| 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](https://keras.io/examples/nlp/semantic_similarity_with_bert/). | |
| Full credits go to [Mohamad Merchant](https://twitter.com/mohmadmerchant1) | |
| Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) | |
| 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 | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |