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--- |
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library_name: tf-keras |
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tags: |
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- sentence-similarity |
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--- |
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## Model description |
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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/). |
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Full credits go to [Mohamad Merchant](https://twitter.com/mohmadmerchant1) |
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Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) |
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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. |
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## Training and evaluation data |
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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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Here are the "similarity" label values in SNLI dataset: |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| Hyperparameters | Value | |
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| :-- | :-- | |
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| name | Adam | |
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| learning_rate | 9.999999747378752e-06 | |
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| decay | 0.0 | |
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| beta_1 | 0.8999999761581421 | |
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| beta_2 | 0.9990000128746033 | |
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| epsilon | 1e-07 | |
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| amsgrad | False | |
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| training_precision | float32 | |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
