metadata
library_name: transformers
license: mit
base_model: vinai/phobert-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: phobert-large_v2
results: []
phobert-large_v2
This model is a fine-tuned version of vinai/phobert-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3603
- Accuracy: 0.9520
- Precision Macro: 0.8966
- Recall Macro: 0.8344
- F1 Macro: 0.8599
- F1 Weighted: 0.9500
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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use 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: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|---|---|
| 0.4609 | 1.0 | 90 | 0.2266 | 0.9387 | 0.9153 | 0.7341 | 0.7705 | 0.9298 |
| 0.1892 | 2.0 | 180 | 0.2063 | 0.9419 | 0.8750 | 0.8032 | 0.8303 | 0.9390 |
| 0.1302 | 3.0 | 270 | 0.1810 | 0.9551 | 0.8937 | 0.8696 | 0.8809 | 0.9545 |
| 0.0972 | 4.0 | 360 | 0.1825 | 0.9533 | 0.9077 | 0.8396 | 0.8671 | 0.9513 |
| 0.0676 | 5.0 | 450 | 0.2135 | 0.9514 | 0.8737 | 0.8546 | 0.8636 | 0.9507 |
| 0.0567 | 6.0 | 540 | 0.2387 | 0.9507 | 0.9005 | 0.8334 | 0.8605 | 0.9487 |
| 0.047 | 7.0 | 630 | 0.2487 | 0.9419 | 0.8520 | 0.8401 | 0.8457 | 0.9414 |
| 0.0405 | 8.0 | 720 | 0.3009 | 0.9501 | 0.9101 | 0.8004 | 0.8370 | 0.9462 |
| 0.0225 | 9.0 | 810 | 0.2780 | 0.9514 | 0.8963 | 0.8217 | 0.8506 | 0.9488 |
| 0.0275 | 10.0 | 900 | 0.2952 | 0.9514 | 0.8945 | 0.8421 | 0.8643 | 0.9498 |
| 0.0141 | 11.0 | 990 | 0.3188 | 0.9488 | 0.8690 | 0.8611 | 0.8649 | 0.9486 |
| 0.0122 | 12.0 | 1080 | 0.3221 | 0.9520 | 0.8983 | 0.8261 | 0.8545 | 0.9496 |
| 0.0091 | 13.0 | 1170 | 0.3291 | 0.9526 | 0.9042 | 0.8430 | 0.8684 | 0.9509 |
| 0.0053 | 14.0 | 1260 | 0.3365 | 0.9476 | 0.8795 | 0.8270 | 0.8490 | 0.9456 |
| 0.0065 | 15.0 | 1350 | 0.3530 | 0.9520 | 0.9009 | 0.8345 | 0.8613 | 0.9500 |
| 0.0038 | 16.0 | 1440 | 0.3478 | 0.9520 | 0.9033 | 0.8261 | 0.8561 | 0.9495 |
| 0.0031 | 17.0 | 1530 | 0.3586 | 0.9501 | 0.9001 | 0.8329 | 0.8601 | 0.9481 |
| 0.0016 | 18.0 | 1620 | 0.3596 | 0.9514 | 0.8947 | 0.8421 | 0.8645 | 0.9498 |
| 0.0015 | 19.0 | 1710 | 0.3594 | 0.9520 | 0.8966 | 0.8344 | 0.8599 | 0.9500 |
| 0.0014 | 20.0 | 1800 | 0.3603 | 0.9520 | 0.8966 | 0.8344 | 0.8599 | 0.9500 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4