readme: ---
  library_name: 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>
  
  ![Model Image](./model.png)
  
  </details>