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b/archived/notebooks/time_series_forecasting/amazon_forecast_pipeline/sm_pipeline_with_amazon_forecast.ipynb similarity index 100% rename from sagemaker-pipelines/time_series_forecasting/amazon_forecast_pipeline/sm_pipeline_with_amazon_forecast.ipynb rename to archived/notebooks/time_series_forecasting/amazon_forecast_pipeline/sm_pipeline_with_amazon_forecast.ipynb diff --git a/sagemaker-pipelines/time_series_forecasting/amazon_forecast_pipeline/train.py b/archived/notebooks/time_series_forecasting/amazon_forecast_pipeline/train.py similarity index 100% rename from sagemaker-pipelines/time_series_forecasting/amazon_forecast_pipeline/train.py rename to archived/notebooks/time_series_forecasting/amazon_forecast_pipeline/train.py diff --git a/introduction_to_amazon_algorithms/jumpstart_sentence_pair_classification/README.md b/introduction_to_amazon_algorithms/jumpstart_sentence_pair_classification/README.md deleted file mode 100644 index 890270db19..0000000000 --- a/introduction_to_amazon_algorithms/jumpstart_sentence_pair_classification/README.md +++ /dev/null @@ -1,2 +0,0 @@ -### SageMaker JumpStart Sentence Pair Classification Training & Deployment -This notebook `Amazon_JumpStart_Sentence_Pair_Classification.ipynb` for Sentence Pair Classification task demonstrates (a) how to use a pre-trained model available in JumpStart for inference, and (b) how to fine-tune a pre-trained Transformer model on a custom dataset using JumpStart transfer learning algorithm, using AMT (Automatic Model Tuning) to search for the best hyperparameters, and how to use the fine-tuned model for inference. \ No newline at end of file