---
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