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: 0.2594
- Bleu Score: 79.8663
- Precision: 58.4229
- Recall: 58.4229
- Gen Len: 12.7658
- Err: 58.4229
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: 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 |
|---|---|---|---|---|---|---|---|---|
| 0.4715 | 1.0 | 838 | 0.2451 | 76.8709 | 49.1039 | 49.1039 | 12.8781 | 49.1039 |
| 0.1802 | 2.0 | 1676 | 0.2337 | 78.3434 | 53.8829 | 53.8829 | 12.7861 | 53.8829 |
| 0.081 | 3.0 | 2514 | 0.2327 | 79.3197 | 56.1529 | 56.1529 | 12.7706 | 56.1529 |
| 0.0365 | 4.0 | 3352 | 0.2594 | 79.8663 | 58.4229 | 58.4229 | 12.7658 | 58.4229 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0