| --- |
| base_model: VietAI/vit5-large |
| library_name: transformers |
| license: mit |
| metrics: |
| - sacrebleu |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: BaViT5_v2 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # BaViT5_v2 |
| |
| This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4562 |
| - Sacrebleu: 15.4902 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:| |
| | 0.5323 | 1.0 | 2966 | 0.4843 | 10.8807 | |
| | 0.4426 | 2.0 | 5932 | 0.4266 | 13.2481 | |
| | 0.3629 | 3.0 | 8898 | 0.4084 | 14.2709 | |
| | 0.3321 | 4.0 | 11864 | 0.4032 | 14.8016 | |
| | 0.286 | 5.0 | 14830 | 0.4061 | 15.1102 | |
| | 0.2528 | 6.0 | 17796 | 0.4160 | 15.2808 | |
| | 0.2235 | 7.0 | 20762 | 0.4270 | 15.4345 | |
| | 0.2018 | 8.0 | 23728 | 0.4400 | 15.4360 | |
| | 0.1856 | 9.0 | 26694 | 0.4562 | 15.4902 | |
| | 0.1639 | 10.0 | 29660 | 0.4705 | 15.4167 | |
| | 0.1565 | 11.0 | 32626 | 0.4886 | 15.4478 | |
| | 0.1392 | 12.0 | 35592 | 0.5035 | 15.4189 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.48.1 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.2.0 |
| - Tokenizers 0.21.0 |
| |
