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---
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
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