Instructions to use NIRVLab/bartede with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NIRVLab/bartede with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NIRVLab/bartede") model = AutoModelForSeq2SeqLM.from_pretrained("NIRVLab/bartede") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [vinai/bartpho-syllable](https://huggingface.co/vinai/bartpho-syllable) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 |
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| No log | 2.0 |
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| No log | 3.0 |
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| No log | 4.0 |
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| No log | 5.0 |
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| No log | 6.0 |
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| No log | 7.0 |
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| No log | 8.0 |
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| No log | 9.0 |
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| No log | 10.0 |
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| No log | 11.0 |
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| No log | 12.0 |
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| No log | 13.0 |
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| No log | 14.0 |
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| No log | 15.0 |
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### Framework versions
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This model is a fine-tuned version of [vinai/bartpho-syllable](https://huggingface.co/vinai/bartpho-syllable) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0589
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 27 | 0.9676 |
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| No log | 2.0 | 54 | 0.1928 |
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| No log | 3.0 | 81 | 0.1324 |
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| No log | 4.0 | 108 | 0.1083 |
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| No log | 5.0 | 135 | 0.0912 |
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| No log | 6.0 | 162 | 0.0774 |
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| No log | 7.0 | 189 | 0.0735 |
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| No log | 8.0 | 216 | 0.0672 |
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| No log | 9.0 | 243 | 0.0682 |
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| No log | 10.0 | 270 | 0.0633 |
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| No log | 11.0 | 297 | 0.0660 |
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| No log | 12.0 | 324 | 0.0631 |
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| No log | 13.0 | 351 | 0.0611 |
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| No log | 14.0 | 378 | 0.0589 |
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| No log | 15.0 | 405 | 0.0590 |
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### Framework versions
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