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--- |
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base_model: VietAI/vit5-base |
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library_name: transformers |
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license: mit |
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metrics: |
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- sacrebleu |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: BaViT5_v01 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# BaViT5_v01 |
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This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4623 |
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- Sacrebleu: 14.3803 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Sacrebleu | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:| |
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| 0.6515 | 1.0 | 2966 | 0.5899 | 7.7723 | |
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| 0.576 | 2.0 | 5932 | 0.5257 | 10.4904 | |
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| 0.4939 | 3.0 | 8898 | 0.4969 | 11.8064 | |
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| 0.4842 | 4.0 | 11864 | 0.4793 | 12.5193 | |
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| 0.4459 | 5.0 | 14830 | 0.4704 | 12.9876 | |
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| 0.4222 | 6.0 | 17796 | 0.4632 | 13.2632 | |
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| 0.4005 | 7.0 | 20762 | 0.4612 | 13.5868 | |
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| 0.3869 | 8.0 | 23728 | 0.4580 | 13.8162 | |
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| 0.381 | 9.0 | 26694 | 0.4556 | 13.9756 | |
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| 0.3594 | 10.0 | 29660 | 0.4561 | 14.0827 | |
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| 0.363 | 11.0 | 32626 | 0.4578 | 14.1701 | |
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| 0.3427 | 12.0 | 35592 | 0.4591 | 14.2903 | |
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| 0.3425 | 13.0 | 38558 | 0.4603 | 14.3091 | |
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| 0.3377 | 14.0 | 41524 | 0.4611 | 14.3649 | |
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| 0.314 | 15.0 | 44490 | 0.4623 | 14.3803 | |
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### Framework versions |
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- Transformers 4.48.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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