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--- |
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base_model: VietAI/vit5-large |
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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: BaViT_Large_Finetune_v0 |
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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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# BaViT_Large_Finetune_v0 |
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This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4909 |
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- Sacrebleu: 11.1985 |
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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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| 0.4969 | 1.0 | 2922 | 0.4594 | 6.2638 | |
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| 0.4322 | 2.0 | 5844 | 0.4101 | 8.4455 | |
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| 0.3585 | 3.0 | 8766 | 0.3905 | 9.4839 | |
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| 0.315 | 4.0 | 11688 | 0.3867 | 10.0488 | |
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| 0.2831 | 5.0 | 14610 | 0.3877 | 10.4073 | |
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| 0.2541 | 6.0 | 17532 | 0.3939 | 10.5730 | |
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| 0.2136 | 7.0 | 20454 | 0.4032 | 10.8551 | |
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| 0.1985 | 8.0 | 23376 | 0.4167 | 10.8040 | |
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| 0.1775 | 9.0 | 26298 | 0.4268 | 10.8393 | |
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| 0.1597 | 10.0 | 29220 | 0.4394 | 10.8971 | |
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| 0.143 | 11.0 | 32142 | 0.4533 | 10.9369 | |
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| 0.1396 | 12.0 | 35064 | 0.4673 | 11.0112 | |
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| 0.1258 | 13.0 | 37986 | 0.4771 | 11.1620 | |
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| 0.1178 | 14.0 | 40908 | 0.4849 | 11.1513 | |
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| 0.1109 | 15.0 | 43830 | 0.4909 | 11.1985 | |
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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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