metadata
library_name: transformers
license: mit
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: phobert-base_v3
results: []
phobert-base_v3
This model is a fine-tuned version of vinai/phobert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2798
- Accuracy: 0.7805
- Precision Macro: 0.7813
- Recall Macro: 0.7807
- F1 Macro: 0.7806
- F1 Weighted: 0.7806
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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|---|---|
| 1.0861 | 1.0 | 72 | 0.8945 | 0.5863 | 0.6024 | 0.5883 | 0.5734 | 0.5725 |
| 0.8854 | 2.0 | 144 | 0.7401 | 0.6993 | 0.7354 | 0.6992 | 0.6982 | 0.6983 |
| 0.5405 | 3.0 | 216 | 0.5891 | 0.7814 | 0.7817 | 0.7816 | 0.7813 | 0.7813 |
| 0.4119 | 4.0 | 288 | 0.6523 | 0.7761 | 0.7776 | 0.7758 | 0.7760 | 0.7760 |
| 0.2355 | 5.0 | 360 | 0.6712 | 0.7894 | 0.7899 | 0.7892 | 0.7894 | 0.7894 |
| 0.1786 | 6.0 | 432 | 0.8116 | 0.7725 | 0.7733 | 0.7726 | 0.7726 | 0.7726 |
| 0.1126 | 7.0 | 504 | 0.8907 | 0.7761 | 0.7792 | 0.7761 | 0.7761 | 0.7761 |
| 0.0844 | 8.0 | 576 | 0.9184 | 0.7827 | 0.7834 | 0.7825 | 0.7826 | 0.7827 |
| 0.0657 | 9.0 | 648 | 1.0276 | 0.7734 | 0.7769 | 0.7735 | 0.7737 | 0.7737 |
| 0.0458 | 10.0 | 720 | 1.2265 | 0.7583 | 0.7713 | 0.7581 | 0.7582 | 0.7583 |
| 0.0494 | 11.0 | 792 | 1.1001 | 0.7783 | 0.7793 | 0.7783 | 0.7784 | 0.7784 |
| 0.0307 | 12.0 | 864 | 1.1487 | 0.7783 | 0.7798 | 0.7781 | 0.7783 | 0.7783 |
| 0.0284 | 13.0 | 936 | 1.1877 | 0.7805 | 0.7812 | 0.7805 | 0.7805 | 0.7805 |
| 0.0192 | 14.0 | 1008 | 1.2280 | 0.7836 | 0.7843 | 0.7839 | 0.7836 | 0.7836 |
| 0.0172 | 15.0 | 1080 | 1.2466 | 0.7823 | 0.7823 | 0.7823 | 0.7823 | 0.7823 |
| 0.0108 | 16.0 | 1152 | 1.2673 | 0.7809 | 0.7837 | 0.7810 | 0.7812 | 0.7812 |
| 0.0111 | 17.0 | 1224 | 1.2614 | 0.7823 | 0.7825 | 0.7823 | 0.7823 | 0.7823 |
| 0.0094 | 18.0 | 1296 | 1.2754 | 0.7814 | 0.7817 | 0.7815 | 0.7814 | 0.7815 |
| 0.0079 | 19.0 | 1368 | 1.2823 | 0.7809 | 0.7821 | 0.7811 | 0.7811 | 0.7811 |
| 0.0095 | 20.0 | 1440 | 1.2798 | 0.7805 | 0.7813 | 0.7807 | 0.7806 | 0.7806 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4