Bert Semantic Similarity

🚀 tf-keras

This repository provides a model and notebook for fine - tuning the BERT model on the SNLI Corpus for semantic similarity tasks.

🚀 Quick Start

This repo focuses on fine - tuning the BERT model on the SNLI Corpus to achieve semantic similarity prediction. You can refer to Semantic Similarity with BERT for more details.

✨ Features

• Fine - tune the BERT model for semantic similarity prediction.
• Use the SNLI Corpus for training and evaluation.

📚 Documentation

Model description

This repo contains the model and the notebook for fine - tuning BERT model on SNLI Corpus for Semantic Similarity. Semantic Similarity with BERT.

Full credits go to Mohamad Merchant.

Reproduced by Vu Minh Chien.

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.

Training and evaluation data

This example demonstrates the use of the Stanford Natural Language Inference (SNLI) Corpus to predict semantic sentence similarity with Transformers.

• Total train samples: 100000
• Total validation samples: 10000
• Total test samples: 10000

Here are the "similarity" label values in SNLI dataset:

• Contradiction: The sentences share no similarity.
• Entailment: The sentences have a similar meaning.
• Neutral: The sentences are neutral.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Model Plot