4955..47a9d82fb77bea 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv package — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.internvl_for_multimodal.html b/docs/api/python/_api/sparknlp.annotator.cv.internvl_for_multimodal.html index e3aed43ab5c843..b380bcfe4b9f8e 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.internvl_for_multimodal.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.internvl_for_multimodal.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.internvl_for_multimodal module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.internvl_for_multimodal module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.janus_for_multimodal.html b/docs/api/python/_api/sparknlp.annotator.cv.janus_for_multimodal.html index cdedc2275cd27a..b48d8c52d9218e 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.janus_for_multimodal.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.janus_for_multimodal.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.janus_for_multimodal module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.janus_for_multimodal module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.llava_for_multimodal.html b/docs/api/python/_api/sparknlp.annotator.cv.llava_for_multimodal.html index f903e189d61196..bfc2056a06b725 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.llava_for_multimodal.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.llava_for_multimodal.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.llava_for_multimodal module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.llava_for_multimodal module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.mllama_for_multimodal.html b/docs/api/python/_api/sparknlp.annotator.cv.mllama_for_multimodal.html index bf6cb23c79fc74..549efd1b15bac7 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.mllama_for_multimodal.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.mllama_for_multimodal.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.mllama_for_multimodal module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.mllama_for_multimodal module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.paligemma_for_multimodal.html b/docs/api/python/_api/sparknlp.annotator.cv.paligemma_for_multimodal.html index 9c5a91768d86d2..1b231debbf7761 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.paligemma_for_multimodal.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.paligemma_for_multimodal.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.paligemma_for_multimodal module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.paligemma_for_multimodal module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.phi3_vision_for_multimodal.html b/docs/api/python/_api/sparknlp.annotator.cv.phi3_vision_for_multimodal.html index a10f22858c6272..18709a2e8a266a 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.phi3_vision_for_multimodal.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.phi3_vision_for_multimodal.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.phi3_vision_for_multimodal module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.phi3_vision_for_multimodal module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.qwen2vl_transformer.html b/docs/api/python/_api/sparknlp.annotator.cv.qwen2vl_transformer.html index df4a0d830d1ae0..6aa12c8717f6ba 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.qwen2vl_transformer.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.qwen2vl_transformer.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.qwen2vl_transformer module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.qwen2vl_transformer module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.smolvlm_transformer.html b/docs/api/python/_api/sparknlp.annotator.cv.smolvlm_transformer.html index b2a1f37c27425a..297e99d292a21f 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.smolvlm_transformer.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.smolvlm_transformer.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.smolvlm_transformer module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.smolvlm_transformer module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.swin_for_image_classification.html b/docs/api/python/_api/sparknlp.annotator.cv.swin_for_image_classification.html index ad1aba665112d0..2ebd9f59a1f8fb 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.swin_for_image_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.swin_for_image_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.swin_for_image_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.swin_for_image_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning.html b/docs/api/python/_api/sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning.html index d3145d6a035b21..4e37c499012279 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.cv.vit_for_image_classification.html b/docs/api/python/_api/sparknlp.annotator.cv.vit_for_image_classification.html index 7bc771e9d119b3..b04eb0d2b3a3eb 100644 --- a/docs/api/python/_api/sparknlp.annotator.cv.vit_for_image_classification.html +++ b/docs/api/python/_api/sparknlp.annotator.cv.vit_for_image_classification.html @@ -8,7 +8,7 @@ - sparknlp.annotator.cv.vit_for_image_classification module — Spark NLP 6.1.5 documentation + sparknlp.annotator.cv.vit_for_image_classification module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.dataframe_optimizer.html b/docs/api/python/_api/sparknlp.annotator.dataframe_optimizer.html index 0d944d2880c0a6..06b4a193892d47 100644 --- a/docs/api/python/_api/sparknlp.annotator.dataframe_optimizer.html +++ b/docs/api/python/_api/sparknlp.annotator.dataframe_optimizer.html @@ -8,7 +8,7 @@ - sparknlp.annotator.dataframe_optimizer module — Spark NLP 6.1.5 documentation + sparknlp.annotator.dataframe_optimizer module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.date2_chunk.html b/docs/api/python/_api/sparknlp.annotator.date2_chunk.html index 89914770f2e8ac..1d70bd9a06e3d4 100644 --- a/docs/api/python/_api/sparknlp.annotator.date2_chunk.html +++ b/docs/api/python/_api/sparknlp.annotator.date2_chunk.html @@ -8,7 +8,7 @@ - sparknlp.annotator.date2_chunk module — Spark NLP 6.1.5 documentation + sparknlp.annotator.date2_chunk module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.dependency.dependency_parser.html b/docs/api/python/_api/sparknlp.annotator.dependency.dependency_parser.html index 627f6470b313e7..d0d31ce1961ff0 100644 --- a/docs/api/python/_api/sparknlp.annotator.dependency.dependency_parser.html +++ b/docs/api/python/_api/sparknlp.annotator.dependency.dependency_parser.html @@ -8,7 +8,7 @@ - sparknlp.annotator.dependency.dependency_parser module — Spark NLP 6.1.5 documentation + sparknlp.annotator.dependency.dependency_parser module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.dependency.html b/docs/api/python/_api/sparknlp.annotator.dependency.html index f21d3f03f6057b..b8b037a9330ae3 100644 --- a/docs/api/python/_api/sparknlp.annotator.dependency.html +++ b/docs/api/python/_api/sparknlp.annotator.dependency.html @@ -8,7 +8,7 @@ - sparknlp.annotator.dependency package — Spark NLP 6.1.5 documentation + sparknlp.annotator.dependency package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.dependency.typed_dependency_parser.html b/docs/api/python/_api/sparknlp.annotator.dependency.typed_dependency_parser.html index 4476c07c92e889..e0d3662766c683 100644 --- a/docs/api/python/_api/sparknlp.annotator.dependency.typed_dependency_parser.html +++ b/docs/api/python/_api/sparknlp.annotator.dependency.typed_dependency_parser.html @@ -8,7 +8,7 @@ - sparknlp.annotator.dependency.typed_dependency_parser module — Spark NLP 6.1.5 documentation + sparknlp.annotator.dependency.typed_dependency_parser module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.document_character_text_splitter.html b/docs/api/python/_api/sparknlp.annotator.document_character_text_splitter.html index 0191cdd8a6443b..add92d280ea80b 100644 --- a/docs/api/python/_api/sparknlp.annotator.document_character_text_splitter.html +++ b/docs/api/python/_api/sparknlp.annotator.document_character_text_splitter.html @@ -8,7 +8,7 @@ - sparknlp.annotator.document_character_text_splitter module — Spark NLP 6.1.5 documentation + sparknlp.annotator.document_character_text_splitter module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.document_normalizer.html b/docs/api/python/_api/sparknlp.annotator.document_normalizer.html index 6e1e0afb728443..82b2f4076f94b4 100644 --- a/docs/api/python/_api/sparknlp.annotator.document_normalizer.html +++ b/docs/api/python/_api/sparknlp.annotator.document_normalizer.html @@ -8,7 +8,7 @@ - sparknlp.annotator.document_normalizer module — Spark NLP 6.1.5 documentation + sparknlp.annotator.document_normalizer module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • @@ -784,6 +785,11 @@ action = Param(parent='undefined', name='action', doc='action to perform applying regex patterns on text')[source]#
    +
    +
    +autoMode = Param(parent='undefined', name='autoMode', doc="Enables a predefined cleaning mode combining multiple text cleaner functions (e.g., 'light_clean', 'document_clean', 'html_clean', 'full_auto').")[source]#
    +
    +
    encoding = Param(parent='undefined', name='encoding', doc='file encoding to apply on normalized documents')[source]#
    @@ -814,6 +820,11 @@ policy = Param(parent='undefined', name='policy', doc='policy to remove pattern from text')[source]#
    +
    +
    +presetPattern = Param(parent='undefined', name='presetPattern', doc="Selects a single text cleaning function from the functional presets (e.g., 'CLEAN_BULLETS', 'CLEAN_DASHES', etc.).")[source]#
    +
    +
    replacement = Param(parent='undefined', name='replacement', doc='replacement string to apply when regexes match')[source]#
    @@ -834,6 +845,20 @@
    +
    +
    +setAutoMode(value)[source]#
    +

    Sets an automatic text cleaning mode using predefined groups of cleaning functions.

    +
    +
    Parameters:
    +
    +
    valuestr

    Auto cleaning mode, e.g., ‘light_clean’, ‘document_clean’, ‘social_clean’, ‘html_clean’, ‘full_auto’.

    +
    +
    +
    +
    +
    +
    setEncoding(value)[source]#
    @@ -893,6 +918,20 @@
    +
    +
    +setPresetPattern(value)[source]#
    +

    Sets a single text cleaning preset pattern.

    +
    +
    Parameters:
    +
    +
    valuestr

    Preset cleaning pattern name, e.g., ‘CLEAN_BULLETS’, ‘CLEAN_DASHES’.

    +
    +
    +
    +
    +
    +
    setReplacement(value)[source]#
    @@ -939,18 +978,22 @@
  • EntityRulerModel
    • diff --git a/docs/api/python/_api/sparknlp.annotator.er.html b/docs/api/python/_api/sparknlp.annotator.er.html index 9a682ac3169833..b121f1cfd0e1b1 100644 --- a/docs/api/python/_api/sparknlp.annotator.er.html +++ b/docs/api/python/_api/sparknlp.annotator.er.html @@ -8,7 +8,7 @@ - sparknlp.annotator.er package — Spark NLP 6.1.5 documentation + sparknlp.annotator.er package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • @@ -713,13 +714,17 @@

    SubmodulesEntityRulerModel diff --git a/docs/api/python/_api/sparknlp.annotator.graph_extraction.html b/docs/api/python/_api/sparknlp.annotator.graph_extraction.html index 132ad9101221b5..fe822ff271386c 100644 --- a/docs/api/python/_api/sparknlp.annotator.graph_extraction.html +++ b/docs/api/python/_api/sparknlp.annotator.graph_extraction.html @@ -8,7 +8,7 @@ - sparknlp.annotator.graph_extraction module — Spark NLP 6.1.5 documentation + sparknlp.annotator.graph_extraction module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • diff --git a/docs/api/python/_api/sparknlp.annotator.html b/docs/api/python/_api/sparknlp.annotator.html index e62216ffb8b892..d902c8f70a3d9a 100644 --- a/docs/api/python/_api/sparknlp.annotator.html +++ b/docs/api/python/_api/sparknlp.annotator.html @@ -8,7 +8,7 @@ - sparknlp.annotator package — Spark NLP 6.1.5 documentation + sparknlp.annotator package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
  • sparknlp.common.annotator_model module
  • sparknlp.common.annotator_properties module
  • sparknlp.common.annotator_type module
    • +
  • sparknlp.common.completion_post_processing module
  • sparknlp.common.coverage_result module
  • sparknlp.common.match_strategy module
  • sparknlp.common.properties module
    • @@ -1578,18 +1579,22 @@

    Submodulessparknlp.annotator.document_normalizer module
    • DocumentNormalizer
      • diff --git a/docs/api/python/_api/sparknlp.annotator.keyword_extraction.html b/docs/api/python/_api/sparknlp.annotator.keyword_extraction.html index ea6b8026d82b2e..90ef1d83434b80 100644 --- a/docs/api/python/_api/sparknlp.annotator.keyword_extraction.html +++ b/docs/api/python/_api/sparknlp.annotator.keyword_extraction.html @@ -8,7 +8,7 @@ - sparknlp.annotator.keyword_extraction package — Spark NLP 6.1.5 documentation + sparknlp.annotator.keyword_extraction package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.keyword_extraction.yake_keyword_extraction.html b/docs/api/python/_api/sparknlp.annotator.keyword_extraction.yake_keyword_extraction.html index 7f2a815017f324..2c01755156a155 100644 --- a/docs/api/python/_api/sparknlp.annotator.keyword_extraction.yake_keyword_extraction.html +++ b/docs/api/python/_api/sparknlp.annotator.keyword_extraction.yake_keyword_extraction.html @@ -8,7 +8,7 @@ - sparknlp.annotator.keyword_extraction.yake_keyword_extraction module — Spark NLP 6.1.5 documentation + sparknlp.annotator.keyword_extraction.yake_keyword_extraction module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ld_dl.html b/docs/api/python/_api/sparknlp.annotator.ld_dl.html index 62e2518cc2a0df..185640fc65da28 100644 --- a/docs/api/python/_api/sparknlp.annotator.ld_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.ld_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ld_dl package — Spark NLP 6.1.5 documentation + sparknlp.annotator.ld_dl package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ld_dl.language_detector_dl.html b/docs/api/python/_api/sparknlp.annotator.ld_dl.language_detector_dl.html index acd1e806c5676c..8bf83654d14f64 100644 --- a/docs/api/python/_api/sparknlp.annotator.ld_dl.language_detector_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.ld_dl.language_detector_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ld_dl.language_detector_dl module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ld_dl.language_detector_dl module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.lemmatizer.html b/docs/api/python/_api/sparknlp.annotator.lemmatizer.html index 23225eec52a159..cddb26e195e869 100644 --- a/docs/api/python/_api/sparknlp.annotator.lemmatizer.html +++ b/docs/api/python/_api/sparknlp.annotator.lemmatizer.html @@ -8,7 +8,7 @@ - sparknlp.annotator.lemmatizer module — Spark NLP 6.1.5 documentation + sparknlp.annotator.lemmatizer module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.matcher.big_text_matcher.html b/docs/api/python/_api/sparknlp.annotator.matcher.big_text_matcher.html index 325d43aa5c78c2..268df64230e8a9 100644 --- a/docs/api/python/_api/sparknlp.annotator.matcher.big_text_matcher.html +++ b/docs/api/python/_api/sparknlp.annotator.matcher.big_text_matcher.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.big_text_matcher module — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.big_text_matcher module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.matcher.date_matcher.html b/docs/api/python/_api/sparknlp.annotator.matcher.date_matcher.html index fdc1b89adb9ae8..6e9b4d8e932fc2 100644 --- a/docs/api/python/_api/sparknlp.annotator.matcher.date_matcher.html +++ b/docs/api/python/_api/sparknlp.annotator.matcher.date_matcher.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.date_matcher module — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.date_matcher module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.matcher.html b/docs/api/python/_api/sparknlp.annotator.matcher.html index 40ae671ea8c270..253bbe07350df4 100644 --- a/docs/api/python/_api/sparknlp.annotator.matcher.html +++ b/docs/api/python/_api/sparknlp.annotator.matcher.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher package — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.matcher.multi_date_matcher.html b/docs/api/python/_api/sparknlp.annotator.matcher.multi_date_matcher.html index d2f5c9cfcb83b4..29f948cb0ccece 100644 --- a/docs/api/python/_api/sparknlp.annotator.matcher.multi_date_matcher.html +++ b/docs/api/python/_api/sparknlp.annotator.matcher.multi_date_matcher.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.multi_date_matcher module — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.multi_date_matcher module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.matcher.regex_matcher.html b/docs/api/python/_api/sparknlp.annotator.matcher.regex_matcher.html index c71086326a4145..b5a6373332c4c4 100644 --- a/docs/api/python/_api/sparknlp.annotator.matcher.regex_matcher.html +++ b/docs/api/python/_api/sparknlp.annotator.matcher.regex_matcher.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.regex_matcher module — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.regex_matcher module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.matcher.text_matcher.html b/docs/api/python/_api/sparknlp.annotator.matcher.text_matcher.html index e9393fefcfa4ee..c797d992200ffe 100644 --- a/docs/api/python/_api/sparknlp.annotator.matcher.text_matcher.html +++ b/docs/api/python/_api/sparknlp.annotator.matcher.text_matcher.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.text_matcher module — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.text_matcher module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.n_gram_generator.html b/docs/api/python/_api/sparknlp.annotator.n_gram_generator.html index 4de6c27c353692..bcb8fe4f65a34c 100644 --- a/docs/api/python/_api/sparknlp.annotator.n_gram_generator.html +++ b/docs/api/python/_api/sparknlp.annotator.n_gram_generator.html @@ -8,7 +8,7 @@ - sparknlp.annotator.n_gram_generator module — Spark NLP 6.1.5 documentation + sparknlp.annotator.n_gram_generator module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.html b/docs/api/python/_api/sparknlp.annotator.ner.html index 9c3a02294eda7f..e1e1af78796bb6 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner package — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.ner_approach.html b/docs/api/python/_api/sparknlp.annotator.ner.ner_approach.html index 4804e2d6089ba5..fff9a0d9ed96d9 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.ner_approach.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.ner_approach.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_approach module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_approach module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.ner_converter.html b/docs/api/python/_api/sparknlp.annotator.ner.ner_converter.html index 21e29a79f156c6..6257e94ea9c3b1 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.ner_converter.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.ner_converter.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_converter module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_converter module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.ner_crf.html b/docs/api/python/_api/sparknlp.annotator.ner.ner_crf.html index c3a8040e199795..35d62183b4abf5 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.ner_crf.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.ner_crf.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_crf module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_crf module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.ner_dl.html b/docs/api/python/_api/sparknlp.annotator.ner.ner_dl.html index 231a5e8017d0fc..babe195ac9c83c 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.ner_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.ner_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_dl module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_dl module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.ner_dl_graph_checker.html b/docs/api/python/_api/sparknlp.annotator.ner.ner_dl_graph_checker.html index 80631631e31eb9..daaa176252bbc9 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.ner_dl_graph_checker.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.ner_dl_graph_checker.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_dl_graph_checker module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_dl_graph_checker module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.ner_overwriter.html b/docs/api/python/_api/sparknlp.annotator.ner.ner_overwriter.html index 277dc9b46b85f6..8df1f402a54302 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.ner_overwriter.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.ner_overwriter.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_overwriter module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_overwriter module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.ner.zero_shot_ner_model.html b/docs/api/python/_api/sparknlp.annotator.ner.zero_shot_ner_model.html index 602d9daa041372..f59bf90dcde2fc 100644 --- a/docs/api/python/_api/sparknlp.annotator.ner.zero_shot_ner_model.html +++ b/docs/api/python/_api/sparknlp.annotator.ner.zero_shot_ner_model.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.zero_shot_ner_model module — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.zero_shot_ner_model module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.normalizer.html b/docs/api/python/_api/sparknlp.annotator.normalizer.html index a208711073b81c..715e49fa5ba368 100644 --- a/docs/api/python/_api/sparknlp.annotator.normalizer.html +++ b/docs/api/python/_api/sparknlp.annotator.normalizer.html @@ -8,7 +8,7 @@ - sparknlp.annotator.normalizer module — Spark NLP 6.1.5 documentation + sparknlp.annotator.normalizer module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.openai.html b/docs/api/python/_api/sparknlp.annotator.openai.html index dc822145aeb640..9e27aa33205e3a 100644 --- a/docs/api/python/_api/sparknlp.annotator.openai.html +++ b/docs/api/python/_api/sparknlp.annotator.openai.html @@ -8,7 +8,7 @@ - sparknlp.annotator.openai package — Spark NLP 6.1.5 documentation + sparknlp.annotator.openai package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.openai.openai_completion.html b/docs/api/python/_api/sparknlp.annotator.openai.openai_completion.html index 0d41fb6341f617..e81eacaa3de4cd 100644 --- a/docs/api/python/_api/sparknlp.annotator.openai.openai_completion.html +++ b/docs/api/python/_api/sparknlp.annotator.openai.openai_completion.html @@ -8,7 +8,7 @@ - sparknlp.annotator.openai.openai_completion module — Spark NLP 6.1.5 documentation + sparknlp.annotator.openai.openai_completion module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.openai.openai_embeddings.html b/docs/api/python/_api/sparknlp.annotator.openai.openai_embeddings.html index 693fc6aea52edb..76d011de66c314 100644 --- a/docs/api/python/_api/sparknlp.annotator.openai.openai_embeddings.html +++ b/docs/api/python/_api/sparknlp.annotator.openai.openai_embeddings.html @@ -8,7 +8,7 @@ - sparknlp.annotator.openai.openai_embeddings module — Spark NLP 6.1.5 documentation + sparknlp.annotator.openai.openai_embeddings module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.param.classifier_encoder.html b/docs/api/python/_api/sparknlp.annotator.param.classifier_encoder.html index 3f084c16fff412..9de26a76db244d 100644 --- a/docs/api/python/_api/sparknlp.annotator.param.classifier_encoder.html +++ b/docs/api/python/_api/sparknlp.annotator.param.classifier_encoder.html @@ -8,7 +8,7 @@ - sparknlp.annotator.param.classifier_encoder module — Spark NLP 6.1.5 documentation + sparknlp.annotator.param.classifier_encoder module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.param.evaluation_dl_params.html b/docs/api/python/_api/sparknlp.annotator.param.evaluation_dl_params.html index e1c9604b033190..59298936558334 100644 --- a/docs/api/python/_api/sparknlp.annotator.param.evaluation_dl_params.html +++ b/docs/api/python/_api/sparknlp.annotator.param.evaluation_dl_params.html @@ -8,7 +8,7 @@ - sparknlp.annotator.param.evaluation_dl_params module — Spark NLP 6.1.5 documentation + sparknlp.annotator.param.evaluation_dl_params module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.param.html b/docs/api/python/_api/sparknlp.annotator.param.html index 3848a58b6341b5..f4c5690fd865c4 100644 --- a/docs/api/python/_api/sparknlp.annotator.param.html +++ b/docs/api/python/_api/sparknlp.annotator.param.html @@ -8,7 +8,7 @@ - sparknlp.annotator.param package — Spark NLP 6.1.5 documentation + sparknlp.annotator.param package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.pos.html b/docs/api/python/_api/sparknlp.annotator.pos.html index 9e53f534fd5ea4..c1075b00c97e3b 100644 --- a/docs/api/python/_api/sparknlp.annotator.pos.html +++ b/docs/api/python/_api/sparknlp.annotator.pos.html @@ -8,7 +8,7 @@ - sparknlp.annotator.pos package — Spark NLP 6.1.5 documentation + sparknlp.annotator.pos package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.pos.perceptron.html b/docs/api/python/_api/sparknlp.annotator.pos.perceptron.html index d61b76b6b8a379..b9edcbf6d0de08 100644 --- a/docs/api/python/_api/sparknlp.annotator.pos.perceptron.html +++ b/docs/api/python/_api/sparknlp.annotator.pos.perceptron.html @@ -8,7 +8,7 @@ - sparknlp.annotator.pos.perceptron module — Spark NLP 6.1.5 documentation + sparknlp.annotator.pos.perceptron module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.sentence.html b/docs/api/python/_api/sparknlp.annotator.sentence.html index 54373ab4105ee9..eaa437817094ab 100644 --- a/docs/api/python/_api/sparknlp.annotator.sentence.html +++ b/docs/api/python/_api/sparknlp.annotator.sentence.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentence package — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentence package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector.html b/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector.html index dbdd24bfdff22c..40cf103cf4d422 100644 --- a/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector.html +++ b/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentence.sentence_detector module — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentence.sentence_detector module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector_dl.html b/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector_dl.html index d2149f34b0b02a..6517af7898fde2 100644 --- a/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector_dl.html +++ b/docs/api/python/_api/sparknlp.annotator.sentence.sentence_detector_dl.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentence.sentence_detector_dl module — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentence.sentence_detector_dl module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.sentiment.html b/docs/api/python/_api/sparknlp.annotator.sentiment.html index 1bea46005b0a76..efd349527714e9 100644 --- a/docs/api/python/_api/sparknlp.annotator.sentiment.html +++ b/docs/api/python/_api/sparknlp.annotator.sentiment.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentiment package — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentiment package — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.sentiment.sentiment_detector.html b/docs/api/python/_api/sparknlp.annotator.sentiment.sentiment_detector.html index 16e9a392e25b83..d572d1f5db7e27 100644 --- a/docs/api/python/_api/sparknlp.annotator.sentiment.sentiment_detector.html +++ b/docs/api/python/_api/sparknlp.annotator.sentiment.sentiment_detector.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentiment.sentiment_detector module — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentiment.sentiment_detector module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.sentiment.vivekn_sentiment.html b/docs/api/python/_api/sparknlp.annotator.sentiment.vivekn_sentiment.html index 5d61213385bd21..c150c162d68a90 100644 --- a/docs/api/python/_api/sparknlp.annotator.sentiment.vivekn_sentiment.html +++ b/docs/api/python/_api/sparknlp.annotator.sentiment.vivekn_sentiment.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentiment.vivekn_sentiment module — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentiment.vivekn_sentiment module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • diff --git a/docs/api/python/_api/sparknlp.annotator.seq2seq.auto_gguf_model.html b/docs/api/python/_api/sparknlp.annotator.seq2seq.auto_gguf_model.html index 8c53ce179d0939..ed5a50fcd8e44b 100644 --- a/docs/api/python/_api/sparknlp.annotator.seq2seq.auto_gguf_model.html +++ b/docs/api/python/_api/sparknlp.annotator.seq2seq.auto_gguf_model.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.auto_gguf_model module — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.auto_gguf_model module — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -576,6 +576,7 @@
    • sparknlp.common.annotator_model module
    • sparknlp.common.annotator_properties module
    • sparknlp.common.annotator_type module
      • +
    • sparknlp.common.completion_post_processing module
    • sparknlp.common.coverage_result module
    • sparknlp.common.match_strategy module
    • sparknlp.common.properties module
      • @@ -696,7 +697,7 @@
      class AutoGGUFModel(classname='com.johnsnowlabs.nlp.annotators.seq2seq.AutoGGUFModel', java_model=None)[source]#
      -

      Bases: AnnotatorModel, HasBatchedAnnotate, HasLlamaCppProperties

      +

      Bases: AnnotatorModel, HasBatchedAnnotate, HasLlamaCppProperties, CompletionPostProcessing

      Annotator that uses the llama.cpp library to generate text completions with large language models.

      For settable parameters, and their explanations, see the parameters of this class and refer to @@ -915,6 +916,12 @@ +-----------------------------------------------------------------------------------------------------------------------------------+ +

      +
      +close()[source]#
      +

      Closes the llama.cpp model backend freeing resources. The model is reloaded when used again.

      +
      +
      inputAnnotatorTypes = ['document'][source]#
      @@ -1008,6 +1015,7 @@
      @@ -1223,6 +1229,7 @@

      ClassesAutoGGUFEmbeddings.getMetadata()
    • AutoGGUFEmbeddings.loadSavedModel()
    • AutoGGUFEmbeddings.pretrained()
      • +
    • AutoGGUFEmbeddings.close()

    diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_embeddings/index.html index c991c33026e985..4d6e993e31e57e 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.bert_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.bert_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_sentence_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_sentence_embeddings/index.html index 503806ef6f73ce..1e8090c8d1bac9 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_sentence_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bert_sentence_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.bert_sentence_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.bert_sentence_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bge_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bge_embeddings/index.html index 34123c110635ef..f33e6fa5ad4d55 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bge_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/bge_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.bge_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.bge_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/camembert_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/camembert_embeddings/index.html index 565c7b75178fc5..ba334e3ef4f95e 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/camembert_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/camembert_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.camembert_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.camembert_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/chunk_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/chunk_embeddings/index.html index 8dce54b2c17cd3..2a6d0fe3b84e97 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/chunk_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/chunk_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.chunk_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.chunk_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/deberta_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/deberta_embeddings/index.html index 57554b34fb161c..6895e07a92c9e9 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/deberta_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/deberta_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.deberta_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.deberta_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/distil_bert_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/distil_bert_embeddings/index.html index e900084ca0d9c2..8851b3758b84ce 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/distil_bert_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/distil_bert_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.distil_bert_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.distil_bert_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/doc2vec/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/doc2vec/index.html index d7198a1372ff3d..cc0eb2b6df71cb 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/doc2vec/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/doc2vec/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.doc2vec — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.doc2vec — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5_embeddings/index.html index 8f1bf521adbd30..f448eae97edb66 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.e5_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.e5_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5v_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5v_embeddings/index.html index a7c68520757d50..e9d382f58ae495 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5v_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/e5v_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.e5v_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.e5v_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/elmo_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/elmo_embeddings/index.html index b0e0e76d15ab68..d1b6f5e45fe385 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/elmo_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/elmo_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.elmo_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.elmo_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/index.html index b91ad11284d8b1..cf2d7190014a2d 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/instructor_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/instructor_embeddings/index.html index 5c1a1e12b75ce6..06192946cd730b 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/instructor_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/instructor_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.instructor_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.instructor_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/longformer_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/longformer_embeddings/index.html index c2c6f5f0218f62..2fa0fc9d4dcd47 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/longformer_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/longformer_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.longformer_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.longformer_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/minilm_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/minilm_embeddings/index.html index 2e9e7523da85d1..63b64d9758f462 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/minilm_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/minilm_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.minilm_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.minilm_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mpnet_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mpnet_embeddings/index.html index 43005c587d73a2..135cb11431faa6 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mpnet_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mpnet_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.mpnet_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.mpnet_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mxbai_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mxbai_embeddings/index.html index 902279ca07ffd9..16c62234d40617 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mxbai_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/mxbai_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.mxbai_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.mxbai_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/nomic_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/nomic_embeddings/index.html index 2ca3b6878dcee4..7884320e059911 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/nomic_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/nomic_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.nomic_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.nomic_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_embeddings/index.html index 7560bdb0ca678a..f8cd8a57dd9ea5 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.roberta_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.roberta_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_sentence_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_sentence_embeddings/index.html index 183da0fde80c3f..2b2cd90eea70df 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_sentence_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/roberta_sentence_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.roberta_sentence_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.roberta_sentence_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/sentence_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/sentence_embeddings/index.html index 6ec085c68b5645..c070da50e3caaf 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/sentence_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/sentence_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.sentence_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.sentence_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/snowflake_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/snowflake_embeddings/index.html index 47e4fdef7efaf8..f0929409f8f29e 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/snowflake_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/snowflake_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.snowflake_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.snowflake_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/uae_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/uae_embeddings/index.html index a2a146eb70cc58..39e1d1d6d8b62b 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/uae_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/uae_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.uae_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.uae_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/universal_sentence_encoder/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/universal_sentence_encoder/index.html index b92e2777358fce..e52029a21a7c7a 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/universal_sentence_encoder/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/universal_sentence_encoder/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.universal_sentence_encoder — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.universal_sentence_encoder — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word2vec/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word2vec/index.html index cad33c86c458e3..1b421fb5a2899c 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word2vec/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word2vec/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.word2vec — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.word2vec — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word_embeddings/index.html index cb6ccedc1363ca..787e27ab27b257 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/word_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.word_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.word_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_embeddings/index.html index 5840537ec4cf15..9a472380d6129b 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.xlm_roberta_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.xlm_roberta_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings/index.html index 75a14088805b88..5607d84b9e40e5 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlm_roberta_sentence_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.xlm_roberta_sentence_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.xlm_roberta_sentence_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlnet_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlnet_embeddings/index.html index 26f983685637cd..a4e2bc3a3d4360 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlnet_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/embeddings/xlnet_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.embeddings.xlnet_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.embeddings.xlnet_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/er/entity_ruler/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/er/entity_ruler/index.html index 4a05ad24c2a2d2..d531410571505d 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/er/entity_ruler/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/er/entity_ruler/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.er.entity_ruler — Spark NLP 6.1.5 documentation + sparknlp.annotator.er.entity_ruler — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -876,6 +876,16 @@

    ClassesoutputAnnotatorType = 'chunk'[source]#

    +
    +
    +autoMode[source]#
    +
    + +
    +
    +extractEntities[source]#
    +
    +
    static pretrained(name, lang='en', remote_loc=None)[source]#
    @@ -886,6 +896,34 @@

    Classesstatic loadStorage(path, spark, storage_ref)[source]#

    +
    +
    +setAutoMode(value)[source]#
    +

    Sets the auto mode for predefined regex entity groups.

    +
    +
    Parameters:
    +
    +
    valuestr

    Name of the auto mode to activate (e.g., ‘communication_entities’, ‘network_entities’, etc.)

    +
    +
    +
    +
    +
    + +
    +
    +setExtractEntities(value)[source]#
    +

    Sets specific entities to extract, filtering only those defined in regexPatterns or autoMode.

    +
    +
    Parameters:
    +
    +
    valuelist[str]

    List of entity names to extract, e.g., [‘EMAIL_ADDRESS_PATTERN’, ‘IPV4_PATTERN’].

    +
    +
    +
    +
    +
    + @@ -941,8 +979,12 @@

    ClassesEntityRulerModel.inputAnnotatorTypes
  • EntityRulerModel.optionalInputAnnotatorTypes
  • EntityRulerModel.outputAnnotatorType
    • +
  • EntityRulerModel.autoMode
    • +
  • EntityRulerModel.extractEntities
  • EntityRulerModel.pretrained()
  • EntityRulerModel.loadStorage()
    • +
  • EntityRulerModel.setAutoMode()
    • +
  • EntityRulerModel.setExtractEntities()
    • diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/er/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/er/index.html index e2ed90d5abd3c9..d2ad8b99aaad3d 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/er/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/er/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.er — Spark NLP 6.1.5 documentation + sparknlp.annotator.er — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/graph_extraction/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/graph_extraction/index.html index e98c29673a95df..ad78d17d28cb1d 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/graph_extraction/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/graph_extraction/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.graph_extraction — Spark NLP 6.1.5 documentation + sparknlp.annotator.graph_extraction — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/index.html index bfaef75a468e39..05abeac4ce65b9 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator — Spark NLP 6.1.5 documentation + sparknlp.annotator — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/index.html index 3d928c17d8730e..a3b3e62ad46581 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.keyword_extraction — Spark NLP 6.1.5 documentation + sparknlp.annotator.keyword_extraction — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/yake_keyword_extraction/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/yake_keyword_extraction/index.html index 19d46691425ebe..085836e6de735f 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/yake_keyword_extraction/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/keyword_extraction/yake_keyword_extraction/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.keyword_extraction.yake_keyword_extraction — Spark NLP 6.1.5 documentation + sparknlp.annotator.keyword_extraction.yake_keyword_extraction — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/index.html index fb465a7ba1f9c9..7535fc0f1f1ce4 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ld_dl — Spark NLP 6.1.5 documentation + sparknlp.annotator.ld_dl — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/language_detector_dl/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/language_detector_dl/index.html index c40e6f300e27a1..1c4805fbe13c0f 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/language_detector_dl/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ld_dl/language_detector_dl/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ld_dl.language_detector_dl — Spark NLP 6.1.5 documentation + sparknlp.annotator.ld_dl.language_detector_dl — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/lemmatizer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/lemmatizer/index.html index 148f305ac84ee9..d63cdd6ed54bae 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/lemmatizer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/lemmatizer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.lemmatizer — Spark NLP 6.1.5 documentation + sparknlp.annotator.lemmatizer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/big_text_matcher/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/big_text_matcher/index.html index 5429e6bf7252c6..28d5484499994e 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/big_text_matcher/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/big_text_matcher/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.big_text_matcher — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.big_text_matcher — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/date_matcher/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/date_matcher/index.html index 11b0a2f4222dce..8a2041f2cc7b7a 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/date_matcher/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/date_matcher/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.date_matcher — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.date_matcher — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/index.html index b76a6c6cd5d322..68446791bcae3d 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/multi_date_matcher/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/multi_date_matcher/index.html index 3652165dcb3da9..1742498a5dfc0c 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/multi_date_matcher/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/multi_date_matcher/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.multi_date_matcher — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.multi_date_matcher — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/regex_matcher/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/regex_matcher/index.html index 17231645d9e858..e9aa986695259f 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/regex_matcher/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/regex_matcher/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.regex_matcher — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.regex_matcher — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/text_matcher/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/text_matcher/index.html index 976b56df3c7011..c3ef932cf0f507 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/text_matcher/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/matcher/text_matcher/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.matcher.text_matcher — Spark NLP 6.1.5 documentation + sparknlp.annotator.matcher.text_matcher — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/n_gram_generator/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/n_gram_generator/index.html index 57c906a7fb23ec..687a41f58385e1 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/n_gram_generator/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/n_gram_generator/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.n_gram_generator — Spark NLP 6.1.5 documentation + sparknlp.annotator.n_gram_generator — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/index.html index 9c067809414893..e9064b94e80738 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_approach/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_approach/index.html index 5769de7d8b2cc8..7b4d5143657383 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_approach/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_approach/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_approach — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_approach — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_converter/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_converter/index.html index e21c0b82750172..d6652b8391f93d 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_converter/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_converter/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_converter — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_converter — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_crf/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_crf/index.html index 95aa0dce80dc9a..4a14cce36b89ff 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_crf/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_crf/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_crf — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_crf — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl/index.html index cfe51f8be2ea5d..778d6ae980a8d6 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_dl — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_dl — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl_graph_checker/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl_graph_checker/index.html index 648c77a33bf8f7..b80a7d68533bf9 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl_graph_checker/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_dl_graph_checker/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_dl_graph_checker — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_dl_graph_checker — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_overwriter/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_overwriter/index.html index db07fbbeedce0d..aa285523531411 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_overwriter/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/ner_overwriter/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.ner_overwriter — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.ner_overwriter — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/zero_shot_ner_model/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/zero_shot_ner_model/index.html index 827a3080a25a5f..d1a140928ae7ac 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/ner/zero_shot_ner_model/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/ner/zero_shot_ner_model/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.ner.zero_shot_ner_model — Spark NLP 6.1.5 documentation + sparknlp.annotator.ner.zero_shot_ner_model — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/normalizer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/normalizer/index.html index 383091eac47b9b..6b17ccdb9be476 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/normalizer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/normalizer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.normalizer — Spark NLP 6.1.5 documentation + sparknlp.annotator.normalizer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/openai/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/openai/index.html index 2d798b33089de0..e2d2809f8ea243 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/openai/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/openai/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.openai — Spark NLP 6.1.5 documentation + sparknlp.annotator.openai — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_completion/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_completion/index.html index cc8013e85c689e..117fe674f3c0e7 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_completion/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_completion/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.openai.openai_completion — Spark NLP 6.1.5 documentation + sparknlp.annotator.openai.openai_completion — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_embeddings/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_embeddings/index.html index 42de2beeda7435..dd05689a2252e7 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_embeddings/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/openai/openai_embeddings/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.openai.openai_embeddings — Spark NLP 6.1.5 documentation + sparknlp.annotator.openai.openai_embeddings — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/param/classifier_encoder/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/param/classifier_encoder/index.html index f005865100b52f..e5b388df08f12a 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/param/classifier_encoder/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/param/classifier_encoder/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.param.classifier_encoder — Spark NLP 6.1.5 documentation + sparknlp.annotator.param.classifier_encoder — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/param/evaluation_dl_params/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/param/evaluation_dl_params/index.html index 3ab1d55470923f..d86da49e4d5af8 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/param/evaluation_dl_params/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/param/evaluation_dl_params/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.param.evaluation_dl_params — Spark NLP 6.1.5 documentation + sparknlp.annotator.param.evaluation_dl_params — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/param/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/param/index.html index 669cd3733102cc..6ecc62895305c5 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/param/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/param/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.param — Spark NLP 6.1.5 documentation + sparknlp.annotator.param — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/pos/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/pos/index.html index 2affd7513761ca..9f7c7c7cac9de4 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/pos/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/pos/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.pos — Spark NLP 6.1.5 documentation + sparknlp.annotator.pos — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/pos/perceptron/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/pos/perceptron/index.html index 57968a593171f6..bd3da5dc2d0862 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/pos/perceptron/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/pos/perceptron/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.pos.perceptron — Spark NLP 6.1.5 documentation + sparknlp.annotator.pos.perceptron — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/index.html index 118a49e9625d1c..b648e54b62bf7e 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentence — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentence — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector/index.html index 2723fff02d1b9c..db26e431b241b0 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentence.sentence_detector — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentence.sentence_detector — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector_dl/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector_dl/index.html index b3cd9b6b6af8bf..47a79385e5f3ec 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector_dl/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/sentence/sentence_detector_dl/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentence.sentence_detector_dl — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentence.sentence_detector_dl — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/index.html index db6072423cf77f..f25328218e0024 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentiment — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentiment — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/sentiment_detector/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/sentiment_detector/index.html index e9a4ba7ad6e4dc..5327ab2db38719 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/sentiment_detector/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/sentiment_detector/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentiment.sentiment_detector — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentiment.sentiment_detector — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/vivekn_sentiment/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/vivekn_sentiment/index.html index 98d6c578211443..d0498c70e47ac7 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/vivekn_sentiment/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/sentiment/vivekn_sentiment/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.sentiment.vivekn_sentiment — Spark NLP 6.1.5 documentation + sparknlp.annotator.sentiment.vivekn_sentiment — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_model/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_model/index.html index 3e50fed586f2cc..fc111b29d2aeb8 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_model/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_model/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.auto_gguf_model — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.auto_gguf_model — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -902,6 +902,12 @@

    Classes +
    +close()[source]#
    +

    Closes the llama.cpp model backend freeing resources. The model is reloaded when used again.

    +
    + @@ -942,6 +948,7 @@

    ClassesAutoGGUFModel.outputAnnotatorType
  • AutoGGUFModel.loadSavedModel()
  • AutoGGUFModel.pretrained()
    • +
  • AutoGGUFModel.close()
    • diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_reranker/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_reranker/index.html index 21999640e8a6a1..8a76c16a7303e4 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_reranker/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_reranker/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.auto_gguf_reranker — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.auto_gguf_reranker — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -939,6 +939,12 @@

    Classes +
    +close()[source]#
    +

    Closes the llama.cpp model backend freeing resources. The model is reloaded when used again.

    +
    + @@ -982,6 +988,7 @@

    ClassesAutoGGUFReranker.getQuery()
  • AutoGGUFReranker.loadSavedModel()
  • AutoGGUFReranker.pretrained()
    • +
  • AutoGGUFReranker.close()
    • diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_vision_model/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_vision_model/index.html index e67a61388c34ca..2a58c17d2d45f0 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_vision_model/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/auto_gguf_vision_model/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.auto_gguf_vision_model — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.auto_gguf_vision_model — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + @@ -902,6 +902,12 @@

    Classes +
    +close()[source]#
    +

    Closes the llama.cpp model backend freeing resources. The model is reloaded when used again.

    +
    + @@ -942,6 +948,7 @@

    ClassesAutoGGUFVisionModel.outputAnnotatorType
  • AutoGGUFVisionModel.loadSavedModel()
  • AutoGGUFVisionModel.pretrained()
    • +
  • AutoGGUFVisionModel.close()
    • diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/bart_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/bart_transformer/index.html index 0aa4b9539484dd..571862a2e7a552 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/bart_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/bart_transformer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.bart_transformer — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.bart_transformer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/cohere_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/cohere_transformer/index.html index 8bce2e45ff08e8..0fe4f58db5d9bb 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/cohere_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/cohere_transformer/index.html @@ -8,7 +8,7 @@ - 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Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/gpt2_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/gpt2_transformer/index.html index 5fd8029e3feec5..1b5903aabfcd3c 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/gpt2_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/gpt2_transformer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.gpt2_transformer — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.gpt2_transformer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/index.html index bcf1ef5aeb5da3..89cefd3bc48d92 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/index.html @@ -8,7 +8,7 @@ - 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Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/marian_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/marian_transformer/index.html index 140232babf2137..944ab256c66ccd 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/marian_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/marian_transformer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.marian_transformer — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.marian_transformer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/mistral_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/mistral_transformer/index.html index f9107b1ef4ceaa..093b72a0bb4a30 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/mistral_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/mistral_transformer/index.html @@ -8,7 +8,7 @@ - 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Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/olmo_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/olmo_transformer/index.html index 41539e9bd6f285..cf3ba239465227 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/olmo_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/olmo_transformer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.olmo_transformer — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.olmo_transformer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi2_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi2_transformer/index.html index ffa84dc5970ed0..4a269d6158c60e 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi2_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi2_transformer/index.html @@ -8,7 +8,7 @@ - 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Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi4_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi4_transformer/index.html index 027a383e6d471c..57e549e97c778f 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi4_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/phi4_transformer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.phi4_transformer — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.phi4_transformer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/qwen_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/qwen_transformer/index.html index 9a72ccbd95e323..ab5cdd76df69d6 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/qwen_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/qwen_transformer/index.html @@ -8,7 +8,7 @@ - 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+ Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/t5_transformer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/t5_transformer/index.html index 6278ae03149371..f68b81c92b041f 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/t5_transformer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/seq2seq/t5_transformer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.seq2seq.t5_transformer — Spark NLP 6.1.5 documentation + sparknlp.annotator.seq2seq.t5_transformer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/similarity/document_similarity_ranker/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/similarity/document_similarity_ranker/index.html index 3ecd60b0ef8718..dccc2c6dc79aa6 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/similarity/document_similarity_ranker/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/similarity/document_similarity_ranker/index.html @@ -8,7 +8,7 @@ - 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+ Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/stemmer/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/stemmer/index.html index 52dd261b1fb867..34222d6c896128 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/stemmer/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/stemmer/index.html @@ -8,7 +8,7 @@ - sparknlp.annotator.stemmer — Spark NLP 6.1.5 documentation + sparknlp.annotator.stemmer — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -122,8 +122,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/reference/autosummary/sparknlp/annotator/stop_words_cleaner/index.html b/docs/api/python/reference/autosummary/sparknlp/annotator/stop_words_cleaner/index.html index a7ab4c6ee6d8c7..4accbaa438a30a 100644 --- a/docs/api/python/reference/autosummary/sparknlp/annotator/stop_words_cleaner/index.html +++ b/docs/api/python/reference/autosummary/sparknlp/annotator/stop_words_cleaner/index.html @@ -8,7 +8,7 @@ - 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"sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification module", "sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification module", "sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification module", "sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification module", "sparknlp.annotator.classifier_dl.xlnet_for_token_classification module", "sparknlp.annotator.cleaners package", "sparknlp.annotator.cleaners.cleaner module", "sparknlp.annotator.cleaners.extractor module", "sparknlp.annotator.coref package", "sparknlp.annotator.coref.spanbert_coref module", "sparknlp.annotator.cv package", "sparknlp.annotator.cv.blip_for_question_answering module", "sparknlp.annotator.cv.clip_for_zero_shot_classification module", "sparknlp.annotator.cv.convnext_for_image_classification module", "sparknlp.annotator.cv.florence2_transformer module", "sparknlp.annotator.cv.gemma3_for_multimodal module", "sparknlp.annotator.cv.internvl_for_multimodal module", "sparknlp.annotator.cv.janus_for_multimodal module", "sparknlp.annotator.cv.llava_for_multimodal module", "sparknlp.annotator.cv.mllama_for_multimodal module", "sparknlp.annotator.cv.paligemma_for_multimodal module", "sparknlp.annotator.cv.phi3_vision_for_multimodal module", "sparknlp.annotator.cv.qwen2vl_transformer module", "sparknlp.annotator.cv.smolvlm_transformer module", "sparknlp.annotator.cv.swin_for_image_classification module", "sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning module", "sparknlp.annotator.cv.vit_for_image_classification module", "sparknlp.annotator.dataframe_optimizer module", "sparknlp.annotator.date2_chunk module", "sparknlp.annotator.dependency package", "sparknlp.annotator.dependency.dependency_parser module", "sparknlp.annotator.dependency.typed_dependency_parser module", "sparknlp.annotator.document_character_text_splitter module", "sparknlp.annotator.document_normalizer module", "sparknlp.annotator.document_token_splitter module", "sparknlp.annotator.document_token_splitter_test module", "sparknlp.annotator.embeddings package", "sparknlp.annotator.embeddings.albert_embeddings module", "sparknlp.annotator.embeddings.auto_gguf_embeddings module", "sparknlp.annotator.embeddings.bert_embeddings module", "sparknlp.annotator.embeddings.bert_sentence_embeddings module", "sparknlp.annotator.embeddings.bge_embeddings module", "sparknlp.annotator.embeddings.camembert_embeddings module", "sparknlp.annotator.embeddings.chunk_embeddings module", "sparknlp.annotator.embeddings.deberta_embeddings module", "sparknlp.annotator.embeddings.distil_bert_embeddings module", "sparknlp.annotator.embeddings.doc2vec module", "sparknlp.annotator.embeddings.e5_embeddings module", "sparknlp.annotator.embeddings.e5v_embeddings module", "sparknlp.annotator.embeddings.elmo_embeddings module", "sparknlp.annotator.embeddings.instructor_embeddings module", "sparknlp.annotator.embeddings.longformer_embeddings module", "sparknlp.annotator.embeddings.minilm_embeddings module", "sparknlp.annotator.embeddings.mpnet_embeddings module", "sparknlp.annotator.embeddings.mxbai_embeddings module", "sparknlp.annotator.embeddings.nomic_embeddings module", "sparknlp.annotator.embeddings.roberta_embeddings module", "sparknlp.annotator.embeddings.roberta_sentence_embeddings module", "sparknlp.annotator.embeddings.sentence_embeddings module", "sparknlp.annotator.embeddings.snowflake_embeddings module", "sparknlp.annotator.embeddings.uae_embeddings module", "sparknlp.annotator.embeddings.universal_sentence_encoder module", "sparknlp.annotator.embeddings.word2vec module", "sparknlp.annotator.embeddings.word_embeddings module", "sparknlp.annotator.embeddings.xlm_roberta_embeddings module", "sparknlp.annotator.embeddings.xlm_roberta_sentence_embeddings module", "sparknlp.annotator.embeddings.xlnet_embeddings module", "sparknlp.annotator.er package", "sparknlp.annotator.er.entity_ruler module", "sparknlp.annotator.graph_extraction module", "sparknlp.annotator.keyword_extraction package", "sparknlp.annotator.keyword_extraction.yake_keyword_extraction module", "sparknlp.annotator.ld_dl package", "sparknlp.annotator.ld_dl.language_detector_dl module", "sparknlp.annotator.lemmatizer module", "sparknlp.annotator.matcher package", "sparknlp.annotator.matcher.big_text_matcher module", "sparknlp.annotator.matcher.date_matcher module", "sparknlp.annotator.matcher.multi_date_matcher module", "sparknlp.annotator.matcher.regex_matcher module", "sparknlp.annotator.matcher.text_matcher module", "sparknlp.annotator.n_gram_generator module", "sparknlp.annotator.ner package", "sparknlp.annotator.ner.ner_approach module", "sparknlp.annotator.ner.ner_converter module", "sparknlp.annotator.ner.ner_crf module", "sparknlp.annotator.ner.ner_dl module", "sparknlp.annotator.ner.ner_dl_graph_checker module", "sparknlp.annotator.ner.ner_overwriter module", "sparknlp.annotator.ner.zero_shot_ner_model module", "sparknlp.annotator.normalizer module", "sparknlp.annotator.openai package", "sparknlp.annotator.openai.openai_completion module", "sparknlp.annotator.openai.openai_embeddings module", "sparknlp.annotator.param package", "sparknlp.annotator.param.classifier_encoder module", "sparknlp.annotator.param.evaluation_dl_params module", "sparknlp.annotator.pos package", "sparknlp.annotator.pos.perceptron module", "sparknlp.annotator.sentence package", "sparknlp.annotator.sentence.sentence_detector module", "sparknlp.annotator.sentence.sentence_detector_dl module", "sparknlp.annotator.sentiment package", "sparknlp.annotator.sentiment.sentiment_detector module", "sparknlp.annotator.sentiment.vivekn_sentiment module", "sparknlp.annotator.seq2seq package", "sparknlp.annotator.seq2seq.auto_gguf_model module", "sparknlp.annotator.seq2seq.auto_gguf_reranker module", "sparknlp.annotator.seq2seq.auto_gguf_vision_model module", "sparknlp.annotator.seq2seq.bart_transformer module", "sparknlp.annotator.seq2seq.cohere_transformer module", "sparknlp.annotator.seq2seq.cpm_transformer module", "sparknlp.annotator.seq2seq.gpt2_transformer module", "sparknlp.annotator.seq2seq.llama2_transformer module", "sparknlp.annotator.seq2seq.llama3_transformer module", "sparknlp.annotator.seq2seq.m2m100_transformer module", "sparknlp.annotator.seq2seq.marian_transformer module", "sparknlp.annotator.seq2seq.mistral_transformer module", "sparknlp.annotator.seq2seq.nllb_transformer module", "sparknlp.annotator.seq2seq.olmo_transformer module", "sparknlp.annotator.seq2seq.phi2_transformer module", "sparknlp.annotator.seq2seq.phi3_transformer module", "sparknlp.annotator.seq2seq.phi4_transformer module", "sparknlp.annotator.seq2seq.qwen_transformer module", "sparknlp.annotator.seq2seq.starcoder_transformer module", "sparknlp.annotator.seq2seq.t5_transformer module", "sparknlp.annotator.similarity package", "sparknlp.annotator.similarity.document_similarity_ranker module", "sparknlp.annotator.spell_check package", "sparknlp.annotator.spell_check.context_spell_checker module", "sparknlp.annotator.spell_check.norvig_sweeting module", "sparknlp.annotator.spell_check.symmetric_delete module", "sparknlp.annotator.stemmer module", "sparknlp.annotator.stop_words_cleaner module", "sparknlp.annotator.tf_ner_dl_graph_builder module", "sparknlp.annotator.token package", "sparknlp.annotator.token.chunk_tokenizer module", "sparknlp.annotator.token.recursive_tokenizer module", "sparknlp.annotator.token.regex_tokenizer module", "sparknlp.annotator.token.tokenizer module", "sparknlp.annotator.token2_chunk module", "sparknlp.annotator.ws package", "sparknlp.annotator.ws.word_segmenter module", "sparknlp.base package", "sparknlp.base.audio_assembler module", "sparknlp.base.doc2_chunk module", "sparknlp.base.document_assembler module", "sparknlp.base.embeddings_finisher module", "sparknlp.base.finisher module", "sparknlp.base.gguf_ranking_finisher module", "sparknlp.base.graph_finisher module", "sparknlp.base.has_recursive_fit module", "sparknlp.base.has_recursive_transform module", "sparknlp.base.image_assembler module", "sparknlp.base.light_pipeline module", "sparknlp.base.multi_document_assembler module", "sparknlp.base.prompt_assembler module", "sparknlp.base.recursive_pipeline module", "sparknlp.base.table_assembler module", "sparknlp.base.token_assembler module", "sparknlp.common package", "sparknlp.common.annotator_approach module", "sparknlp.common.annotator_model module", "sparknlp.common.annotator_properties module", "sparknlp.common.annotator_type module", "sparknlp.common.coverage_result module", "sparknlp.common.match_strategy module", "sparknlp.common.properties module", "sparknlp.common.read_as module", "sparknlp.common.recursive_annotator_approach module", "sparknlp.common.storage module", "sparknlp.common.utils module", "sparknlp.functions module", "sparknlp.internal package", "sparknlp.internal.annotator_java_ml module", "sparknlp.internal.annotator_transformer module", "sparknlp.internal.extended_java_wrapper module", "sparknlp.internal.params_getters_setters module", "sparknlp.internal.recursive module", "sparknlp.logging package", "sparknlp.logging.comet module", "sparknlp.partition package", "sparknlp.partition.partition module", "sparknlp.partition.partition_properties module", "sparknlp.partition.partition_transformer module", "sparknlp.pretrained package", "sparknlp.pretrained.pretrained_pipeline module", "sparknlp.pretrained.resource_downloader module", "sparknlp.pretrained.utils module", "sparknlp.reader package", "sparknlp.reader.enums module", "sparknlp.reader.pdf_to_text module", "sparknlp.reader.reader2doc module", "sparknlp.reader.reader2image module", "sparknlp.reader.reader2table module", "sparknlp.reader.reader_assembler module", "sparknlp.reader.sparknlp_reader module", "sparknlp.training package", "sparknlp.training.conll module", "sparknlp.training.conllu module", "sparknlp.training.pos module", "sparknlp.training.pub_tator module", "sparknlp.training.spacy_to_annotation module", "sparknlp.training.tfgraphs module", "sparknlp.upload_to_hub module", "sparknlp.util module", "Getting Started", "Spark NLP Documentation", "sparknlp.annotation", "sparknlp.annotation_audio", "sparknlp.annotation_image", "sparknlp.annotator.audio.hubert_for_ctc", "sparknlp.annotator.audio", "sparknlp.annotator.audio.wav2vec2_for_ctc", "sparknlp.annotator.audio.whisper_for_ctc", "sparknlp.annotator.chunk2_doc", "sparknlp.annotator.chunker", "sparknlp.annotator.classifier_dl.albert_for_multiple_choice", "sparknlp.annotator.classifier_dl.albert_for_question_answering", "sparknlp.annotator.classifier_dl.albert_for_sequence_classification", "sparknlp.annotator.classifier_dl.albert_for_token_classification", "sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.bart_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.bert_for_multiple_choice", "sparknlp.annotator.classifier_dl.bert_for_question_answering", "sparknlp.annotator.classifier_dl.bert_for_sequence_classification", "sparknlp.annotator.classifier_dl.bert_for_token_classification", "sparknlp.annotator.classifier_dl.bert_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.camembert_for_question_answering", "sparknlp.annotator.classifier_dl.camembert_for_sequence_classification", "sparknlp.annotator.classifier_dl.camembert_for_token_classification", "sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.classifier_dl", "sparknlp.annotator.classifier_dl.deberta_for_question_answering", "sparknlp.annotator.classifier_dl.deberta_for_sequence_classification", "sparknlp.annotator.classifier_dl.deberta_for_token_classification", "sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.distil_bert_for_question_answering", "sparknlp.annotator.classifier_dl.distil_bert_for_sequence_classification", "sparknlp.annotator.classifier_dl.distil_bert_for_token_classification", "sparknlp.annotator.classifier_dl.distil_bert_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.distilbert_for_multiple_choice", "sparknlp.annotator.classifier_dl", "sparknlp.annotator.classifier_dl.longformer_for_question_answering", "sparknlp.annotator.classifier_dl.longformer_for_sequence_classification", "sparknlp.annotator.classifier_dl.longformer_for_token_classification", "sparknlp.annotator.classifier_dl.mpnet_for_question_answering", "sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification", "sparknlp.annotator.classifier_dl.mpnet_for_token_classification", "sparknlp.annotator.classifier_dl.multi_classifier_dl", "sparknlp.annotator.classifier_dl.roberta_for_multiple_choice", "sparknlp.annotator.classifier_dl.roberta_for_question_answering", "sparknlp.annotator.classifier_dl.roberta_for_sequence_classification", "sparknlp.annotator.classifier_dl.roberta_for_token_classification", "sparknlp.annotator.classifier_dl.roberta_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.sentiment_dl", "sparknlp.annotator.classifier_dl.tapas_for_question_answering", "sparknlp.annotator.classifier_dl.xlm_roberta_for_multiple_choice", "sparknlp.annotator.classifier_dl.xlm_roberta_for_question_answering", "sparknlp.annotator.classifier_dl.xlm_roberta_for_sequence_classification", "sparknlp.annotator.classifier_dl.xlm_roberta_for_token_classification", "sparknlp.annotator.classifier_dl.xlm_roberta_for_zero_shot_classification", "sparknlp.annotator.classifier_dl.xlnet_for_sequence_classification", "sparknlp.annotator.classifier_dl.xlnet_for_token_classification", "sparknlp.annotator.cleaners.cleaner", "sparknlp.annotator.cleaners.extractor", "sparknlp.annotator.cleaners", "sparknlp.annotator.coref", "sparknlp.annotator.coref.spanbert_coref", "sparknlp.annotator.cv.blip_for_question_answering", "sparknlp.annotator.cv.clip_for_zero_shot_classification", "sparknlp.annotator.cv.convnext_for_image_classification", "sparknlp.annotator.cv.florence2_transformer", "sparknlp.annotator.cv.gemma3_for_multimodal", "sparknlp.annotator.cv", "sparknlp.annotator.cv.internvl_for_multimodal", "sparknlp.annotator.cv.janus_for_multimodal", "sparknlp.annotator.cv.llava_for_multimodal", "sparknlp.annotator.cv.mllama_for_multimodal", "sparknlp.annotator.cv.paligemma_for_multimodal", "sparknlp.annotator.cv.phi3_vision_for_multimodal", "sparknlp.annotator.cv.qwen2vl_transformer", "sparknlp.annotator.cv.smolvlm_transformer", "sparknlp.annotator.cv.swin_for_image_classification", "sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning", "sparknlp.annotator.cv.vit_for_image_classification", "sparknlp.annotator.dataframe_optimizer", "sparknlp.annotator.date2_chunk", "sparknlp.annotator.dependency.dependency_parser", "sparknlp.annotator.dependency", "sparknlp.annotator.dependency.typed_dependency_parser", "sparknlp.annotator.document_character_text_splitter", "sparknlp.annotator.document_normalizer", "sparknlp.annotator.document_token_splitter", "sparknlp.annotator.document_token_splitter_test", "sparknlp.annotator.embeddings.albert_embeddings", "sparknlp.annotator.embeddings.auto_gguf_embeddings", "sparknlp.annotator.embeddings.bert_embeddings", "sparknlp.annotator.embeddings.bert_sentence_embeddings", "sparknlp.annotator.embeddings.bge_embeddings", "sparknlp.annotator.embeddings.camembert_embeddings", "sparknlp.annotator.embeddings.chunk_embeddings", "sparknlp.annotator.embeddings.deberta_embeddings", "sparknlp.annotator.embeddings.distil_bert_embeddings", "sparknlp.annotator.embeddings.doc2vec", "sparknlp.annotator.embeddings.e5_embeddings", "sparknlp.annotator.embeddings.e5v_embeddings", "sparknlp.annotator.embeddings.elmo_embeddings", "sparknlp.annotator.embeddings", "sparknlp.annotator.embeddings.instructor_embeddings", "sparknlp.annotator.embeddings.longformer_embeddings", "sparknlp.annotator.embeddings.minilm_embeddings", "sparknlp.annotator.embeddings.mpnet_embeddings", "sparknlp.annotator.embeddings.mxbai_embeddings", "sparknlp.annotator.embeddings.nomic_embeddings", "sparknlp.annotator.embeddings.roberta_embeddings", "sparknlp.annotator.embeddings.roberta_sentence_embeddings", "sparknlp.annotator.embeddings.sentence_embeddings", "sparknlp.annotator.embeddings.snowflake_embeddings", "sparknlp.annotator.embeddings.uae_embeddings", "sparknlp.annotator.embeddings.universal_sentence_encoder", "sparknlp.annotator.embeddings.word2vec", "sparknlp.annotator.embeddings.word_embeddings", "sparknlp.annotator.embeddings.xlm_roberta_embeddings", "sparknlp.annotator.embeddings.xlm_roberta_sentence_embeddings", "sparknlp.annotator.embeddings.xlnet_embeddings", "sparknlp.annotator.er.entity_ruler", "sparknlp.annotator.er", "sparknlp.annotator.graph_extraction", "sparknlp.annotator", "sparknlp.annotator.keyword_extraction", "sparknlp.annotator.keyword_extraction.yake_keyword_extraction", "sparknlp.annotator.ld_dl", "sparknlp.annotator.ld_dl.language_detector_dl", "sparknlp.annotator.lemmatizer", "sparknlp.annotator.matcher.big_text_matcher", "sparknlp.annotator.matcher.date_matcher", "sparknlp.annotator.matcher", "sparknlp.annotator.matcher.multi_date_matcher", "sparknlp.annotator.matcher.regex_matcher", "sparknlp.annotator.matcher.text_matcher", "sparknlp.annotator.n_gram_generator", "sparknlp.annotator.ner", "sparknlp.annotator.ner.ner_approach", "sparknlp.annotator.ner.ner_converter", "sparknlp.annotator.ner.ner_crf", "sparknlp.annotator.ner.ner_dl", "sparknlp.annotator.ner.ner_dl_graph_checker", "sparknlp.annotator.ner.ner_overwriter", "sparknlp.annotator.ner.zero_shot_ner_model", "sparknlp.annotator.normalizer", "sparknlp.annotator.openai", "sparknlp.annotator.openai.openai_completion", "sparknlp.annotator.openai.openai_embeddings", "sparknlp.annotator.param.classifier_encoder", "sparknlp.annotator.param.evaluation_dl_params", "sparknlp.annotator.param", "sparknlp.annotator.pos", "sparknlp.annotator.pos.perceptron", "sparknlp.annotator.sentence", "sparknlp.annotator.sentence.sentence_detector", "sparknlp.annotator.sentence.sentence_detector_dl", "sparknlp.annotator.sentiment", "sparknlp.annotator.sentiment.sentiment_detector", "sparknlp.annotator.sentiment.vivekn_sentiment", "sparknlp.annotator.seq2seq.auto_gguf_model", "sparknlp.annotator.seq2seq.auto_gguf_reranker", "sparknlp.annotator.seq2seq.auto_gguf_vision_model", "sparknlp.annotator.seq2seq.bart_transformer", "sparknlp.annotator.seq2seq.cohere_transformer", "sparknlp.annotator.seq2seq.cpm_transformer", "sparknlp.annotator.seq2seq.gpt2_transformer", "sparknlp.annotator.seq2seq", "sparknlp.annotator.seq2seq.llama2_transformer", "sparknlp.annotator.seq2seq.llama3_transformer", "sparknlp.annotator.seq2seq.m2m100_transformer", "sparknlp.annotator.seq2seq.marian_transformer", "sparknlp.annotator.seq2seq.mistral_transformer", "sparknlp.annotator.seq2seq.nllb_transformer", "sparknlp.annotator.seq2seq.olmo_transformer", "sparknlp.annotator.seq2seq.phi2_transformer", "sparknlp.annotator.seq2seq.phi3_transformer", "sparknlp.annotator.seq2seq.phi4_transformer", "sparknlp.annotator.seq2seq.qwen_transformer", "sparknlp.annotator.seq2seq.starcoder_transformer", "sparknlp.annotator.seq2seq.t5_transformer", "sparknlp.annotator.similarity.document_similarity_ranker", "sparknlp.annotator.similarity", "sparknlp.annotator.spell_check.context_spell_checker", "sparknlp.annotator.spell_check", "sparknlp.annotator.spell_check.norvig_sweeting", "sparknlp.annotator.spell_check.symmetric_delete", "sparknlp.annotator.stemmer", "sparknlp.annotator.stop_words_cleaner", "sparknlp.annotator.tf_ner_dl_graph_builder", 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59, 61, 108, 113, 114, 125, 130, 192, 224, 252, 262, 319, 367, 372, 373, 385, 389, 452, 484, 487, 512], "wordembed": [1, 5, 90, 97, 112, 117, 140, 201, 355, 371, 376, 400, 460, 487], "By": [1, 114, 157, 163, 179, 187, 192, 201, 234, 348, 373, 417, 422, 439, 447, 452, 460, 487, 494, 522], "thi": [1, 2, 3, 4, 7, 8, 9, 11, 15, 16, 17, 18, 21, 22, 23, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 47, 48, 49, 50, 54, 55, 56, 57, 58, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110, 112, 114, 115, 116, 117, 118, 119, 120, 122, 123, 125, 128, 130, 131, 133, 134, 135, 138, 139, 140, 141, 142, 144, 146, 152, 154, 155, 157, 158, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 183, 184, 185, 187, 190, 191, 192, 193, 194, 196, 198, 200, 201, 203, 204, 207, 208, 209, 210, 211, 212, 213, 217, 220, 221, 228, 232, 234, 236, 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19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 123, 125, 127, 128, 130, 131, 132, 133, 134, 135, 138, 139, 140, 141, 142, 143, 144, 146, 150, 152, 154, 155, 157, 158, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 183, 184, 185, 187, 188, 190, 191, 192, 193, 196, 198, 199, 200, 201, 202, 203, 204, 207, 208, 209, 210, 212, 213, 221, 225, 226, 232, 234, 237, 238, 241, 245, 246, 249, 250, 252, 254, 255, 256, 260, 265, 267, 268, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 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115, 161, 207, 221, 268, 273, 275, 276, 279, 281, 283, 285, 290, 292, 294, 298, 301, 306, 308, 313, 315, 316, 340, 374, 420, 466, 481, 487], "configur": [1, 67, 112, 193, 236, 249, 250, 260, 325, 348, 371, 453, 487, 497, 510, 511], "note": [1, 7, 8, 9, 17, 18, 23, 28, 36, 44, 49, 50, 56, 79, 81, 91, 92, 95, 99, 101, 103, 106, 107, 110, 112, 117, 118, 120, 125, 140, 146, 160, 161, 162, 163, 164, 165, 166, 167, 168, 170, 171, 173, 174, 175, 176, 177, 178, 179, 208, 221, 224, 236, 237, 250, 260, 265, 267, 268, 275, 276, 281, 286, 294, 303, 308, 309, 315, 318, 338, 340, 349, 350, 353, 357, 359, 361, 365, 366, 369, 371, 376, 377, 379, 385, 400, 406, 419, 420, 421, 422, 423, 424, 425, 427, 428, 430, 431, 433, 434, 435, 436, 437, 438, 439, 468, 481, 484, 487, 497, 498, 511, 531], "s3": [1, 241, 487, 502], "hdf": [1, 487], "dbf": [1, 487], "dict": [1, 2, 3, 4, 81, 84, 85, 122, 128, 130, 133, 134, 139, 142, 143, 144, 146, 150, 157, 183, 184, 185, 193, 208, 221, 225, 232, 234, 236, 237, 238, 240, 250, 258, 262, 263, 264, 340, 342, 344, 380, 388, 389, 393, 394, 399, 402, 403, 404, 406, 409, 417, 442, 444, 445, 453, 468, 481, 485, 487, 493, 494, 497, 498, 499, 501, 511, 519], "custom": [1, 86, 117, 139, 140, 141, 154, 155, 193, 210, 234, 247, 249, 250, 345, 376, 399, 400, 401, 414, 415, 453, 470, 487, 494, 508, 510, 511], "save": [1, 7, 8, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 91, 92, 93, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 115, 118, 119, 120, 140, 150, 155, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 224, 234, 265, 267, 268, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 322, 323, 324, 325, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 349, 350, 351, 352, 353, 354, 356, 357, 359, 360, 361, 363, 364, 365, 366, 367, 368, 369, 370, 372, 373, 374, 377, 378, 379, 400, 409, 415, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 484, 487, 494, 522, 526], "from": [1, 2, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 123, 125, 127, 128, 130, 131, 132, 133, 134, 135, 138, 139, 140, 141, 142, 143, 144, 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407, 408, 412, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 442, 444, 445, 446, 447, 449, 451, 452, 453, 454, 456, 457, 458, 459, 460, 461, 462, 463, 466, 468, 469, 470, 471, 472, 473, 475, 481, 486, 487, 490, 493, 494, 497, 498, 499, 501, 502, 506, 507, 508, 510, 511, 512, 513, 515, 516, 517, 522, 525, 526, 527, 530, 531, 532], "dure": [1, 7, 28, 44, 50, 66, 92, 99, 104, 139, 140, 141, 150, 154, 160, 161, 162, 181, 183, 221, 234, 237, 265, 286, 303, 309, 324, 350, 357, 363, 399, 400, 401, 409, 414, 419, 420, 421, 440, 442, 481, 487, 494, 498, 522], "annotator_log": [1, 487], "print": [1, 87, 241, 346, 348, 487, 502], "jvm": [1, 187, 447, 487], "output": [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 123, 125, 127, 128, 130, 131, 132, 133, 134, 135, 138, 139, 140, 141, 142, 143, 144, 146, 147, 152, 154, 155, 157, 158, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 183, 184, 185, 186, 187, 190, 191, 192, 193, 194, 196, 198, 199, 200, 201, 202, 203, 204, 207, 208, 209, 210, 212, 213, 217, 221, 226, 234, 236, 237, 238, 245, 246, 247, 249, 254, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 344, 345, 346, 347, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 382, 385, 387, 388, 389, 390, 392, 393, 394, 395, 398, 399, 400, 401, 402, 403, 404, 406, 407, 412, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 442, 444, 445, 446, 447, 449, 451, 452, 453, 454, 456, 457, 458, 459, 460, 461, 462, 463, 466, 468, 469, 470, 472, 473, 476, 481, 486, 487, 494, 497, 498, 499, 506, 507, 508, 510, 515, 522, 525, 526, 527], "real": [1, 140, 171, 173, 174, 175, 177, 234, 400, 431, 433, 434, 435, 437, 487, 494, 522], "time": [1, 28, 44, 50, 61, 91, 95, 100, 116, 118, 119, 125, 131, 150, 158, 162, 183, 196, 286, 303, 309, 319, 349, 353, 358, 375, 377, 378, 385, 390, 409, 418, 421, 442, 456, 487, 525, 526, 530, 531], "int": [1, 2, 4, 7, 8, 9, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 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167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 318, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 345, 347, 354, 357, 364, 366, 385, 400, 406, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "add": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 117, 131, 132, 154, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 183, 193, 203, 210, 221, 258, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 376, 390, 392, 414, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 442, 453, 462, 470, 481, 519, 526], "top_p": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 146, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 326, 327, 338, 406], "higher": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 117, 125, 146, 158, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 376, 385, 406, 418, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "kept": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "repetitionpenalti": [9, 64, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 159, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 214, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "repetit": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "penalti": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "norepeatngrams": [9, 64, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 159, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 214, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "ngram": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 97, 135, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 355, 395, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "occur": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "onc": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 210, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 470, 481], "beamsiz": [9, 64, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 159, 163, 164, 168, 169, 172, 176, 214, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 422, 423, 428, 429, 432, 436, 481], "beam": [9, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 163, 164, 168, 169, 172, 176, 221, 268, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 338, 422, 423, 428, 429, 432, 436, 481], "mr": [9, 268], "middl": [9, 191, 196, 268, 451, 456], "langaug": [9, 240, 268, 501], "xx": [9, 23, 56, 118, 119, 127, 165, 169, 170, 172, 268, 281, 315, 318, 377, 378, 387, 424, 429, 430, 432], "do": [9, 10, 88, 115, 125, 138, 164, 187, 193, 208, 210, 268, 269, 347, 374, 385, 398, 423, 447, 453, 468, 470, 522, 526, 530], "convert": [10, 60, 79, 82, 87, 97, 107, 112, 117, 131, 132, 135, 138, 144, 146, 163, 179, 192, 194, 196, 199, 202, 204, 234, 237, 245, 269, 318, 338, 341, 346, 355, 366, 371, 376, 390, 392, 395, 398, 404, 406, 422, 439, 452, 454, 456, 458, 461, 463, 494, 522, 529], "back": [10, 88, 141, 146, 163, 269, 347, 401, 406, 422], "try": [10, 183, 252, 269, 442, 512], "re": [10, 95, 101, 109, 210, 269, 353, 359, 368, 470, 526], "analysi": [10, 28, 44, 50, 103, 104, 120, 156, 157, 179, 219, 236, 258, 269, 286, 303, 309, 361, 363, 379, 416, 417, 439, 478, 497, 519, 527], "york": [10, 143, 202, 269, 403, 461], "jersei": [10, 202, 269, 461], "aren": [10, 202, 269, 461], "t": [10, 17, 18, 23, 27, 32, 36, 49, 56, 88, 92, 99, 110, 128, 144, 155, 157, 160, 161, 162, 174, 193, 202, 210, 221, 269, 275, 276, 281, 285, 290, 294, 308, 315, 347, 350, 357, 369, 388, 404, 415, 417, 419, 420, 421, 434, 453, 461, 470, 481], "far": [10, 146, 166, 202, 269, 406, 425, 461], "apart": [10, 84, 85, 202, 269, 342, 344, 461], "actual": [10, 135, 202, 213, 269, 395, 461, 473], "id": [10, 44, 61, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 87, 118, 122, 146, 147, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 183, 199, 200, 202, 209, 213, 221, 234, 252, 269, 303, 319, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 346, 377, 380, 406, 407, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 442, 458, 459, 461, 469, 473, 481, 494, 512], "defin": [10, 11, 138, 139, 140, 141, 183, 187, 191, 202, 210, 226, 234, 240, 245, 269, 270, 398, 399, 400, 401, 442, 447, 451, 461, 470, 486, 494, 501, 506, 522, 526, 530], "amongst": [10, 202, 269, 461], "thing": [10, 168, 183, 202, 269, 428, 442, 461], "explain_document_dl": [10, 202, 208, 226, 240, 269, 461, 468, 486, 501], "chunktodoc": [10, 269], "chunkconvert": [10, 269], "explainresult": [10, 202, 269, 461], "selectexpr": [10, 11, 28, 51, 63, 66, 67, 78, 79, 80, 84, 85, 86, 87, 88, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 125, 128, 130, 131, 132, 133, 134, 135, 138, 142, 143, 144, 152, 154, 155, 157, 162, 170, 186, 187, 190, 192, 193, 194, 199, 201, 202, 226, 252, 253, 254, 269, 270, 286, 310, 318, 322, 324, 325, 337, 338, 339, 342, 344, 345, 346, 347, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 361, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 385, 388, 389, 390, 392, 393, 394, 395, 398, 402, 403, 404, 412, 414, 415, 417, 421, 430, 446, 447, 449, 452, 453, 454, 458, 460, 461, 486, 512, 513, 515, 525, 526, 531], "explod": [10, 11, 28, 51, 63, 84, 85, 86, 88, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 125, 130, 132, 133, 134, 135, 138, 142, 143, 152, 154, 155, 170, 194, 201, 202, 226, 237, 252, 254, 269, 270, 286, 310, 318, 322, 342, 344, 345, 347, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 361, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 385, 389, 392, 393, 394, 395, 398, 402, 403, 412, 414, 415, 430, 454, 460, 461, 486, 498, 512, 515, 525, 526, 531], "col": [10, 84, 85, 122, 130, 138, 142, 181, 199, 201, 202, 204, 209, 226, 250, 269, 342, 344, 380, 389, 398, 402, 461, 486, 511, 525], "7": [10, 50, 78, 93, 94, 98, 131, 132, 147, 152, 166, 167, 171, 174, 175, 202, 254, 256, 260, 269, 309, 337, 351, 352, 356, 390, 392, 407, 412, 425, 427, 431, 434, 435, 461, 515, 517, 525], "loc": [10, 16, 22, 26, 31, 35, 40, 43, 48, 55, 58, 123, 138, 139, 140, 202, 208, 226, 240, 252, 269, 274, 280, 284, 289, 293, 299, 302, 307, 314, 317, 382, 398, 399, 400, 461, 468, 486, 501, 512], "22": [10, 202, 252, 269, 461, 512, 525], "part": [11, 96, 125, 128, 151, 152, 160, 161, 162, 167, 186, 196, 199, 221, 237, 238, 254, 258, 270, 348, 354, 385, 388, 411, 412, 419, 420, 421, 427, 446, 456, 458, 481, 515, 532], "tag": [11, 28, 44, 50, 87, 96, 137, 138, 139, 140, 142, 151, 152, 196, 226, 234, 236, 237, 238, 250, 252, 254, 255, 258, 270, 286, 303, 309, 346, 354, 397, 398, 399, 400, 402, 411, 412, 456, 486, 494, 497, 498, 511, 512, 515, 516, 519, 532], "order": [11, 86, 104, 120, 122, 125, 131, 160, 161, 162, 163, 167, 174, 184, 185, 203, 208, 213, 221, 226, 249, 270, 345, 348, 363, 379, 380, 385, 390, 419, 420, 421, 422, 427, 434, 444, 445, 462, 468, 473, 481, 486, 510, 526, 527, 530, 532], "meaning": [11, 186, 270, 446], "phrase": [11, 96, 100, 116, 130, 134, 270, 354, 358, 375, 389, 394], "onto": [11, 226, 270, 486, 527], "pars": [11, 83, 84, 85, 96, 122, 128, 131, 132, 183, 184, 185, 208, 210, 212, 236, 237, 240, 246, 247, 250, 252, 254, 270, 342, 343, 344, 354, 380, 388, 390, 392, 442, 444, 445, 468, 470, 472, 497, 501, 507, 508, 511, 512, 515], "regular": [11, 86, 133, 139, 154, 270, 345, 393, 399, 414], "express": [11, 50, 63, 86, 131, 133, 154, 270, 309, 322, 345, 390, 393, 414], "wrap": [11, 270, 319, 440, 474, 475, 483, 489, 493, 509], "angl": [11, 114, 270, 373], "bracket": [11, 270], "easili": [11, 103, 152, 201, 270, 361, 412, 460, 522], "distinguish": [11, 270], "itself": [11, 125, 172, 179, 196, 211, 270, 348, 385, 432, 439, 456, 471, 527], "form": [11, 28, 44, 50, 117, 122, 128, 130, 131, 133, 134, 155, 157, 184, 185, 196, 201, 234, 252, 253, 270, 286, 303, 309, 376, 380, 388, 389, 390, 393, 394, 415, 417, 444, 445, 456, 460, 494, 512, 513, 526], "peter": [11, 105, 128, 144, 152, 155, 184, 186, 252, 270, 364, 388, 404, 412, 415, 444, 446, 512], "piper": [11, 128, 152, 186, 270, 388, 412, 446], "employe": [11, 128, 152, 186, 270, 388, 412, 446], "pick": [11, 128, 152, 186, 270, 388, 412, 446], "peck": [11, 128, 152, 186, 270, 388, 412, 446], "pickl": [11, 128, 152, 186, 270, 388, 412, 446], "pepper": [11, 128, 152, 186, 270, 388, 412, 446], "nnp": [11, 152, 208, 226, 252, 253, 254, 255, 270, 412, 468, 486, 512, 513, 515, 516, 525, 530, 531, 532], "nn": [11, 86, 152, 252, 253, 254, 255, 270, 345, 412, 512, 513, 515, 516, 532], "vbp": [11, 152, 208, 253, 270, 412, 468, 513, 525, 530, 531], "vbg": [11, 152, 270, 412], "IN": [11, 152, 208, 226, 253, 254, 255, 270, 412, 468, 486, 513, 515, 516, 525, 530, 531], "jj": [11, 152, 208, 226, 252, 254, 270, 412, 468, 486, 512, 515, 525, 530, 531, 532], "enclos": [11, 270], "treat": [11, 183, 196, 270, 442, 456], "group": [11, 92, 160, 161, 162, 167, 171, 177, 193, 236, 237, 270, 350, 419, 420, 421, 427, 431, 437, 453, 497], "so": [11, 17, 18, 23, 28, 36, 49, 50, 56, 88, 125, 138, 146, 155, 169, 210, 211, 234, 270, 275, 276, 281, 286, 294, 308, 309, 315, 347, 348, 385, 398, 406, 415, 429, 470, 471, 494, 522], "here": [11, 128, 210, 226, 270, 388, 470, 486, 526], "specif": [11, 51, 71, 84, 85, 87, 93, 94, 99, 114, 115, 123, 125, 140, 161, 163, 166, 168, 169, 172, 178, 181, 188, 208, 211, 234, 237, 238, 270, 310, 330, 342, 344, 346, 351, 352, 357, 373, 374, 382, 385, 400, 420, 422, 425, 428, 429, 432, 438, 448, 468, 471, 494, 498, 530], "noun": [11, 253, 270, 513], "success": [11, 96, 107, 166, 270, 354, 366, 425], "grammar": [11, 160, 161, 162, 214, 221, 270, 419, 420, 421, 481], "parser": [11, 84, 85, 123, 258, 270, 342, 344, 382, 383], "Of": [11, 91, 196, 270, 349, 456], "postag": [11, 270], "11": [11, 82, 118, 119, 131, 132, 135, 152, 245, 256, 270, 341, 377, 378, 390, 392, 395, 412, 506, 517], "21": [11, 131, 132, 142, 152, 256, 270, 390, 392, 402, 412, 517], "35": [11, 95, 152, 164, 256, 270, 353, 412, 423, 517], "39": [11, 142, 147, 152, 246, 254, 256, 270, 402, 407, 412, 507, 515, 517], "52": [11, 142, 152, 254, 270, 402, 412, 515], "58": [11, 78, 152, 270, 337, 412], "choicesdelimit": [12, 13, 19, 37, 45, 271, 277, 295, 304], "setchoicesdelimit": [12, 13, 19, 37, 45, 271, 277, 295, 304], "coalescesent": [12, 14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 42, 46, 47, 49, 53, 54, 56, 57, 126, 127, 272, 273, 275, 276, 278, 279, 281, 282, 283, 285, 287, 288, 290, 291, 292, 294, 297, 298, 301, 305, 306, 308, 312, 313, 315, 316, 387], "getclass": [12, 15, 16, 17, 18, 21, 22, 23, 25, 26, 27, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 47, 48, 49, 54, 55, 56, 57, 58, 64, 67, 78, 80, 136, 143, 273, 274, 275, 276, 279, 280, 281, 283, 284, 285, 288, 289, 290, 292, 293, 294, 298, 299, 301, 302, 306, 307, 308, 313, 314, 315, 316, 317, 325, 337, 339, 403], "setcoalescesent": [12, 15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 126, 127, 273, 275, 276, 279, 281, 283, 285, 288, 290, 292, 294, 298, 301, 306, 308, 313, 315, 316, 387], "maxsentencelength": [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 63, 64, 65, 70, 71, 72, 73, 74, 75, 76, 77, 90, 91, 93, 94, 95, 96, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 116, 118, 119, 120, 214, 221, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 304, 305, 306, 307, 308, 310, 311, 312, 313, 314, 315, 316, 317, 322, 323, 329, 330, 331, 332, 333, 334, 335, 336, 349, 351, 352, 353, 354, 356, 357, 358, 359, 363, 364, 365, 366, 367, 368, 369, 370, 372, 373, 375, 377, 378, 379, 481], "setmaxsentencelength": [12, 18, 48, 49, 56, 90, 100, 116, 214, 221, 276, 307, 308, 315, 358, 375, 481], "dropout": [12, 28, 50, 136, 140, 286, 309, 400], "setdropout": [12, 28, 50, 136, 140, 286, 309, 400, 526], "setshuffleperepoch": [12, 44, 234, 303, 494, 522], "setthreshold": [12, 44, 50, 124, 125, 126, 127, 214, 221, 234, 235, 237, 303, 309, 385, 387, 481, 494, 498, 522], "setverbos": [12, 44, 136, 139, 140, 141, 148, 150, 303, 399, 400, 401, 409], "shuffleperepoch": [12, 44, 303], "threshold": [12, 44, 50, 92, 100, 116, 124, 125, 126, 127, 139, 143, 160, 161, 162, 183, 196, 203, 214, 221, 235, 236, 237, 238, 303, 309, 350, 358, 375, 385, 387, 399, 403, 419, 420, 421, 442, 456, 462, 481, 497, 498], "setthresholdlabel": [12, 50, 126, 127, 309, 387], "thresholdlabel": [12, 50, 126, 127, 309, 387], "classifi": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 66, 67, 78, 125, 201, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 324, 325, 337, 383, 385, 460, 526], "dl": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 127, 140, 141, 143, 184, 185, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 387, 400, 401, 403, 444, 445], "albert": [13, 14, 15, 16, 91, 271, 272, 273, 274, 349], "choic": [13, 19, 37, 45, 110, 111, 133, 168, 171, 271, 277, 295, 304, 369, 370, 393, 428, 431], "linear": [13, 14, 15, 16, 19, 20, 21, 22, 24, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 52, 53, 54, 55, 57, 58, 78, 92, 160, 161, 162, 166, 221, 271, 272, 273, 274, 277, 278, 279, 280, 282, 283, 284, 287, 288, 289, 291, 292, 293, 295, 297, 298, 299, 300, 301, 302, 304, 305, 306, 307, 311, 312, 313, 314, 316, 317, 337, 350, 419, 420, 421, 425, 481], "layer": [13, 14, 15, 16, 19, 20, 21, 22, 24, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 52, 53, 54, 55, 57, 58, 91, 92, 93, 94, 98, 103, 106, 120, 160, 161, 162, 221, 271, 272, 273, 274, 277, 278, 279, 280, 282, 283, 284, 287, 288, 289, 291, 292, 293, 295, 297, 298, 299, 300, 301, 302, 304, 305, 306, 307, 311, 312, 313, 314, 316, 317, 349, 350, 351, 352, 356, 361, 365, 379, 419, 420, 421, 481], "pool": [13, 15, 19, 21, 25, 30, 34, 37, 39, 42, 45, 47, 54, 57, 92, 97, 103, 108, 112, 113, 114, 160, 161, 221, 271, 273, 277, 279, 283, 288, 292, 295, 298, 301, 304, 306, 313, 316, 350, 355, 361, 367, 371, 372, 373, 419, 420, 481], "softmax": [13, 15, 17, 18, 19, 21, 23, 25, 27, 32, 34, 36, 37, 39, 42, 45, 47, 49, 54, 56, 57, 98, 100, 116, 183, 221, 271, 273, 275, 276, 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434, 435, 436, 437, 438, 439, 444, 447, 468, 481, 498], "case": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 85, 86, 87, 91, 93, 94, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 118, 119, 120, 122, 127, 130, 134, 141, 144, 164, 167, 174, 183, 184, 187, 193, 199, 221, 252, 253, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 344, 345, 346, 348, 349, 351, 352, 353, 354, 356, 357, 359, 361, 363, 364, 365, 366, 367, 368, 369, 370, 372, 373, 377, 378, 379, 380, 387, 389, 394, 401, 423, 427, 434, 442, 444, 447, 453, 458, 481, 512, 513, 526], "512": [13, 15, 17, 18, 19, 21, 23, 25, 27, 30, 32, 34, 36, 37, 39, 42, 45, 47, 49, 51, 54, 56, 57, 88, 95, 101, 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20, 37, 45, 271, 277, 278, 295, 304], "eiffel": [13, 19, 37, 45, 271, 277, 295, 304], "tower": [13, 19, 37, 45, 271, 277, 295, 304], "countri": [13, 19, 37, 45, 183, 271, 277, 295, 304, 442], "germani": [13, 19, 37, 45, 271, 277, 295, 304], "franc": [13, 19, 27, 37, 45, 170, 271, 277, 285, 295, 304, 318, 430], "itali": [13, 19, 37, 45, 271, 277, 295, 304], "split": [13, 19, 37, 45, 66, 67, 77, 78, 79, 80, 86, 88, 92, 154, 155, 160, 161, 162, 170, 190, 191, 192, 196, 221, 237, 245, 271, 277, 295, 304, 318, 324, 325, 336, 337, 338, 339, 345, 347, 350, 414, 415, 419, 420, 421, 430, 449, 451, 452, 456, 481, 498, 506], "bert_base_uncased_multiple_choic": [13, 19, 37, 271, 277, 295], "span": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 163, 178, 179, 272, 278, 282, 287, 291, 297, 300, 305, 311, 312, 422, 438, 439], "squad": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 91, 93, 94, 98, 107, 110, 111, 163, 272, 278, 282, 287, 291, 297, 300, 305, 311, 312, 349, 351, 352, 356, 366, 369, 370, 422], "comput": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 67, 78, 80, 81, 91, 98, 99, 104, 115, 141, 166, 170, 171, 172, 181, 185, 196, 208, 272, 278, 282, 287, 291, 297, 300, 305, 311, 312, 318, 325, 337, 339, 340, 349, 356, 357, 363, 374, 401, 425, 430, 431, 432, 440, 445, 456, 468, 530], "logit": [14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 32, 33, 34, 36, 38, 39, 41, 42, 46, 47, 49, 52, 53, 54, 56, 57, 146, 160, 161, 162, 221, 272, 273, 275, 276, 278, 279, 281, 282, 283, 285, 287, 290, 291, 292, 294, 297, 298, 300, 301, 305, 306, 308, 311, 312, 313, 315, 316, 406, 419, 420, 421, 481], "albert_base_qa_squad2": [14, 272], "128": [14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 91, 93, 94, 96, 98, 99, 104, 110, 111, 118, 119, 120, 234, 272, 273, 274, 275, 276, 278, 279, 280, 281, 282, 283, 284, 285, 287, 288, 289, 290, 291, 292, 293, 294, 297, 299, 300, 301, 302, 303, 305, 306, 307, 308, 311, 312, 313, 314, 315, 316, 317, 349, 351, 352, 354, 356, 357, 363, 369, 370, 377, 378, 379, 494, 522], "what": [14, 20, 24, 29, 33, 38, 41, 46, 50, 52, 53, 65, 67, 72, 73, 84, 85, 125, 127, 143, 146, 170, 172, 176, 183, 191, 253, 272, 278, 282, 287, 291, 297, 300, 305, 309, 311, 312, 318, 323, 325, 331, 332, 342, 344, 385, 387, 403, 406, 430, 432, 436, 442, 451, 513, 523], "": [14, 17, 18, 20, 23, 24, 27, 29, 32, 33, 36, 38, 41, 46, 49, 52, 53, 56, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 86, 87, 88, 91, 95, 96, 98, 99, 100, 101, 105, 109, 110, 111, 115, 116, 118, 119, 122, 125, 133, 140, 144, 146, 158, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 183, 184, 185, 191, 192, 193, 196, 198, 202, 208, 210, 221, 226, 232, 234, 250, 260, 261, 272, 275, 276, 278, 281, 282, 285, 287, 290, 291, 294, 297, 300, 305, 308, 311, 312, 315, 318, 319, 323, 325, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 345, 346, 347, 348, 349, 353, 354, 356, 357, 358, 359, 364, 368, 369, 370, 375, 377, 378, 380, 385, 393, 400, 404, 406, 418, 421, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 442, 444, 445, 451, 452, 453, 456, 457, 461, 468, 470, 474, 475, 481, 483, 486, 489, 493, 494, 509, 511, 522, 525, 526, 527, 530], "my": [14, 16, 17, 20, 22, 24, 28, 29, 31, 33, 35, 38, 40, 41, 42, 43, 46, 48, 50, 52, 53, 55, 58, 86, 87, 88, 133, 135, 143, 146, 154, 165, 166, 167, 171, 173, 174, 175, 177, 187, 190, 192, 210, 272, 274, 275, 278, 280, 282, 286, 287, 289, 291, 293, 297, 299, 300, 301, 302, 305, 307, 309, 311, 312, 314, 317, 345, 346, 347, 393, 395, 403, 406, 414, 424, 425, 427, 431, 433, 434, 435, 437, 447, 449, 452, 470, 526], "clara": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 143, 272, 278, 282, 287, 291, 297, 300, 305, 311, 312, 403], "live": [14, 16, 20, 22, 24, 29, 31, 33, 35, 38, 40, 41, 43, 46, 48, 52, 53, 55, 58, 143, 166, 167, 234, 272, 274, 278, 280, 282, 287, 289, 291, 293, 297, 299, 300, 302, 305, 307, 311, 312, 314, 317, 403, 425, 427, 494, 522], "berkelei": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 173, 174, 175, 177, 272, 278, 282, 287, 291, 297, 300, 305, 311, 312, 433, 434, 435, 437], "instead": [14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 42, 46, 47, 49, 53, 54, 56, 57, 94, 125, 127, 131, 132, 179, 201, 202, 207, 208, 237, 273, 275, 276, 279, 281, 283, 285, 288, 290, 292, 294, 298, 301, 306, 308, 313, 315, 316, 352, 385, 387, 390, 392, 439, 460, 461, 466, 468, 530], "per": [14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 81, 95, 101, 109, 118, 119, 127, 131, 135, 137, 138, 139, 140, 141, 146, 149, 196, 208, 210, 226, 237, 240, 245, 252, 273, 274, 275, 276, 279, 280, 281, 283, 284, 285, 286, 288, 289, 290, 292, 293, 294, 298, 299, 301, 302, 303, 306, 307, 308, 309, 313, 314, 315, 316, 317, 340, 353, 359, 368, 377, 378, 387, 390, 395, 397, 398, 399, 400, 406, 456, 468, 470, 486, 498, 501, 506, 512], "averag": [14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 42, 46, 47, 49, 51, 53, 54, 56, 57, 95, 97, 101, 104, 109, 112, 118, 119, 127, 140, 152, 181, 221, 273, 275, 276, 279, 281, 283, 285, 288, 290, 292, 294, 298, 301, 306, 308, 310, 313, 315, 316, 353, 355, 359, 363, 368, 371, 377, 378, 387, 400, 412, 440, 481], "regress": [15, 21, 25, 30, 34, 39, 42, 47, 54, 57, 163, 179, 273, 279, 283, 288, 292, 298, 301, 306, 313, 316, 422, 439], "multi": [15, 21, 25, 28, 30, 34, 39, 42, 44, 47, 50, 54, 57, 91, 113, 115, 118, 119, 125, 127, 170, 221, 273, 279, 283, 286, 288, 292, 298, 301, 303, 306, 309, 313, 316, 318, 349, 372, 374, 377, 378, 385, 387, 430, 481], "sequenceclassifi": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 273, 275, 276, 279, 281, 283, 285, 288, 290, 292, 294, 298, 301, 306, 308, 313, 315, 316], 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165, 273, 276, 279, 281, 286, 288, 290, 292, 294, 298, 306, 308, 309, 313, 315, 316, 418, 424, 526], "child": [15, 18, 21, 23, 30, 32, 34, 36, 39, 47, 49, 54, 56, 57, 273, 276, 279, 281, 288, 290, 292, 294, 298, 306, 308, 313, 315, 316], "pretti": [15, 18, 21, 23, 30, 32, 34, 36, 39, 44, 47, 49, 54, 56, 57, 125, 273, 276, 279, 281, 288, 290, 292, 294, 298, 303, 306, 308, 313, 315, 316, 385], "bore": [15, 18, 21, 23, 30, 32, 34, 36, 39, 47, 49, 54, 56, 57, 273, 276, 279, 281, 288, 290, 292, 294, 298, 306, 308, 313, 315, 316], "neg": [15, 18, 21, 23, 25, 30, 32, 34, 36, 39, 47, 49, 50, 54, 56, 57, 113, 157, 158, 181, 221, 234, 273, 276, 279, 281, 283, 288, 290, 292, 294, 298, 306, 308, 309, 313, 315, 316, 372, 417, 418, 440, 481, 494, 522], "limit": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 67, 69, 78, 80, 88, 91, 96, 105, 107, 114, 117, 120, 125, 158, 179, 184, 203, 221, 273, 275, 276, 279, 281, 283, 285, 288, 290, 292, 294, 298, 301, 306, 308, 313, 315, 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340, 350, 353, 358, 369, 370, 372, 373, 375, 400, 403, 415, 419, 420, 421, 423, 427, 428, 434, 435, 481], "deploy": [69, 70, 327, 329], "laptop": [69, 327], "desktop": [69, 87, 327, 346], "cloud": [69, 125, 327, 385], "visualqa": [69, 70, 77, 327, 329, 336], "gemma3_4b_it_int4": [69, 327], "9": [69, 98, 118, 119, 135, 147, 160, 165, 169, 175, 176, 327, 356, 377, 378, 395, 407, 419, 424, 429, 435, 436, 525, 530, 531], "testdf": [69, 77, 327, 336], "bo": [69, 160, 161, 162, 327, 419, 420, 421], "start_of_turn": [69, 327], "nyou": [69, 76, 88, 210, 327, 335, 347, 470], "assist": [69, 70, 72, 73, 75, 76, 102, 164, 168, 176, 178, 210, 226, 327, 329, 331, 332, 334, 335, 360, 423, 428, 436, 438, 470, 486], "start_of_imag": [69, 327], "end_of_turn": [69, 327], "http": [69, 70, 71, 72, 73, 74, 75, 76, 77, 91, 95, 96, 98, 100, 102, 103, 104, 109, 115, 116, 146, 147, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 196, 221, 236, 238, 250, 349, 353, 354, 356, 358, 360, 361, 363, 368, 374, 375, 406, 407, 422, 424, 434, 435, 436, 437, 456, 497, 499, 511, 523], "arxiv": [69, 70, 71, 72, 73, 74, 75, 76, 77, 105, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 364, 422, 436], "pdf": [69, 70, 71, 72, 73, 74, 75, 76, 77, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 236, 237, 238, 243, 245, 246, 249, 250, 497, 498, 499, 506, 507, 510, 511], "1909": [69, 70, 71, 72, 73, 74, 75, 76, 77, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221], "05858": [69, 70, 71, 72, 73, 74, 75, 76, 77, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221], "__": [69, 70, 71, 72, 73, 74, 75, 76, 77, 87, 141, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 201, 202, 221, 346, 401, 422, 460, 461], "ctrl": [69, 70, 71, 72, 73, 74, 75, 76, 77, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 327, 329, 330, 331, 332, 333, 334, 335, 336, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "condit": [69, 70, 71, 72, 73, 74, 75, 76, 77, 93, 94, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 221, 327, 329, 330, 331, 332, 333, 334, 335, 336, 351, 352, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 481], "control": [69, 70, 71, 72, 73, 74, 75, 76, 77, 146, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 183, 221, 318, 327, 329, 330, 331, 332, 333, 334, 335, 336, 406, 422, 423, 424, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 442, 481], "internvl": [70, 329], "advanc": [70, 95, 127, 168, 171, 176, 177, 178, 199, 210, 213, 329, 353, 387, 428, 431, 436, 437, 438, 458, 470, 473], "mllm": [70, 329], "seri": [70, 165, 173, 177, 329, 424, 433, 437], "build": [70, 98, 99, 110, 111, 123, 125, 130, 166, 167, 169, 173, 174, 178, 234, 246, 249, 260, 329, 356, 357, 369, 370, 382, 385, 389, 425, 427, 429, 433, 434, 438, 494, 507, 510, 522], "core": [70, 81, 114, 329, 340, 373], "introduc": [70, 93, 94, 95, 99, 103, 104, 105, 107, 114, 163, 167, 168, 171, 175, 177, 178, 179, 329, 351, 352, 353, 357, 361, 363, 364, 366, 373, 422, 427, 428, 431, 435, 437, 438, 439], "signific": [70, 110, 111, 118, 119, 125, 128, 167, 177, 329, 369, 370, 377, 378, 385, 388, 427, 437], "enhanc": [70, 71, 98, 158, 163, 168, 171, 174, 329, 330, 356, 418, 422, 428, 431, 434], "strategi": [70, 92, 97, 108, 112, 113, 114, 129, 131, 133, 154, 160, 161, 162, 183, 220, 221, 237, 249, 329, 350, 355, 367, 371, 372, 373, 390, 393, 414, 419, 420, 421, 442, 480, 481, 510], "int4": [70, 329], "quantiz": [70, 329], "internvl2_5_1b_int4": [70, 329], "larger": [70, 71, 73, 77, 79, 92, 95, 96, 99, 110, 111, 160, 161, 162, 163, 164, 165, 166, 167, 168, 170, 171, 173, 174, 175, 176, 177, 178, 179, 181, 318, 329, 330, 332, 336, 338, 350, 353, 354, 357, 369, 370, 419, 420, 421, 422, 423, 424, 425, 427, 428, 430, 431, 433, 434, 435, 436, 437, 438, 439, 440], "im_start": [70, 76, 77, 329, 335, 336], "ndescrib": [70, 74, 329, 333], "im_end": [70, 76, 329, 335], "janu": [71, 330], "alongsid": [71, 73, 178, 237, 238, 330, 332, 438], "novel": [71, 98, 107, 114, 120, 125, 163, 172, 330, 356, 366, 373, 379, 385, 422, 432], "autoregress": [71, 120, 330, 379], "unifi": [71, 163, 179, 236, 249, 330, 422, 439, 497, 510], "understand": [71, 93, 94, 99, 107, 118, 120, 125, 163, 168, 174, 175, 178, 179, 196, 330, 351, 352, 357, 366, 377, 379, 385, 422, 428, 434, 435, 438, 439, 456, 525], "decoupl": [71, 330], "separ": [71, 86, 88, 99, 110, 133, 135, 154, 155, 184, 193, 196, 202, 226, 237, 238, 252, 330, 345, 347, 357, 369, 393, 395, 414, 415, 444, 453, 456, 461, 486, 498, 512, 524], "pathwai": [71, 330], "allevi": [71, 330], "conflict": [71, 330], "role": [71, 210, 330, 470], "surpass": [71, 78, 98, 109, 165, 171, 330, 337, 356, 368, 424, 431], "previou": [71, 78, 104, 107, 118, 119, 166, 168, 172, 177, 217, 330, 337, 363, 366, 377, 378, 425, 428, 432, 437, 526], "exce": [71, 110, 111, 146, 330, 369, 370, 406], "deepseek": [71, 330], "llm": [71, 73, 114, 167, 168, 174, 177, 178, 330, 332, 373, 427, 428, 434, 437, 438], "3b": [71, 178, 330, 438], "approxim": [71, 181, 330, 440], "500b": [71, 330], "emploi": [71, 88, 120, 330, 347, 379], "siglip": [71, 330], "downsampl": [71, 330], "16": [71, 91, 120, 142, 238, 254, 330, 349, 379, 402, 499, 515, 525], "janus_1_3b_int4": [71, 330], "suit": [71, 73, 76, 77, 95, 113, 330, 332, 335, 336, 353, 372], "image_placehold": [71, 330], "nassist": [71, 74, 77, 330, 333, 336], "unusu": [71, 72, 73, 75, 76, 77, 330, 331, 332, 334, 335, 336], "aspect": [71, 72, 73, 75, 77, 171, 330, 331, 332, 334, 336, 431], "presenc": [71, 72, 73, 75, 77, 160, 161, 162, 221, 330, 331, 332, 334, 336, 419, 420, 421, 481], "ly": [71, 72, 73, 75, 76, 77, 162, 330, 331, 332, 334, 335, 336, 421], "pink": [71, 72, 73, 75, 76, 77, 162, 330, 331, 332, 334, 335, 336, 421], "couch": [71, 72, 73, 75, 76, 77, 79, 330, 331, 332, 334, 335, 336, 338], "parallel": [71, 92, 102, 154, 155, 208, 221, 252, 330, 350, 360, 414, 415, 468, 481, 512, 530], "llava": [72, 331], "llava_1_5_7b_hf": [72, 331], "llama": [73, 92, 160, 161, 162, 167, 168, 171, 174, 207, 210, 332, 350, 419, 420, 421, 427, 428, 431, 434, 466, 470], "compris": [73, 146, 332, 406], "11b": [73, 332], "90b": [73, 332], "These": [73, 78, 91, 110, 111, 114, 120, 125, 139, 166, 168, 177, 210, 240, 332, 337, 349, 369, 370, 373, 379, 385, 399, 425, 428, 437, 470, 501, 524], "reason": [73, 86, 88, 146, 164, 171, 174, 175, 176, 178, 332, 345, 347, 406, 423, 431, 434, 435, 436, 438], "proprietari": [73, 113, 173, 177, 332, 372, 433, 437], "industri": [73, 87, 332, 346], "llama_3_2_11b_vision_instruct_int4": [73, 332], "begin_of_text": [73, 332], "start_header_id": [73, 102, 168, 176, 210, 332, 360, 428, 436, 470], "end_header_id": [73, 102, 168, 176, 210, 332, 360, 428, 436, 470], "eot_id": [73, 102, 210, 332, 360, 470], "paligemma": [74, 333], "paligemma_3b_pt_224_int4": [74, 333], "stand": [74, 79, 93, 94, 162, 171, 333, 338, 351, 352, 421, 431], "grassi": [74, 162, 333, 421], "field": [74, 79, 95, 100, 116, 117, 122, 134, 162, 173, 174, 175, 177, 258, 333, 338, 353, 358, 375, 376, 380, 394, 421, 433, 434, 435, 437, 519], "phi_3_vision_128k_instruct": [75, 334], "image_1": [75, 334], "nwhat": [75, 334], "phi3v": [75, 334], "qwen2": [76, 162, 177, 335, 421, 437], "integr": [76, 95, 120, 125, 145, 170, 234, 318, 335, 353, 379, 385, 405, 430, 494, 522, 524], "respons": [76, 146, 155, 167, 168, 174, 178, 335, 406, 415, 427, 428, 434, 438], "queri": [76, 95, 101, 109, 113, 159, 161, 167, 171, 177, 181, 335, 353, 359, 368, 372, 420, 427, 431, 437, 440, 523], "qwen2_vl_2b_instruct_int4": [76, 335], "vision_start": [76, 335], "image_pad": [76, 335], "vision_end": [76, 335], "becaus": [76, 146, 157, 260, 319, 335, 406, 417, 440, 475, 493], "smolvlm": [77, 336], "compact": [77, 336], "arbitrari": [77, 163, 171, 336, 422, 431], "produc": [77, 336, 526], "stori": [77, 336], "ground": [77, 79, 336, 338], "Its": [77, 78, 85, 143, 163, 336, 337, 344, 403, 422], "suitabl": [77, 141, 167, 174, 336, 401, 427, 434], "devic": [77, 99, 336, 357], "strong": [77, 101, 102, 118, 119, 173, 174, 175, 177, 336, 359, 360, 377, 378, 433, 434, 435, 437], "smolvlm_instruct_int4": [77, 336], "49153": [77, 336], "81": [77, 254, 336, 515], "patch": [77, 80, 336, 339], "14": [77, 138, 152, 194, 208, 226, 240, 246, 254, 336, 398, 412, 454, 468, 486, 501, 507, 515], "pad": [77, 255, 336, 516], "constant": [77, 220, 222, 336, 480, 482], "end_of_utter": [77, 336], "openvino": [77, 93, 94, 101, 102, 106, 110, 118, 336, 351, 352, 359, 360, 365, 369, 377], "stop": [77, 125, 139, 145, 146, 160, 161, 162, 164, 168, 176, 179, 187, 221, 242, 258, 336, 385, 399, 406, 419, 420, 421, 423, 428, 436, 439, 447, 481, 503], "termin": [77, 336], "swinimageclassif": [78, 337], "shift": [78, 337], "ze": [78, 337], "yutong": [78, 337], "lin": [78, 337], "yue": [78, 337], "cao": [78, 337], "han": [78, 337], "hu": [78, 337], "yixuan": [78, 337], "zheng": [78, 337], "zhang": [78, 337], "stephen": [78, 337], "bain": [78, 337], "guo": [78, 337], "basic": [78, 125, 154, 210, 260, 337, 385, 414, 470, 525], "whose": [78, 175, 337, 348, 435], "scheme": [78, 110, 163, 337, 369, 422], "bring": [78, 169, 337, 429, 526], "greater": [78, 125, 146, 160, 161, 162, 221, 337, 385, 406, 419, 420, 421, 481], "attent": [78, 80, 92, 98, 105, 106, 146, 160, 161, 162, 167, 171, 177, 221, 337, 339, 350, 356, 364, 365, 406, 419, 420, 421, 427, 431, 437, 481], "non": [78, 165, 169, 174, 193, 196, 246, 249, 337, 424, 429, 434, 453, 456, 507, 510], "overlap": [78, 86, 88, 130, 134, 235, 237, 337, 345, 347, 389, 394, 498], "cross": [78, 118, 119, 142, 337, 377, 378, 402], "connect": [78, 337], 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499, 507, 508, 511, 520], "complex": [78, 103, 114, 115, 125, 171, 177, 184, 185, 337, 361, 373, 374, 385, 431, 437, 444, 445], "respect": [78, 98, 117, 139, 140, 141, 175, 254, 337, 348, 356, 376, 399, 400, 401, 435, 515], "broad": [78, 86, 166, 337, 345, 425], "rang": [78, 93, 94, 98, 99, 101, 104, 118, 119, 160, 161, 162, 163, 167, 168, 174, 203, 221, 337, 351, 352, 356, 357, 359, 363, 377, 378, 419, 420, 421, 422, 427, 428, 434, 462, 481], "1k": [78, 337], "dens": [78, 93, 94, 95, 169, 175, 176, 181, 337, 351, 352, 353, 429, 435, 436, 440], "box": [78, 102, 169, 172, 337, 360, 429, 432], "ap": [78, 337], "51": [78, 200, 209, 254, 337, 459, 469, 515], "53": [78, 130, 134, 337, 389, 394], "miou": [78, 337], "val": [78, 337], "margin": [78, 107, 120, 337, 366, 379], "prove": [78, 337], "benefici": [78, 125, 337, 385], "mlp": [78, 337], "visionencoderdecod": [79, 338], "auto": [79, 163, 188, 338, 422, 448], "beit": [79, 338], "deit": [79, 338], "gpt2": [79, 166, 338, 425], 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431, 433, 434, 435, 436, 437, 438, 439], "imagecapt": [79, 338], "fill": [79, 163, 258, 338, 422, 519], "furnitur": [79, 338], "lai": [79, 172, 338, 432], "anoth": [79, 106, 162, 221, 246, 338, 365, 421, 481, 507, 527], "brown": [79, 162, 236, 338, 421, 497], "bear": [79, 236, 338, 497], "bodi": [79, 162, 338, 421], "water": [79, 162, 338, 421], "flock": [79, 338], "chicken": [79, 162, 338, 421], "lush": [79, 338], "green": [79, 162, 338, 421], "small": [79, 80, 87, 91, 92, 96, 99, 100, 109, 116, 128, 152, 160, 161, 162, 172, 174, 175, 178, 208, 221, 237, 254, 338, 339, 346, 349, 350, 354, 357, 358, 368, 375, 388, 412, 419, 420, 421, 432, 434, 435, 438, 468, 481, 515, 530], "wet": [79, 338], "wooden": [79, 338], "floor": [79, 162, 338, 421], "wear": [79, 162, 338, 421], "blue": [79, 338], "sweater": [79, 162, 338, 421], "man": [79, 92, 161, 162, 166, 167, 203, 338, 350, 420, 421, 425, 427, 462], "cow": [79, 338], "altern": [80, 125, 146, 157, 183, 184, 185, 208, 212, 339, 385, 406, 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"sparknlp.partition.partition_properties.HasEmailReaderProperties.setAddAttachmentContent"]], "setappendcells() (hasexcelreaderproperties method)": [[237, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setAppendCells"], [498, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setAppendCells"]], "setcellseparator() (hasexcelreaderproperties method)": [[237, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setCellSeparator"], [498, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setCellSeparator"]], "setchunkingstrategy() (haschunkerproperties method)": [[237, "sparknlp.partition.partition_properties.HasChunkerProperties.setChunkingStrategy"], [498, "sparknlp.partition.partition_properties.HasChunkerProperties.setChunkingStrategy"]], "setcombinetextundernchars() (haschunkerproperties method)": [[237, "sparknlp.partition.partition_properties.HasChunkerProperties.setCombineTextUnderNChars"], [498, 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27, 28, 32, 36, 49, 51, 56, 91, 95, 101, 109, 110, 111, 144, 167, 169, 196, 276, 277, 282, 286, 287, 291, 295, 309, 311, 316, 350, 354, 360, 369, 370, 371, 405, 428, 430, 457, 489, 528], "alloc": [1, 489], "locat": [1, 10, 13, 19, 37, 45, 122, 154, 241, 270, 272, 278, 296, 305, 381, 415, 489, 503, 528], "download": [1, 7, 8, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 84, 85, 91, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 115, 116, 117, 118, 119, 120, 123, 127, 128, 130, 134, 139, 140, 142, 143, 152, 155, 158, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 183, 184, 185, 187, 193, 196, 241, 242, 266, 268, 269, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 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299, 302, 307, 311, 314, 317, 339, 350, 352, 353, 357, 358, 365, 366, 367, 370, 371, 373, 378, 379, 380, 401, 403, 404, 423, 440], "offlin": [7, 235, 266, 496], "step": [7, 28, 44, 50, 100, 116, 140, 150, 235, 266, 287, 304, 310, 359, 376, 401, 410, 496, 524, 528], "align": [7, 175, 177, 266, 436, 438], "target": [7, 81, 115, 128, 160, 161, 162, 163, 169, 170, 172, 193, 199, 208, 222, 241, 266, 319, 341, 375, 420, 421, 422, 423, 430, 431, 433, 454, 459, 469, 483, 503], "label": [7, 15, 16, 17, 18, 21, 22, 23, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 47, 48, 49, 50, 54, 55, 56, 57, 58, 66, 67, 78, 80, 84, 85, 95, 101, 114, 122, 127, 137, 138, 139, 140, 141, 143, 149, 150, 157, 158, 179, 183, 188, 196, 222, 235, 253, 266, 274, 275, 276, 277, 280, 281, 282, 284, 285, 286, 287, 289, 290, 291, 293, 294, 295, 299, 300, 302, 303, 304, 307, 308, 309, 310, 314, 315, 316, 317, 318, 325, 326, 338, 340, 343, 345, 354, 360, 374, 381, 388, 398, 399, 400, 401, 402, 404, 409, 410, 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440, 480, 499, 521, 529], "york": [10, 143, 202, 270, 404, 462], "jersei": [10, 202, 270, 462], "aren": [10, 202, 270, 462], "t": [10, 17, 18, 23, 27, 32, 36, 49, 56, 88, 92, 99, 110, 128, 144, 155, 157, 160, 161, 162, 174, 193, 202, 210, 222, 270, 276, 277, 282, 286, 291, 295, 309, 316, 348, 351, 358, 370, 389, 405, 416, 418, 420, 421, 422, 435, 454, 462, 471, 483], "far": [10, 146, 166, 202, 270, 407, 426, 462], "apart": [10, 84, 85, 202, 270, 343, 345, 462], "actual": [10, 135, 202, 213, 270, 396, 462, 474], "id": [10, 44, 61, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 87, 118, 122, 146, 147, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 183, 199, 200, 202, 209, 213, 222, 235, 253, 270, 304, 320, 327, 328, 330, 331, 332, 333, 334, 335, 336, 337, 347, 378, 381, 407, 408, 420, 421, 422, 423, 424, 425, 426, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 443, 459, 460, 462, 470, 474, 483, 496, 514], "defin": [10, 11, 122, 138, 139, 140, 141, 183, 187, 191, 202, 210, 227, 235, 241, 246, 270, 271, 381, 399, 400, 401, 402, 443, 448, 452, 462, 471, 488, 496, 503, 508, 524, 528, 532], "amongst": [10, 202, 270, 462], "thing": [10, 168, 183, 202, 270, 429, 443, 462], "explain_document_dl": [10, 202, 208, 227, 241, 270, 462, 469, 488, 503], "chunktodoc": [10, 270], "chunkconvert": [10, 270], "explainresult": [10, 202, 270, 462], "selectexpr": [10, 11, 28, 51, 63, 66, 67, 78, 79, 80, 84, 85, 86, 87, 88, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 125, 128, 130, 131, 132, 133, 134, 135, 138, 142, 143, 144, 152, 154, 155, 157, 162, 170, 186, 187, 190, 192, 193, 194, 199, 201, 202, 227, 253, 254, 255, 270, 271, 287, 311, 319, 323, 325, 326, 338, 339, 340, 343, 345, 346, 347, 348, 350, 352, 353, 354, 355, 356, 357, 358, 359, 360, 362, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 386, 389, 390, 391, 393, 394, 395, 396, 399, 403, 404, 405, 413, 415, 416, 418, 422, 431, 447, 448, 450, 453, 454, 455, 459, 461, 462, 488, 514, 515, 517, 527, 528, 533], "explod": [10, 11, 28, 51, 63, 84, 85, 86, 88, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 122, 125, 130, 132, 133, 134, 135, 138, 142, 143, 152, 154, 155, 170, 194, 201, 202, 227, 238, 253, 255, 270, 271, 287, 311, 319, 323, 343, 345, 346, 348, 350, 352, 353, 354, 355, 356, 357, 358, 359, 360, 362, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 386, 390, 393, 394, 395, 396, 399, 403, 404, 413, 415, 416, 431, 455, 461, 462, 488, 500, 514, 517, 527, 528, 533], "col": [10, 84, 85, 122, 130, 138, 142, 181, 199, 201, 202, 204, 209, 227, 251, 270, 343, 345, 381, 390, 399, 403, 462, 488, 513, 527], "7": [10, 50, 78, 93, 94, 98, 131, 132, 147, 152, 166, 167, 171, 174, 175, 202, 255, 257, 261, 270, 310, 338, 352, 353, 357, 391, 393, 408, 413, 426, 428, 432, 435, 436, 462, 517, 519, 527], "loc": [10, 16, 22, 26, 31, 35, 40, 43, 48, 55, 58, 123, 138, 139, 140, 202, 208, 227, 241, 253, 270, 275, 281, 285, 290, 294, 300, 303, 308, 315, 318, 383, 399, 400, 401, 462, 469, 488, 503, 514], "22": [10, 202, 253, 270, 462, 514, 527], "part": [11, 96, 125, 128, 151, 152, 160, 161, 162, 167, 186, 196, 199, 222, 238, 239, 255, 259, 271, 349, 355, 386, 389, 412, 413, 420, 421, 422, 428, 447, 457, 459, 483, 517, 534], "tag": [11, 28, 44, 50, 87, 96, 137, 138, 139, 140, 142, 151, 152, 196, 219, 227, 235, 237, 238, 239, 251, 253, 255, 256, 259, 271, 287, 304, 310, 347, 355, 398, 399, 400, 401, 403, 412, 413, 457, 479, 488, 496, 499, 500, 513, 514, 517, 518, 521, 534], "order": [11, 86, 104, 120, 122, 125, 131, 160, 161, 162, 163, 167, 174, 184, 185, 203, 208, 213, 222, 227, 250, 271, 346, 349, 364, 380, 381, 386, 391, 420, 421, 422, 423, 428, 435, 445, 446, 463, 469, 474, 483, 488, 512, 528, 529, 532, 534], "meaning": [11, 186, 271, 447], "phrase": [11, 96, 100, 116, 130, 134, 271, 355, 359, 376, 390, 395], "onto": [11, 227, 271, 488, 529], "pars": [11, 83, 84, 85, 96, 122, 128, 131, 132, 183, 184, 185, 208, 210, 212, 237, 238, 241, 247, 248, 251, 253, 255, 271, 343, 344, 345, 355, 381, 389, 391, 393, 443, 445, 446, 469, 471, 473, 499, 503, 509, 510, 513, 514, 517], "regular": [11, 86, 133, 139, 154, 271, 346, 394, 400, 415], "express": [11, 50, 63, 86, 131, 133, 154, 271, 310, 323, 346, 391, 394, 415], "wrap": [11, 271, 320, 441, 475, 476, 485, 491, 495, 511], "angl": [11, 114, 271, 374], "bracket": [11, 271], "easili": [11, 103, 152, 201, 271, 362, 413, 461, 524], "distinguish": [11, 271], "itself": [11, 125, 172, 179, 196, 211, 271, 349, 386, 433, 440, 457, 472, 529], "form": [11, 28, 44, 50, 117, 122, 128, 130, 131, 133, 134, 155, 157, 184, 185, 196, 201, 235, 253, 254, 271, 287, 304, 310, 377, 381, 389, 390, 391, 394, 395, 416, 418, 445, 446, 457, 461, 496, 514, 515, 528], "peter": [11, 105, 128, 144, 152, 155, 184, 186, 253, 271, 365, 389, 405, 413, 416, 445, 447, 514], "piper": [11, 128, 152, 186, 271, 389, 413, 447], "employe": [11, 128, 152, 186, 271, 389, 413, 447], "pick": [11, 128, 152, 186, 271, 389, 413, 447], "peck": [11, 128, 152, 186, 271, 389, 413, 447], "pickl": [11, 128, 152, 186, 271, 389, 413, 447], "pepper": [11, 128, 152, 186, 271, 389, 413, 447], "nnp": [11, 152, 208, 227, 253, 254, 255, 256, 271, 413, 469, 488, 514, 515, 517, 518, 527, 532, 533, 534], "nn": [11, 86, 152, 253, 254, 255, 256, 271, 346, 413, 514, 515, 517, 518, 534], "vbp": [11, 152, 208, 254, 271, 413, 469, 515, 527, 532, 533], "vbg": [11, 152, 271, 413], "IN": [11, 152, 208, 227, 254, 255, 256, 271, 413, 469, 488, 515, 517, 518, 527, 532, 533], "jj": [11, 152, 208, 227, 253, 255, 271, 413, 469, 488, 514, 517, 527, 532, 533, 534], "enclos": [11, 271], "treat": [11, 183, 196, 271, 443, 457], "group": [11, 87, 92, 122, 160, 161, 162, 167, 171, 177, 193, 237, 238, 271, 347, 351, 381, 420, 421, 422, 428, 432, 438, 454, 499], "so": [11, 17, 18, 23, 28, 36, 49, 50, 56, 88, 125, 138, 146, 155, 169, 210, 211, 235, 271, 276, 277, 282, 287, 295, 309, 310, 316, 348, 349, 386, 399, 407, 416, 430, 471, 472, 496, 524], "here": [11, 128, 210, 227, 271, 389, 471, 488, 528], "specif": [11, 51, 71, 84, 85, 87, 93, 94, 99, 114, 115, 122, 123, 125, 140, 161, 163, 166, 168, 169, 172, 178, 181, 188, 208, 211, 235, 238, 239, 271, 311, 331, 343, 345, 347, 352, 353, 358, 374, 375, 381, 383, 386, 401, 421, 423, 426, 429, 430, 433, 439, 449, 469, 472, 496, 500, 532], "noun": [11, 254, 271, 515], "success": [11, 96, 107, 166, 271, 355, 367, 426], "grammar": [11, 160, 161, 162, 214, 222, 271, 420, 421, 422, 483], "parser": [11, 84, 85, 123, 259, 271, 343, 345, 383, 384], "Of": [11, 91, 196, 271, 350, 457], "postag": [11, 271], "11": [11, 82, 118, 119, 131, 132, 135, 152, 246, 257, 271, 342, 378, 379, 391, 393, 396, 413, 508, 519], "21": [11, 131, 132, 142, 152, 257, 271, 391, 393, 403, 413, 519], "35": [11, 95, 152, 164, 257, 271, 354, 413, 424, 519], "39": [11, 142, 147, 152, 247, 255, 257, 271, 403, 408, 413, 509, 517, 519], "52": [11, 142, 152, 255, 271, 403, 413, 517], "58": [11, 78, 152, 271, 338, 413], "choicesdelimit": [12, 13, 19, 37, 45, 272, 278, 296, 305], "setchoicesdelimit": [12, 13, 19, 37, 45, 272, 278, 296, 305], "coalescesent": [12, 14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 42, 46, 47, 49, 53, 54, 56, 57, 126, 127, 273, 274, 276, 277, 279, 280, 282, 283, 284, 286, 288, 289, 291, 292, 293, 295, 298, 299, 302, 306, 307, 309, 313, 314, 316, 317, 388], "getclass": [12, 15, 16, 17, 18, 21, 22, 23, 25, 26, 27, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 47, 48, 49, 54, 55, 56, 57, 58, 64, 67, 78, 80, 136, 143, 274, 275, 276, 277, 280, 281, 282, 284, 285, 286, 289, 290, 291, 293, 294, 295, 299, 300, 302, 303, 307, 308, 309, 314, 315, 316, 317, 318, 326, 338, 340, 404], "setcoalescesent": [12, 15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 126, 127, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 388], "maxsentencelength": [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 63, 64, 65, 70, 71, 72, 73, 74, 75, 76, 77, 90, 91, 93, 94, 95, 96, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 116, 118, 119, 120, 214, 222, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 309, 311, 312, 313, 314, 315, 316, 317, 318, 323, 324, 330, 331, 332, 333, 334, 335, 336, 337, 350, 352, 353, 354, 355, 357, 358, 359, 360, 364, 365, 366, 367, 368, 369, 370, 371, 373, 374, 376, 378, 379, 380, 483], "setmaxsentencelength": [12, 18, 48, 49, 56, 90, 100, 116, 214, 222, 277, 308, 309, 316, 359, 376, 483], "dropout": [12, 28, 50, 136, 140, 287, 310, 401], "setdropout": [12, 28, 50, 136, 140, 287, 310, 401, 528], "setshuffleperepoch": [12, 44, 235, 304, 496, 524], "setthreshold": [12, 44, 50, 124, 125, 126, 127, 214, 222, 235, 236, 238, 304, 310, 386, 388, 483, 496, 500, 524], "setverbos": [12, 44, 136, 139, 140, 141, 148, 150, 304, 400, 401, 402, 410], "shuffleperepoch": [12, 44, 304], "threshold": [12, 44, 50, 92, 100, 116, 124, 125, 126, 127, 139, 143, 160, 161, 162, 183, 196, 203, 214, 222, 236, 237, 238, 239, 304, 310, 351, 359, 376, 386, 388, 400, 404, 420, 421, 422, 443, 457, 463, 483, 499, 500], "setthresholdlabel": [12, 50, 126, 127, 310, 388], "thresholdlabel": [12, 50, 126, 127, 310, 388], "classifi": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 66, 67, 78, 125, 201, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 325, 326, 338, 384, 386, 461, 528], "dl": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 127, 140, 141, 143, 184, 185, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 388, 401, 402, 404, 445, 446], "albert": [13, 14, 15, 16, 91, 272, 273, 274, 275, 350], "choic": [13, 19, 37, 45, 110, 111, 133, 168, 171, 272, 278, 296, 305, 370, 371, 394, 429, 432], "linear": [13, 14, 15, 16, 19, 20, 21, 22, 24, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 52, 53, 54, 55, 57, 58, 78, 92, 160, 161, 162, 166, 222, 272, 273, 274, 275, 278, 279, 280, 281, 283, 284, 285, 288, 289, 290, 292, 293, 294, 296, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 312, 313, 314, 315, 317, 318, 338, 351, 420, 421, 422, 426, 483], "layer": [13, 14, 15, 16, 19, 20, 21, 22, 24, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 52, 53, 54, 55, 57, 58, 91, 92, 93, 94, 98, 103, 106, 120, 160, 161, 162, 222, 272, 273, 274, 275, 278, 279, 280, 281, 283, 284, 285, 288, 289, 290, 292, 293, 294, 296, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 312, 313, 314, 315, 317, 318, 350, 351, 352, 353, 357, 362, 366, 380, 420, 421, 422, 483], "pool": [13, 15, 19, 21, 25, 30, 34, 37, 39, 42, 45, 47, 54, 57, 92, 97, 103, 108, 112, 113, 114, 160, 161, 222, 272, 274, 278, 280, 284, 289, 293, 296, 299, 302, 305, 307, 314, 317, 351, 356, 362, 368, 372, 373, 374, 420, 421, 483], "softmax": [13, 15, 17, 18, 19, 21, 23, 25, 27, 32, 34, 36, 37, 39, 42, 45, 47, 49, 54, 56, 57, 98, 100, 116, 183, 222, 272, 274, 276, 277, 278, 280, 282, 284, 286, 291, 293, 295, 296, 299, 302, 305, 307, 309, 314, 316, 317, 357, 359, 376, 443, 483], "rocstori": [13, 19, 37, 45, 272, 278, 296, 305], "swag": [13, 19, 37, 45, 272, 278, 296, 305], "spanclassifi": [13, 14, 19, 20, 24, 29, 33, 37, 38, 41, 45, 46, 52, 53, 272, 273, 278, 279, 283, 288, 292, 296, 298, 301, 305, 306, 312, 313], "document_quest": [13, 14, 19, 20, 24, 29, 33, 37, 38, 41, 45, 46, 51, 52, 53, 272, 273, 278, 279, 283, 288, 292, 296, 298, 301, 305, 306, 311, 312, 313], "document_context": [13, 14, 19, 20, 24, 29, 33, 37, 38, 41, 45, 46, 52, 53, 272, 273, 278, 279, 283, 288, 292, 296, 298, 301, 305, 306, 312, 313], "answer": [13, 14, 19, 20, 24, 29, 33, 37, 38, 41, 45, 46, 51, 52, 53, 65, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 84, 85, 86, 93, 94, 103, 120, 143, 163, 164, 166, 171, 179, 208, 210, 259, 272, 273, 278, 279, 283, 288, 292, 296, 298, 301, 305, 306, 311, 312, 313, 324, 327, 328, 330, 331, 332, 333, 334, 335, 336, 337, 343, 345, 346, 352, 353, 362, 380, 404, 423, 424, 426, 432, 440, 469, 471], "albert_base_uncased_multiple_choic": [13, 272], "allow": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 65, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 103, 109, 115, 139, 140, 144, 154, 155, 163, 178, 192, 193, 211, 221, 239, 247, 248, 250, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 309, 311, 312, 313, 314, 315, 316, 317, 318, 324, 327, 328, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 341, 362, 369, 375, 400, 401, 405, 415, 416, 423, 439, 453, 454, 472, 482, 501, 509, 510, 512], "faster": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 51, 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426, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 445, 448, 469, 483, 500], "case": [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 85, 86, 87, 91, 93, 94, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 118, 119, 120, 122, 127, 130, 134, 141, 144, 164, 167, 174, 183, 184, 187, 193, 199, 222, 253, 254, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 345, 346, 347, 349, 350, 352, 353, 354, 355, 357, 358, 360, 362, 364, 365, 366, 367, 368, 369, 370, 371, 373, 374, 378, 379, 380, 381, 388, 390, 395, 402, 424, 428, 435, 443, 445, 448, 454, 459, 483, 514, 515, 528], "512": [13, 15, 17, 18, 19, 21, 23, 25, 27, 30, 32, 34, 36, 37, 39, 42, 45, 47, 49, 51, 54, 56, 57, 88, 95, 101, 103, 106, 107, 108, 109, 113, 114, 170, 222, 272, 274, 276, 277, 278, 280, 282, 284, 286, 289, 291, 293, 295, 296, 299, 302, 305, 307, 309, 311, 314, 316, 317, 319, 348, 354, 360, 362, 366, 367, 368, 369, 373, 374, 431, 483], "question": [13, 14, 19, 20, 24, 29, 33, 37, 38, 41, 45, 46, 51, 52, 53, 65, 69, 70, 71, 72, 73, 74, 75, 76, 77, 84, 85, 93, 94, 103, 110, 111, 120, 143, 152, 163, 164, 166, 171, 179, 208, 259, 272, 273, 278, 279, 283, 288, 292, 296, 298, 301, 305, 306, 311, 312, 313, 324, 328, 330, 331, 332, 333, 334, 335, 336, 337, 343, 345, 352, 353, 362, 370, 371, 380, 404, 413, 423, 424, 426, 432, 440, 469], "context": [13, 14, 19, 20, 24, 29, 33, 37, 38, 41, 45, 46, 52, 53, 69, 70, 92, 93, 94, 100, 103, 109, 116, 120, 146, 158, 160, 161, 162, 167, 175, 177, 183, 193, 222, 238, 272, 273, 278, 279, 283, 288, 292, 296, 298, 301, 305, 306, 312, 313, 328, 330, 351, 352, 353, 359, 362, 369, 376, 380, 407, 419, 420, 421, 422, 428, 436, 438, 443, 454, 483], "questionansw": [13, 19, 20, 37, 45, 272, 278, 279, 296, 305], "eiffel": [13, 19, 37, 45, 272, 278, 296, 305], "tower": [13, 19, 37, 45, 272, 278, 296, 305], "countri": [13, 19, 37, 45, 183, 272, 278, 296, 305, 443], "germani": [13, 19, 37, 45, 272, 278, 296, 305], "franc": [13, 19, 27, 37, 45, 170, 272, 278, 286, 296, 305, 319, 431], "itali": [13, 19, 37, 45, 272, 278, 296, 305], "split": [13, 19, 37, 45, 66, 67, 77, 78, 79, 80, 86, 88, 92, 154, 155, 160, 161, 162, 170, 190, 191, 192, 196, 222, 238, 246, 272, 278, 296, 305, 319, 325, 326, 337, 338, 339, 340, 346, 348, 351, 415, 416, 420, 421, 422, 431, 450, 452, 453, 457, 483, 500, 508], "bert_base_uncased_multiple_choic": [13, 19, 37, 272, 278, 296], "span": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 163, 178, 179, 273, 279, 283, 288, 292, 298, 301, 306, 312, 313, 423, 439, 440], "squad": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 91, 93, 94, 98, 107, 110, 111, 163, 273, 279, 283, 288, 292, 298, 301, 306, 312, 313, 350, 352, 353, 357, 367, 370, 371, 423], "comput": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 67, 78, 80, 81, 91, 98, 99, 104, 115, 141, 166, 170, 171, 172, 181, 185, 196, 208, 273, 279, 283, 288, 292, 298, 301, 306, 312, 313, 319, 326, 338, 340, 341, 350, 357, 358, 364, 375, 402, 426, 431, 432, 433, 441, 446, 457, 469, 532], "logit": [14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 32, 33, 34, 36, 38, 39, 41, 42, 46, 47, 49, 52, 53, 54, 56, 57, 146, 160, 161, 162, 222, 273, 274, 276, 277, 279, 280, 282, 283, 284, 286, 288, 291, 292, 293, 295, 298, 299, 301, 302, 306, 307, 309, 312, 313, 314, 316, 317, 407, 420, 421, 422, 483], "albert_base_qa_squad2": [14, 273], "128": [14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 91, 93, 94, 96, 98, 99, 104, 110, 111, 118, 119, 120, 235, 273, 274, 275, 276, 277, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 298, 300, 301, 302, 303, 304, 306, 307, 308, 309, 312, 313, 314, 315, 316, 317, 318, 350, 352, 353, 355, 357, 358, 364, 370, 371, 378, 379, 380, 496, 524], "what": [14, 20, 24, 29, 33, 38, 41, 46, 50, 52, 53, 65, 67, 72, 73, 84, 85, 125, 127, 143, 146, 170, 172, 176, 183, 191, 254, 273, 279, 283, 288, 292, 298, 301, 306, 310, 312, 313, 319, 324, 326, 332, 333, 343, 345, 386, 388, 404, 407, 431, 433, 437, 443, 452, 515, 525], "": [14, 17, 18, 20, 23, 24, 27, 29, 32, 33, 36, 38, 41, 46, 49, 52, 53, 56, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 86, 87, 88, 91, 95, 96, 98, 99, 100, 101, 105, 109, 110, 111, 115, 116, 118, 119, 122, 125, 133, 140, 144, 146, 158, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 183, 184, 185, 191, 192, 193, 196, 198, 202, 208, 210, 219, 222, 227, 233, 235, 251, 261, 262, 273, 276, 277, 279, 282, 283, 286, 288, 291, 292, 295, 298, 301, 306, 309, 312, 313, 316, 319, 320, 324, 326, 327, 328, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 346, 347, 348, 349, 350, 354, 355, 357, 358, 359, 360, 365, 369, 370, 371, 376, 378, 379, 381, 386, 394, 401, 405, 407, 419, 422, 423, 424, 425, 426, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 443, 445, 446, 452, 453, 454, 457, 458, 462, 469, 471, 475, 476, 479, 483, 485, 488, 491, 495, 496, 511, 513, 524, 527, 528, 529, 532], "my": [14, 16, 17, 20, 22, 24, 28, 29, 31, 33, 35, 38, 40, 41, 42, 43, 46, 48, 50, 52, 53, 55, 58, 86, 87, 88, 133, 135, 143, 146, 154, 165, 166, 167, 171, 173, 174, 175, 177, 187, 190, 192, 210, 273, 275, 276, 279, 281, 283, 287, 288, 290, 292, 294, 298, 300, 301, 302, 303, 306, 308, 310, 312, 313, 315, 318, 346, 347, 348, 394, 396, 404, 407, 415, 425, 426, 428, 432, 434, 435, 436, 438, 448, 450, 453, 471, 528], "clara": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 143, 273, 279, 283, 288, 292, 298, 301, 306, 312, 313, 404], "live": [14, 16, 20, 22, 24, 29, 31, 33, 35, 38, 40, 41, 43, 46, 48, 52, 53, 55, 58, 143, 166, 167, 235, 273, 275, 279, 281, 283, 288, 290, 292, 294, 298, 300, 301, 303, 306, 308, 312, 313, 315, 318, 404, 426, 428, 496, 524], "berkelei": [14, 20, 24, 29, 33, 38, 41, 46, 52, 53, 173, 174, 175, 177, 273, 279, 283, 288, 292, 298, 301, 306, 312, 313, 434, 435, 436, 438], "instead": [14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 42, 46, 47, 49, 53, 54, 56, 57, 94, 125, 127, 131, 132, 179, 201, 202, 207, 208, 238, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 353, 386, 388, 391, 393, 440, 461, 462, 467, 469, 532], "per": [14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 42, 43, 44, 46, 47, 48, 49, 50, 53, 54, 55, 56, 57, 58, 81, 95, 101, 109, 118, 119, 127, 131, 135, 137, 138, 139, 140, 141, 146, 149, 196, 208, 210, 227, 238, 241, 246, 253, 274, 275, 276, 277, 280, 281, 282, 284, 285, 286, 287, 289, 290, 291, 293, 294, 295, 299, 300, 302, 303, 304, 307, 308, 309, 310, 314, 315, 316, 317, 318, 341, 354, 360, 369, 378, 379, 388, 391, 396, 398, 399, 400, 401, 407, 457, 469, 471, 488, 500, 503, 508, 514], "averag": [14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 42, 46, 47, 49, 51, 53, 54, 56, 57, 95, 97, 101, 104, 109, 112, 118, 119, 127, 140, 152, 181, 222, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 311, 314, 316, 317, 354, 356, 360, 364, 369, 372, 378, 379, 388, 401, 413, 441, 483], "regress": [15, 21, 25, 30, 34, 39, 42, 47, 54, 57, 163, 179, 274, 280, 284, 289, 293, 299, 302, 307, 314, 317, 423, 440], "multi": [15, 21, 25, 28, 30, 34, 39, 42, 44, 47, 50, 54, 57, 91, 113, 115, 118, 119, 125, 127, 170, 222, 274, 280, 284, 287, 289, 293, 299, 302, 304, 307, 310, 314, 317, 319, 350, 373, 375, 378, 379, 386, 388, 431, 483], "sequenceclassifi": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317], "albert_base_sequence_classifier_imdb": [15, 274], "activ": [15, 17, 18, 21, 23, 25, 27, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 122, 125, 177, 207, 214, 222, 261, 274, 276, 277, 280, 282, 284, 286, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 381, 386, 438, 467, 483], "calcul": [15, 17, 18, 21, 23, 25, 27, 28, 32, 34, 36, 39, 42, 44, 47, 49, 50, 54, 56, 57, 81, 117, 135, 140, 150, 157, 181, 222, 274, 276, 277, 280, 282, 284, 286, 287, 291, 293, 295, 299, 302, 304, 307, 309, 310, 314, 316, 317, 341, 377, 396, 401, 410, 418, 441, 483], "sigmoid": [15, 17, 18, 21, 23, 25, 27, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 222, 274, 276, 277, 280, 282, 284, 286, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 483], "love": [15, 18, 21, 23, 28, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 94, 111, 119, 155, 158, 274, 277, 280, 282, 287, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 353, 371, 379, 416, 419, 528], "movi": [15, 18, 21, 23, 28, 30, 32, 34, 36, 39, 47, 49, 50, 54, 56, 57, 158, 165, 274, 277, 280, 282, 287, 289, 291, 293, 295, 299, 307, 309, 310, 314, 316, 317, 419, 425, 528], "child": [15, 18, 21, 23, 30, 32, 34, 36, 39, 47, 49, 54, 56, 57, 274, 277, 280, 282, 289, 291, 293, 295, 299, 307, 309, 314, 316, 317], "pretti": [15, 18, 21, 23, 30, 32, 34, 36, 39, 44, 47, 49, 54, 56, 57, 125, 274, 277, 280, 282, 289, 291, 293, 295, 299, 304, 307, 309, 314, 316, 317, 386], "bore": [15, 18, 21, 23, 30, 32, 34, 36, 39, 47, 49, 54, 56, 57, 274, 277, 280, 282, 289, 291, 293, 295, 299, 307, 309, 314, 316, 317], "neg": [15, 18, 21, 23, 25, 30, 32, 34, 36, 39, 47, 49, 50, 54, 56, 57, 113, 157, 158, 181, 222, 235, 274, 277, 280, 282, 284, 289, 291, 293, 295, 299, 307, 309, 310, 314, 316, 317, 373, 418, 419, 441, 483, 496, 524], "limit": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 67, 69, 78, 80, 88, 91, 96, 105, 107, 114, 117, 120, 125, 158, 179, 184, 203, 222, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 326, 328, 338, 340, 348, 350, 355, 365, 367, 374, 377, 380, 386, 440, 445, 463, 483], "almost": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317], "help": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 69, 76, 84, 85, 86, 91, 92, 146, 147, 160, 161, 162, 164, 167, 168, 170, 174, 176, 193, 210, 222, 235, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 319, 328, 336, 343, 345, 346, 350, 351, 407, 408, 420, 421, 422, 424, 428, 429, 431, 435, 437, 454, 471, 483, 496, 524, 529, 533], "feed": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317], "entir": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 47, 49, 54, 56, 57, 67, 95, 108, 113, 114, 170, 175, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 307, 309, 314, 316, 317, 319, 326, 354, 368, 373, 374, 431, 436], "one": [15, 17, 18, 21, 23, 25, 27, 30, 32, 34, 36, 39, 42, 44, 47, 49, 54, 56, 57, 63, 67, 78, 79, 80, 81, 84, 85, 87, 93, 94, 102, 107, 112, 118, 119, 120, 125, 127, 131, 133, 138, 141, 146, 155, 162, 175, 184, 190, 210, 213, 222, 237, 238, 251, 274, 276, 277, 280, 282, 284, 286, 289, 291, 293, 295, 299, 302, 304, 307, 309, 314, 316, 317, 323, 326, 338, 339, 340, 341, 343, 345, 347, 352, 353, 361, 367, 372, 378, 379, 380, 386, 388, 391, 394, 399, 402, 407, 416, 422, 436, 445, 450, 471, 474, 483, 499, 500, 513, 524, 528], "token_classifi": [16, 26, 40, 43, 48, 55, 58, 275, 285, 300, 303, 308, 315, 318], "albert_base_token_classifier_conll03": [16, 275], "tokenclassifi": [16, 22, 26, 31, 35, 40, 43, 48, 55, 58, 275, 281, 285, 290, 294, 300, 303, 308, 315, 318], "john": [16, 22, 31, 35, 40, 43, 48, 55, 58, 63, 94, 111, 119, 122, 123, 142, 144, 155, 204, 251, 257, 275, 281, 290, 294, 300, 303, 308, 315, 318, 323, 353, 371, 379, 381, 383, 403, 405, 416, 464, 513, 519], "lenon": [16, 22, 31, 35, 40, 43, 48, 55, 58, 275, 281, 290, 294, 300, 303, 308, 315, 318], "born": [16, 22, 31, 35, 40, 43, 48, 55, 58, 166, 167, 275, 281, 290, 294, 300, 303, 308, 315, 318, 426, 428], "london": [16, 22, 31, 35, 40, 43, 48, 55, 58, 275, 281, 290, 294, 300, 303, 308, 315, 318], "pari": [16, 22, 31, 35, 40, 43, 48, 55, 58, 143, 275, 281, 290, 294, 300, 303, 308, 315, 318, 404], "sarah": [16, 22, 31, 35, 40, 43, 48, 55, 58, 275, 281, 290, 294, 300, 303, 308, 315, 318], "o": [16, 22, 26, 31, 35, 40, 43, 48, 55, 58, 138, 139, 140, 142, 208, 227, 241, 253, 256, 275, 281, 285, 290, 294, 300, 303, 308, 315, 318, 399, 400, 401, 403, 469, 488, 503, 514, 518, 534], "modelforsequenceclassif": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "nli": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "natur": [17, 18, 23, 27, 32, 36, 49, 50, 56, 80, 91, 92, 93, 94, 96, 98, 99, 100, 107, 115, 116, 120, 127, 163, 166, 171, 177, 179, 199, 209, 213, 276, 277, 282, 286, 291, 295, 309, 310, 316, 340, 350, 351, 352, 353, 355, 357, 358, 359, 367, 375, 376, 380, 388, 423, 426, 432, 438, 440, 459, 470, 474], "don": [17, 18, 23, 27, 32, 36, 49, 56, 99, 110, 144, 222, 276, 277, 282, 286, 291, 295, 309, 316, 358, 370, 405, 483], "hardcod": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "potenti": [17, 18, 23, 27, 32, 36, 49, 56, 78, 165, 169, 173, 175, 183, 276, 277, 282, 286, 291, 295, 309, 316, 338, 425, 430, 434, 436, 443], "thei": [17, 18, 23, 27, 32, 36, 49, 51, 56, 84, 85, 86, 140, 144, 146, 162, 166, 211, 254, 276, 277, 282, 286, 291, 295, 309, 311, 316, 343, 345, 346, 401, 405, 407, 422, 426, 472, 491, 511, 515, 528], "chosen": [17, 18, 23, 27, 32, 36, 49, 56, 84, 85, 140, 146, 276, 277, 282, 286, 291, 295, 309, 316, 343, 345, 401, 407], "runtim": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "usual": [17, 18, 23, 27, 32, 36, 49, 56, 80, 213, 242, 276, 277, 282, 286, 291, 295, 309, 316, 340, 474, 504], "slower": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "flexibl": [17, 18, 23, 27, 32, 36, 49, 56, 66, 71, 78, 250, 276, 277, 282, 286, 291, 295, 309, 316, 325, 331, 338, 512], "loop": [17, 18, 23, 36, 49, 56, 210, 276, 277, 282, 295, 309, 316, 471], "through": [17, 18, 23, 36, 49, 56, 91, 107, 123, 125, 135, 169, 172, 174, 204, 276, 277, 282, 295, 309, 316, 350, 367, 383, 386, 430, 433, 435, 464, 529], "longer": [17, 18, 23, 36, 49, 56, 91, 100, 105, 116, 127, 167, 170, 171, 276, 277, 282, 295, 309, 316, 319, 350, 359, 365, 376, 388, 428, 431, 432, 533], "pass": [17, 18, 23, 27, 32, 36, 49, 56, 146, 210, 276, 277, 282, 286, 291, 295, 309, 316, 349, 407, 471], "pose": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "premis": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "hypothesi": [17, 18, 23, 27, 32, 36, 49, 56, 276, 277, 282, 286, 291, 295, 309, 316], "pair": [17, 18, 23, 27, 32, 36, 49, 56, 66, 101, 102, 109, 113, 123, 169, 235, 276, 277, 282, 286, 291, 295, 309, 316, 325, 360, 361, 369, 373, 383, 430, 496], "albert_base_zero_shot_classifier_onnx": [17, 276], "iphon": [17, 276], "resolv": [17, 276], "asap": [17, 276], "urgent": [17, 276], "albert_zero_shot_classifier_onnx": [17, 276], "bart": [17, 18, 163, 276, 277, 423], "bartforsequenceclassif": [18, 277], "bart_large_zero_shot_classifier_mnli": [18, 277], "bert_base_cased_qa_squad2": [20, 279], "bert_base_sequence_classifier_imdb": [21, 280], "bert_base_token_classifier_conll03": [22, 281], "bert_zero_shot_classifier_mnli": [23, 282], "camembert": [24, 25, 26, 96, 283, 284, 285, 355], "camembert_base_qa_fquad": [24, 283], "fr": [24, 25, 27, 96, 127, 169, 172, 283, 284, 286, 355, 388, 430, 433], "sequence_classifi": [25, 284], "camembert_base_sequence_classifier_allocin": [25, 284], "j": 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"sparknlp.partition.partition_properties.HasReaderProperties"]], "hastextreaderproperties (class in sparknlp.partition.partition_properties)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties"]], "addattachmentcontent (hasemailreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasEmailReaderProperties.addAttachmentContent"], [500, "sparknlp.partition.partition_properties.HasEmailReaderProperties.addAttachmentContent"]], "appendcells (hasexcelreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasExcelReaderProperties.appendCells"], [500, "sparknlp.partition.partition_properties.HasExcelReaderProperties.appendCells"]], "cellseparator (hasexcelreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasExcelReaderProperties.cellSeparator"], [500, "sparknlp.partition.partition_properties.HasExcelReaderProperties.cellSeparator"]], "chunkingstrategy (haschunkerproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.chunkingStrategy"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.chunkingStrategy"]], "combinetextundernchars (haschunkerproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.combineTextUnderNChars"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.combineTextUnderNChars"]], "contentpath (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.contentPath"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.contentPath"]], "contenttype (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.contentType"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.contentType"]], 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"sparknlp.partition.partition_properties.HasTextReaderProperties.getMaxLineCount"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.getMaxLineCount"]], "getparagraphsplit() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.getParagraphSplit"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.getParagraphSplit"]], "getshortlinewordthreshold() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.getShortLineWordThreshold"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.getShortLineWordThreshold"]], "getthreshold() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.getThreshold"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.getThreshold"]], "gettimeout() (hashtmlreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.getTimeout"], [500, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.getTimeout"]], "gettitlelengthsize() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.getTitleLengthSize"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.getTitleLengthSize"]], "groupbrokenparagraphs (hastextreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.groupBrokenParagraphs"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.groupBrokenParagraphs"]], "ignoreexceptions (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.ignoreExceptions"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.ignoreExceptions"]], "includepagebreaks (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.includePageBreaks"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.includePageBreaks"]], "includeslidenotes (haspowerpointproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPowerPointProperties.includeSlideNotes"], [500, "sparknlp.partition.partition_properties.HasPowerPointProperties.includeSlideNotes"]], "infertablestructure (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.inferTableStructure"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.inferTableStructure"]], "inputcol (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.inputCol"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.inputCol"]], "maxcharacters (haschunkerproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.maxCharacters"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.maxCharacters"]], "maxlinecount (hastextreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.maxLineCount"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.maxLineCount"]], "newafternchars (haschunkerproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.newAfterNChars"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.newAfterNChars"]], "normalizeligatures (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.normalizeLigatures"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.normalizeLigatures"]], "onlypagenum (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.onlyPageNum"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.onlyPageNum"]], "origincol (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.originCol"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.originCol"]], "outputasdocument (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.outputAsDocument"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.outputAsDocument"]], "outputcol (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.outputCol"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.outputCol"]], "outputformat (hashtmlreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.outputFormat"], [500, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.outputFormat"]], "overlap (haschunkerproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.overlap"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.overlap"]], "overlapall (haschunkerproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.overlapAll"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.overlapAll"]], "pagenumcol (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.pageNumCol"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.pageNumCol"]], "paragraphsplit (hastextreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.paragraphSplit"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.paragraphSplit"]], "partitionnum (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.partitionNum"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.partitionNum"]], "readasimage (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.readAsImage"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.readAsImage"]], "setaddattachmentcontent() (hasemailreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasEmailReaderProperties.setAddAttachmentContent"], [500, "sparknlp.partition.partition_properties.HasEmailReaderProperties.setAddAttachmentContent"]], "setappendcells() (hasexcelreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setAppendCells"], [500, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setAppendCells"]], "setcellseparator() (hasexcelreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setCellSeparator"], [500, "sparknlp.partition.partition_properties.HasExcelReaderProperties.setCellSeparator"]], "setchunkingstrategy() (haschunkerproperties method)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.setChunkingStrategy"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.setChunkingStrategy"]], "setcombinetextundernchars() (haschunkerproperties method)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.setCombineTextUnderNChars"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.setCombineTextUnderNChars"]], "setcontentpath() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setContentPath"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setContentPath"]], "setcontenttype() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setContentType"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setContentType"]], "setexplodedocs() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setExplodeDocs"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setExplodeDocs"]], "setextractcoordinates() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setExtractCoordinates"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setExtractCoordinates"]], "setflattenoutput() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setFlattenOutput"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setFlattenOutput"]], "setgroupbrokenparagraphs() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.setGroupBrokenParagraphs"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.setGroupBrokenParagraphs"]], "setheaders() (hashtmlreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.setHeaders"], [500, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.setHeaders"]], "setignoreexceptions() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setIgnoreExceptions"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setIgnoreExceptions"]], "setincludepagebreaks() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setIncludePageBreaks"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setIncludePageBreaks"]], "setincludeslidenotes() (haspowerpointproperties method)": [[238, "sparknlp.partition.partition_properties.HasPowerPointProperties.setIncludeSlideNotes"], [500, "sparknlp.partition.partition_properties.HasPowerPointProperties.setIncludeSlideNotes"]], "setinfertablestructure() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setInferTableStructure"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setInferTableStructure"]], "setinputcol() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setInputCol"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setInputCol"]], "setmaxcharacters() (haschunkerproperties method)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.setMaxCharacters"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.setMaxCharacters"]], "setmaxlinecount() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.setMaxLineCount"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.setMaxLineCount"]], "setnewafternchars() (haschunkerproperties method)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.setNewAfterNChars"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.setNewAfterNChars"]], "setnormalizeligatures() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setNormalizeLigatures"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setNormalizeLigatures"]], "setonlypagenum() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setOnlyPageNum"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setOnlyPageNum"]], "setorigincol() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setOriginCol"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setOriginCol"]], "setoutputasdocument() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setOutputAsDocument"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setOutputAsDocument"]], "setoutputcol() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setOutputCol"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setOutputCol"]], "setoutputformat() (hashtmlreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.setOutputFormat"], [500, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.setOutputFormat"]], "setoverlap() (haschunkerproperties method)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.setOverlap"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.setOverlap"]], "setoverlapall() (haschunkerproperties method)": [[238, "sparknlp.partition.partition_properties.HasChunkerProperties.setOverlapAll"], [500, "sparknlp.partition.partition_properties.HasChunkerProperties.setOverlapAll"]], "setpagenumcol() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setPageNumCol"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setPageNumCol"]], "setparagraphsplit() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.setParagraphSplit"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.setParagraphSplit"]], "setpartitionnum() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setPartitionNum"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setPartitionNum"]], "setreadasimage() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setReadAsImage"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setReadAsImage"]], "setshortlinewordthreshold() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.setShortLineWordThreshold"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.setShortLineWordThreshold"]], "setsort() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setSort"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setSort"]], "setsplitpage() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setSplitPage"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setSplitPage"]], "setstorecontent() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setStoreContent"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setStoreContent"]], "setstoresplittedpdf() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setStoreSplittedPdf"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setStoreSplittedPdf"]], "settextstripper() (haspdfproperties method)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.setTextStripper"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.setTextStripper"]], "setthreshold() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.setThreshold"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.setThreshold"]], "settimeout() (hashtmlreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.setTimeout"], [500, "sparknlp.partition.partition_properties.HasHTMLReaderProperties.setTimeout"]], "settitlefontsize() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setTitleFontSize"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setTitleFontSize"]], "settitlelengthsize() (hastextreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.setTitleLengthSize"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.setTitleLengthSize"]], "settitlethreshold() (hasreaderproperties method)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.setTitleThreshold"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.setTitleThreshold"]], "shortlinewordthreshold (hastextreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasTextReaderProperties.shortLineWordThreshold"], [500, "sparknlp.partition.partition_properties.HasTextReaderProperties.shortLineWordThreshold"]], "sort (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.sort"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.sort"]], "sparknlp.partition.partition_properties": [[238, "module-sparknlp.partition.partition_properties"], [500, "module-sparknlp.partition.partition_properties"]], "splitpage (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.splitPage"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.splitPage"]], "storecontent (hasreaderproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasReaderProperties.storeContent"], [500, "sparknlp.partition.partition_properties.HasReaderProperties.storeContent"]], "storesplittedpdf (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.storeSplittedPdf"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.storeSplittedPdf"]], "textstripper (haspdfproperties attribute)": [[238, "sparknlp.partition.partition_properties.HasPdfProperties.textStripper"], [500, "sparknlp.partition.partition_properties.HasPdfProperties.textStripper"]], "threshold (hastextreaderproperties attribute)": [[238, 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(partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.getContentType"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.getContentType"]], "getincludepagebreaks() (partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.getIncludePageBreaks"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.getIncludePageBreaks"]], "getinfertablestructure() (partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.getInferTableStructure"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.getInferTableStructure"]], "getstorecontent() (partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.getStoreContent"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.getStoreContent"]], "gettitlefontsize() (partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.getTitleFontSize"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.getTitleFontSize"]], "includepagebreaks (partitiontransformer attribute)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.includePageBreaks"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.includePageBreaks"]], "infertablestructure (partitiontransformer attribute)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.inferTableStructure"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.inferTableStructure"]], "inputannotatortypes (partitiontransformer attribute)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.inputAnnotatorTypes"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.inputAnnotatorTypes"]], "name (partitiontransformer attribute)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.name"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.name"]], "outputannotatortype (partitiontransformer attribute)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.outputAnnotatorType"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.outputAnnotatorType"]], "setcontentpath() (partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.setContentPath"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.setContentPath"]], "setcontenttype() (partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.setContentType"], [501, "sparknlp.partition.partition_transformer.PartitionTransformer.setContentType"]], "setincludepagebreaks() (partitiontransformer method)": [[239, "sparknlp.partition.partition_transformer.PartitionTransformer.setIncludePageBreaks"], [501, 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"sparknlp.annotator.parser"]], "pos (in module sparknlp.annotator)": [[384, "sparknlp.annotator.pos"]], "regex (in module sparknlp.annotator)": [[384, "sparknlp.annotator.regex"]], "sbd (in module sparknlp.annotator)": [[384, "sparknlp.annotator.sbd"]], "sda (in module sparknlp.annotator)": [[384, "sparknlp.annotator.sda"]], "sentence_detector_dl (in module sparknlp.annotator)": [[384, "sparknlp.annotator.sentence_detector_dl"]], "seq2seq (in module sparknlp.annotator)": [[384, "sparknlp.annotator.seq2seq"]], "spell (in module sparknlp.annotator)": [[384, "sparknlp.annotator.spell"]], "ws (in module sparknlp.annotator)": [[384, "sparknlp.annotator.ws"]], "dictionary_path (norvigsweetingapproach attribute)": [[445, "sparknlp.annotator.spell_check.norvig_sweeting.NorvigSweetingApproach.dictionary_path"]], "dictionary_path (symmetricdeleteapproach attribute)": [[446, "sparknlp.annotator.spell_check.symmetric_delete.SymmetricDeleteApproach.dictionary_path"]], "parse_embeddings (lightpipeline attribute)": [[469, "sparknlp.base.light_pipeline.LightPipeline.parse_embeddings"]], "pipeline_model (lightpipeline attribute)": [[469, "sparknlp.base.light_pipeline.LightPipeline.pipeline_model"]], "stages (recursivepipelinemodel attribute)": [[472, "sparknlp.base.recursive_pipeline.RecursivePipelineModel.stages"]], "covered (coverageresult attribute)": [[480, "sparknlp.common.coverage_result.CoverageResult.covered"]], "percentage (coverageresult attribute)": [[480, "sparknlp.common.coverage_result.CoverageResult.percentage"]], "total (coverageresult attribute)": [[480, "sparknlp.common.coverage_result.CoverageResult.total"]], "annotators (in module sparknlp)": [[489, "sparknlp.annotators"]], "embeddings (in module sparknlp)": [[489, "sparknlp.embeddings"]], "java_obj (extendedjavawrapper attribute)": [[492, "sparknlp.internal.extended_java_wrapper.ExtendedJavaWrapper.java_obj"]], "sc (extendedjavawrapper attribute)": [[492, "sparknlp.internal.extended_java_wrapper.ExtendedJavaWrapper.sc"]], "comet_ml (in module sparknlp.logging.comet)": [[496, "sparknlp.logging.comet.comet_ml"]], "comet_mode (cometlogger attribute)": [[496, "sparknlp.logging.comet.CometLogger.comet_mode"]], "experiment (cometlogger attribute)": [[496, "sparknlp.logging.comet.CometLogger.experiment"]], "experiment_id (cometlogger attribute)": [[496, "sparknlp.logging.comet.CometLogger.experiment_id"]], "experiment_kwargs (cometlogger attribute)": [[496, "sparknlp.logging.comet.CometLogger.experiment_kwargs"]], "project_name (cometlogger attribute)": [[496, "sparknlp.logging.comet.CometLogger.project_name"]], "thread (cometlogger attribute)": [[496, "sparknlp.logging.comet.CometLogger.thread"]], "workspace (cometlogger attribute)": [[496, "sparknlp.logging.comet.CometLogger.workspace"]], "spark (partition attribute)": [[499, "sparknlp.partition.partition.Partition.spark"]], "light_model (pretrainedpipeline attribute)": [[503, 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documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/third_party/index.html b/docs/api/python/third_party/index.html index b1a5788c692a4a..d7c12ce093ad51 100644 --- a/docs/api/python/third_party/index.html +++ b/docs/api/python/third_party/index.html @@ -8,7 +8,7 @@ - Third Party Projects — Spark NLP 6.1.5 documentation + Third Party Projects — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -124,8 +124,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/user_guide/annotation.html b/docs/api/python/user_guide/annotation.html index f407c6e4903d5b..24fb8aac3e4de7 100644 --- a/docs/api/python/user_guide/annotation.html +++ b/docs/api/python/user_guide/annotation.html @@ -8,7 +8,7 @@ - Annotation — Spark NLP 6.1.5 documentation + Annotation — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -124,8 +124,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/user_guide/annotators.html b/docs/api/python/user_guide/annotators.html index 6e188565ef93a5..f231cb3aa3c364 100644 --- a/docs/api/python/user_guide/annotators.html +++ b/docs/api/python/user_guide/annotators.html @@ -8,7 +8,7 @@ - Annotators — Spark NLP 6.1.5 documentation + Annotators — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -124,8 +124,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/user_guide/custom_pipelines.html b/docs/api/python/user_guide/custom_pipelines.html index dad4237fcf2a36..f9c9431c2a1fe1 100644 --- a/docs/api/python/user_guide/custom_pipelines.html +++ b/docs/api/python/user_guide/custom_pipelines.html @@ -8,7 +8,7 @@ - Setting up your own pipeline — Spark NLP 6.1.5 documentation + Setting up your own pipeline — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -124,8 +124,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/user_guide/helpers.html b/docs/api/python/user_guide/helpers.html index 1d16f5b4676772..4938f208ca517e 100644 --- a/docs/api/python/user_guide/helpers.html +++ b/docs/api/python/user_guide/helpers.html @@ -8,7 +8,7 @@ - Helper Functions — Spark NLP 6.1.5 documentation + Helper Functions — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -124,8 +124,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/python/user_guide/index.html b/docs/api/python/user_guide/index.html index 564e0c685ca8a7..fdf24186ed5c49 100644 --- a/docs/api/python/user_guide/index.html +++ b/docs/api/python/user_guide/index.html @@ -8,7 +8,7 @@ - User Guide — Spark NLP 6.1.5 documentation + User Guide — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -124,8 +124,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git 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6.2.0 documentation - Home + diff --git a/docs/api/python/user_guide/training.html b/docs/api/python/user_guide/training.html index 398cdf025ab68a..df34ffae6cbf31 100644 --- a/docs/api/python/user_guide/training.html +++ b/docs/api/python/user_guide/training.html @@ -8,7 +8,7 @@ - Loading datasets for training — Spark NLP 6.1.5 documentation + Loading datasets for training — Spark NLP 6.2.0 documentation @@ -37,7 +37,7 @@ - + @@ -124,8 +124,8 @@ - Spark NLP 6.1.5 documentation - Home - + Spark NLP 6.2.0 documentation - Home + diff --git a/docs/api/scala/collection/compat/index.html b/docs/api/scala/collection/compat/index.html index df94edc0045338..c79a3d6abfddf6 100644 --- a/docs/api/scala/collection/compat/index.html +++ b/docs/api/scala/collection/compat/index.html @@ -3,9 +3,9 @@ - Spark NLP 6.1.5 ScalaDoc - scala.collection.compat - - + Spark NLP 6.2.0 ScalaDoc - scala.collection.compat + + @@ -28,7 +28,7 @@