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finetune-deploy-bert-with-amazon-sagemaker-for-hugging-face's Introduction

Finetuning Hugging Face DistilBERT with Amazon Reviews Polarity dataset.

In this demo, we will use the Hugging Faces transformers and datasets library with Amazon SageMaker to fine-tune a pre-trained transformer on binary text classification. In particular, we will use the pre-trained DistilBERT model with the Amazon Reviews Polarity dataset. We will then deploy the resulting model for inference using SageMaker Endpoint.

We'll be using an offshoot of BERT called DistilBERT that is smaller, and so faster and cheaper for both training and inference. A pre-trained model is available in the transformers library from Hugging Face.

The Amazon Reviews Polarity dataset consists of reviews from Amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. It's avalaible under the amazon_polarity dataset on Hugging Face.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.

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finetune-deploy-bert-with-amazon-sagemaker-for-hugging-face's Issues

Unable to complete the training Job

I am trying to run the notebook as it is and getting error mentioned below,

Traceback (most recent call last): File "train.py", line 45, in train_dataset = load_from_disk(args.training_dir) File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 797, in load_from_disk return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 665, in load_from_disk dataset_info = DatasetInfo.from_dict(json.load(dataset_info_file)) File "/opt/conda/lib/python3.6/site-packages/datasets/info.py", line 225, in from_dict return cls({k: v for k, v in dataset_info_dict.items() if k in field_names}) File "", line 18, in init File "/opt/conda/lib/python3.6/site-packages/datasets/info.py", line 137, in post_init self.features = Features.from_dict(self.features) File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 947, in from_dict obj = generate_from_dict(dic) File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 895, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 895, in return {key: generate_from_dict(value) for key, value in obj.items()} File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 899, in generate_from_dict return Sequence(feature=generate_from_dict(obj["feature"]), length=obj["length"]) | Traceback (most recent call last): File "train.py", line 45, in train_dataset = load_from_disk(args.training_dir) File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 797, in load_from_disk return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 665, in load_from_disk dataset_info = DatasetInfo.from_dict(json.load(dataset_info_file)) File "/opt/conda/lib/python3.6/site-packages/datasets/info.py", line 225, in from_dict return cls({k: v for k, v in dataset_info_dict.items() if k in field_names}) File "", line 18, in init File "/opt/conda/lib/python3.6/site-packages/datasets/info.py", line 137, in post_init self.features = Features.from_dict(self.features) File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 947, in from_dict obj = generate_from_dict(dic) File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 895, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 895, in return {key: generate_from_dict(value) for key, value in obj.items()} File "/opt/conda/lib/python3.6/site-packages/datasets/features.py", line 899, in generate_from_dict return Sequence(feature=generate_from_dict(obj["feature"]), length=obj["length"])

KeyError: 'length'

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