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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: BaViT_Base_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_Base_Finetune_v0 |
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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: 0.4516 |
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- Sacrebleu: 7.9929 |
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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: 100 |
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- eval_batch_size: 100 |
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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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| 0.6988 | 1.0 | 468 | 0.6266 | 2.2994 | |
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| 0.6014 | 2.0 | 936 | 0.5592 | 4.0780 | |
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| 0.5548 | 3.0 | 1404 | 0.5231 | 4.9356 | |
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| 0.5239 | 4.0 | 1872 | 0.5022 | 5.6738 | |
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| 0.5063 | 5.0 | 2340 | 0.4875 | 6.2733 | |
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| 0.4849 | 6.0 | 2808 | 0.4769 | 6.7126 | |
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| 0.4701 | 7.0 | 3276 | 0.4705 | 6.9856 | |
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| 0.4555 | 8.0 | 3744 | 0.4651 | 7.2721 | |
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| 0.4524 | 9.0 | 4212 | 0.4601 | 7.5539 | |
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| 0.4388 | 10.0 | 4680 | 0.4571 | 7.6076 | |
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| 0.4341 | 11.0 | 5148 | 0.4549 | 7.7267 | |
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| 0.4231 | 12.0 | 5616 | 0.4536 | 7.9165 | |
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| 0.4174 | 13.0 | 6084 | 0.4519 | 7.9585 | |
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| 0.4209 | 14.0 | 6552 | 0.4515 | 7.9864 | |
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| 0.4167 | 15.0 | 7020 | 0.4516 | 7.9929 | |
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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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