---
base_model: VietAI/vit5-base
library_name: transformers
license: mit
metrics:
- sacrebleu
tags:
- generated_from_trainer
model-index:
- name: BaViT_Base_Finetune_v0
  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. -->

# BaViT_Base_Finetune_v0

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: 0.4516
- Sacrebleu: 7.9929

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


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0