| --- |
| library_name: transformers |
| license: agpl-3.0 |
| base_model: vinai/phobert-base-v2 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: test |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # test |
|
|
| This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6816 |
| - Accuracy: 0.8543 |
| - F1: 0.8351 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.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: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
| | No log | 0.4545 | 150 | 0.6440 | 0.8190 | 0.6792 | |
| | No log | 0.9091 | 300 | 0.5366 | 0.8283 | 0.7146 | |
| | 0.8169 | 1.3636 | 450 | 0.4794 | 0.8418 | 0.7297 | |
| | 0.8169 | 1.8182 | 600 | 0.4390 | 0.8608 | 0.8331 | |
| | 0.4454 | 2.2727 | 750 | 0.4754 | 0.8471 | 0.8247 | |
| | 0.4454 | 2.7273 | 900 | 0.4398 | 0.8564 | 0.8243 | |
| | 0.3148 | 3.1818 | 1050 | 0.4512 | 0.8553 | 0.8265 | |
| | 0.3148 | 3.6364 | 1200 | 0.4722 | 0.8519 | 0.8332 | |
| | 0.2367 | 4.0909 | 1350 | 0.4722 | 0.8596 | 0.8287 | |
| | 0.2367 | 4.5455 | 1500 | 0.4794 | 0.8623 | 0.8415 | |
| | 0.1722 | 5.0 | 1650 | 0.4721 | 0.8568 | 0.8257 | |
| | 0.1722 | 5.4545 | 1800 | 0.5492 | 0.8581 | 0.8293 | |
| | 0.1722 | 5.9091 | 1950 | 0.5362 | 0.8598 | 0.8285 | |
| | 0.1339 | 6.3636 | 2100 | 0.5936 | 0.8530 | 0.8311 | |
| | 0.1339 | 6.8182 | 2250 | 0.5909 | 0.8598 | 0.8284 | |
| | 0.1051 | 7.2727 | 2400 | 0.5739 | 0.8583 | 0.8358 | |
| | 0.1051 | 7.7273 | 2550 | 0.6112 | 0.8589 | 0.8348 | |
| | 0.0882 | 8.1818 | 2700 | 0.6568 | 0.8541 | 0.8304 | |
| | 0.0882 | 8.6364 | 2850 | 0.6647 | 0.8564 | 0.8373 | |
| | 0.0715 | 9.0909 | 3000 | 0.6697 | 0.8560 | 0.8363 | |
| | 0.0715 | 9.5455 | 3150 | 0.6750 | 0.8549 | 0.8386 | |
| | 0.0586 | 10.0 | 3300 | 0.6816 | 0.8543 | 0.8351 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.57.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.6.0 |
| - Tokenizers 0.22.1 |
|
|
