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: 0.2324
- Bleu Score: 79.5557
- Precision: 56.9892
- Recall: 56.9892
- Gen Len: 12.7969
- Err: 56.9892
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|---|
| 0.4672 | 1.0 | 419 | 0.2390 | 76.7871 | 49.7013 | 49.7013 | 12.8292 | 49.7013 |
| 0.1742 | 2.0 | 838 | 0.2173 | 77.9698 | 54.0024 | 54.0024 | 12.8076 | 54.0024 |
| 0.0814 | 3.0 | 1257 | 0.2010 | 79.204 | 56.6308 | 56.6308 | 12.7754 | 56.6308 |
| 0.0382 | 4.0 | 1676 | 0.2324 | 79.5557 | 56.9892 | 56.9892 | 12.7969 | 56.9892 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0