BERT Base Uncased

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This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

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This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub . It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of sentences. The model available for deployment is created by attaching a binary classification layer to the output of the Text Embedding model, and then fine-tuning the entire model on QNLI dataset. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

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This is a Sentence Pair Classification from PyTorch Hub: https://pytorch.org/hub/huggingface_pytorch-transformers/

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BERT Base Uncased

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Amazon SageMaker model

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Version release notes

This GPU version supports model run on GPU instance types

Additional details

Inputs

Summary: The input is a pair of sentences.

Input MIME type: application/list-text

Real-time inference sample input data

https://github.com/aws-samples/aws-marketplace-machine-learning/blob/master/using_open_source_model_packages/pytorch_spc_model/input_example

Batch transform sample input data

https://github.com/aws-samples/aws-marketplace-machine-learning/blob/master/using_open_source_model_packages/pytorch_spc_model/output_example

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PyTorch Hub link

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