https://github.com/zobayerakib/transfer-learning-for-nlp-with-tensorflow-hub
This project demonstrates the use of various pre-trained models for transfer learning in NLP using TensorFlow Hub.
https://github.com/zobayerakib/transfer-learning-for-nlp-with-tensorflow-hub
fine-tuning natural-language-processing nlp pretrained-language-model pretrained-models quora-insincere-questions-classification tensorboard-visualizations tensorflowhub transfer-learning
Last synced: 2 months ago
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This project demonstrates the use of various pre-trained models for transfer learning in NLP using TensorFlow Hub.
- Host: GitHub
- URL: https://github.com/zobayerakib/transfer-learning-for-nlp-with-tensorflow-hub
- Owner: ZobayerAkib
- License: mit
- Created: 2024-06-04T18:28:08.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-06-04T18:48:29.000Z (12 months ago)
- Last Synced: 2025-01-19T22:51:53.281Z (4 months ago)
- Topics: fine-tuning, natural-language-processing, nlp, pretrained-language-model, pretrained-models, quora-insincere-questions-classification, tensorboard-visualizations, tensorflowhub, transfer-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 3.05 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Transfer Learning for NLP with TensorFlow Hub
This project demonstrates the use of various pre-trained models for transfer learning in NLP using TensorFlow Hub.
## Training Results
| Model | Epoch | Accuracy | Loss | Val Accuracy | Val Loss |
|------------------------------------|-------|----------|-------|--------------|----------|
| gnews-swivel-20dim | 0 | 0.9331 | 0.2729| 0.9381 | 0.1989 |
| nnlm-en-dim50 | 0 | 0.9339 | 0.3251| 0.9381 | 0.2252 |
| gnews-swivel-20dim-finetuned | 0 | 0.9337 | 0.3145| 0.9381 | 0.2124 |
| nnlm-en-dim128 | 0 | 0.9213 | 0.3408| 0.9381 | 0.2256 |
| universal-sentence-encoder | 0 | 0.9344 | 0.3153| 0.9381 | 0.1770 |
| universal-sentence-encoder-large | 0 | 0.9365 | 0.2923| 0.9381 | 0.1682 |
This table provides a quick overview of the training results for each model, including accuracy, loss, and validation metrics.
# Summary of Training Results
- The models were trained for one epoch each.
- The accuracy of the models ranged from 92.13% to 93.65%.
- The loss values varied between 0.2729 and 0.3408.
- All models achieved a validation accuracy of 93.81%.
- The validation loss ranged from 0.1682 to 0.2256.
This summary provides a concise overview of the training performance across different models.