File size: 1,596 Bytes
62c98e1 a4dab23 0cfe872 a4dab23 62c98e1 0cfe872 62c98e1 0cfe872 62c98e1 0cfe872 f351457 62c98e1 e66c827 62c98e1 f351457 62c98e1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
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> |