NER - pl example

examples/ner/README.md

@@ -112,6 +112,13 @@ Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) a
 | `roberta-large`                  | 95.96 | 91.87
 | `distilbert-base-uncased` | 94.34 | 90.32
 
+#### Run PyTorch version using PyTorch-Lightning
+
+Run `bash run_pl.sh` from the `ner` directory. This would also install `pytorch-lightning` and the `examples/requirements.txt`. It is a shell pipeline which would automatically download, pre-process the data and run the models in `germeval-model` directory. Logs are saved in `lightning_logs` directory.
+
+Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: `TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}`
+
+
 ### Run the Tensorflow 2 version
 
 To start training, just run:

examples/ner/run_pl.sh

@@ -1,6 +1,9 @@
+#!/usr/bin/env bash
+
 # Install newest ptl.
 pip install -U git+http://github.com/PyTorchLightning/pytorch-lightning/
-
+# for seqeval metrics import
+pip install -r ../requirements.txt
 
 curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-train.tsv?attredirects=0&d=1' \
 | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
@@ -15,12 +18,15 @@ python3 preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
 python3 preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
 python3 preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
 cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
-export OUTPUT_DIR=germeval-model
 export BATCH_SIZE=32
 export NUM_EPOCHS=3
-export SAVE_STEPS=750
 export SEED=1
 
+export OUTPUT_DIR_NAME=germeval-model
+export CURRENT_DIR=${PWD}
+export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
+mkdir -p $OUTPUT_DIR
+
 python3 run_pl_ner.py --data_dir ./ \
 --model_type bert \
 --labels ./labels.txt \
@@ -29,7 +35,6 @@ python3 run_pl_ner.py --data_dir ./ \
 --max_seq_length  $MAX_LENGTH \
 --num_train_epochs $NUM_EPOCHS \
 --train_batch_size 32 \
---save_steps $SAVE_STEPS \
 --seed $SEED \
 --do_train \
---do_predict
+--do_predict

examples/ner/run_pl_ner.py

@@ -141,10 +141,14 @@ def _eval_end(self, outputs):
         return ret, preds_list, out_label_list
 
     def validation_end(self, outputs):
+        # todo: update to validation_epoch_end instead of deprecated validation_end
+        # when stable
         ret, preds, targets = self._eval_end(outputs)
-        return ret
+        logs = ret["log"]
+        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
-    def test_end(self, outputs):
+    def test_epoch_end(self, outputs):
+        # updating to test_epoch_end instead of deprecated test_end
         ret, predictions, targets = self._eval_end(outputs)
 
         if self.is_logger():
@@ -172,7 +176,12 @@ def test_end(self, outputs):
                             logger.warning(
                                 "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
                             )
-        return ret
+        # Converting to the dic required by pl
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
+        # pytorch_lightning/trainer/logging.py#L139
+        logs = ret["log"]
+        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
+        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
     @staticmethod
     def add_model_specific_args(parser, root_dir):
@@ -217,6 +226,10 @@ def add_model_specific_args(parser, root_dir):
     trainer = generic_train(model, args)
 
     if args.do_predict:
-        checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpoint_*.ckpt", recursive=True)))
+        # See https://github.com/huggingface/transformers/issues/3159
+        # pl use this format to create a checkpoint:
+        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
+        # /pytorch_lightning/callbacks/model_checkpoint.py#L169
+        checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpointepoch=*.ckpt", recursive=True)))
         NERTransformer.load_from_checkpoint(checkpoints[-1])
         trainer.test(model)