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---
base_model: VietAI/vit5-base
library_name: transformers
license: mit
metrics:
- sacrebleu
tags:
- generated_from_trainer
model-index:
- name: BaViT5_v01
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_v01
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4623
- Sacrebleu: 14.3803
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|
| 0.6515 | 1.0 | 2966 | 0.5899 | 7.7723 |
| 0.576 | 2.0 | 5932 | 0.5257 | 10.4904 |
| 0.4939 | 3.0 | 8898 | 0.4969 | 11.8064 |
| 0.4842 | 4.0 | 11864 | 0.4793 | 12.5193 |
| 0.4459 | 5.0 | 14830 | 0.4704 | 12.9876 |
| 0.4222 | 6.0 | 17796 | 0.4632 | 13.2632 |
| 0.4005 | 7.0 | 20762 | 0.4612 | 13.5868 |
| 0.3869 | 8.0 | 23728 | 0.4580 | 13.8162 |
| 0.381 | 9.0 | 26694 | 0.4556 | 13.9756 |
| 0.3594 | 10.0 | 29660 | 0.4561 | 14.0827 |
| 0.363 | 11.0 | 32626 | 0.4578 | 14.1701 |
| 0.3427 | 12.0 | 35592 | 0.4591 | 14.2903 |
| 0.3425 | 13.0 | 38558 | 0.4603 | 14.3091 |
| 0.3377 | 14.0 | 41524 | 0.4611 | 14.3649 |
| 0.314 | 15.0 | 44490 | 0.4623 | 14.3803 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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