This repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity.
Semantic Similarity with BERT.
Motivation: Semantic Similarity determines how similar two sentences are, in terms of their meaning. In this tutorial, we can fine-tune BERT model and use it to predict the similarity score for two sentences.
This example demonstrates the use of the Stanford Natural Language Inference (SNLI) Corpus to predict semantic sentence similarity with Transformers.
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Total train samples: 100000
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Total validation samples: 10000
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Total test samples: 10000
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Contradiction: The sentences share no similarity.
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Entailment: The sentences have a similar meaning.
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Neutral: The sentences are neutral.
