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---
base_model: VietAI/vit5-large
library_name: transformers
license: mit
metrics:
- sacrebleu
tags:
- generated_from_trainer
model-index:
- name: BaViT_Large_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_Large_Finetune_v0
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: 0.4909
- Sacrebleu: 11.1985
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|
| 0.4969 | 1.0 | 2922 | 0.4594 | 6.2638 |
| 0.4322 | 2.0 | 5844 | 0.4101 | 8.4455 |
| 0.3585 | 3.0 | 8766 | 0.3905 | 9.4839 |
| 0.315 | 4.0 | 11688 | 0.3867 | 10.0488 |
| 0.2831 | 5.0 | 14610 | 0.3877 | 10.4073 |
| 0.2541 | 6.0 | 17532 | 0.3939 | 10.5730 |
| 0.2136 | 7.0 | 20454 | 0.4032 | 10.8551 |
| 0.1985 | 8.0 | 23376 | 0.4167 | 10.8040 |
| 0.1775 | 9.0 | 26298 | 0.4268 | 10.8393 |
| 0.1597 | 10.0 | 29220 | 0.4394 | 10.8971 |
| 0.143 | 11.0 | 32142 | 0.4533 | 10.9369 |
| 0.1396 | 12.0 | 35064 | 0.4673 | 11.0112 |
| 0.1258 | 13.0 | 37986 | 0.4771 | 11.1620 |
| 0.1178 | 14.0 | 40908 | 0.4849 | 11.1513 |
| 0.1109 | 15.0 | 43830 | 0.4909 | 11.1985 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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