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
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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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### 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: 0.2840
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 158 | 0.4219 |
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| No log | 2.0 | 316 | 0.3691 |
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| No log | 3.0 | 474 | 0.3415 |
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| 0.4889 | 4.0 | 632 | 0.3219 |
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| 0.4889 | 5.0 | 790 | 0.3108 |
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| 0.4889 | 6.0 | 948 | 0.2988 |
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| 0.2882 | 7.0 | 1106 | 0.2931 |
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| 0.2882 | 8.0 | 1264 | 0.2903 |
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| 0.2882 | 9.0 | 1422 | 0.2893 |
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| 0.2379 | 10.0 | 1580 | 0.2850 |
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| 0.2379 | 11.0 | 1738 | 0.2868 |
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| 0.2379 | 12.0 | 1896 | 0.2840 |
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| 0.2053 | 13.0 | 2054 | 0.2849 |
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| 0.2053 | 14.0 | 2212 | 0.2871 |
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| 0.2053 | 15.0 | 2370 | 0.2874 |
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### Framework versions
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