---
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