metadata
library_name: transformers
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: ViNormT5
results: []
ViNormT5
This model is a fine-tuned version of VietAI/vit5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2580
- Bleu Score: 79.3456
- Precision: 57.4671
- Recall: 57.4671
- Gen Len: 12.7694
- Err: 57.4671
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu Score | Precision | Recall | Gen Len | Err |
|---|---|---|---|---|---|---|---|---|
| 0.5544 | 1.0 | 838 | 0.2951 | 75.8053 | 45.9976 | 45.9976 | 12.6918 | 45.9976 |
| 0.2306 | 2.0 | 1676 | 0.2519 | 77.9908 | 51.9713 | 51.9713 | 12.7515 | 51.9713 |
| 0.1091 | 3.0 | 2514 | 0.2437 | 78.935 | 54.9582 | 54.9582 | 12.7778 | 54.9582 |
| 0.0435 | 4.0 | 3352 | 0.2580 | 79.3456 | 57.4671 | 57.4671 | 12.7694 | 57.4671 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0