library_name: tf-keras
tags:
- sentence-similarity
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:
| Hyperparameters | Value |
|---|---|
| name | Adam |
| learning_rate | 9.999999747378752e-06 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
Model Plot
View Model Plot
