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--- |
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library_name: transformers |
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license: mit |
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base_model: vinai/phobert-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: phobert-base_v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# phobert-base_v2 |
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This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3362 |
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- Accuracy: 0.9482 |
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- Precision Macro: 0.8854 |
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- Recall Macro: 0.8318 |
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- F1 Macro: 0.8543 |
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- F1 Weighted: 0.9464 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:| |
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| 0.4592 | 1.0 | 90 | 0.2280 | 0.9356 | 0.8885 | 0.7440 | 0.7800 | 0.9283 | |
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| 0.1801 | 2.0 | 180 | 0.1823 | 0.9476 | 0.8617 | 0.8443 | 0.8523 | 0.9469 | |
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| 0.1221 | 3.0 | 270 | 0.1834 | 0.9482 | 0.8795 | 0.8359 | 0.8548 | 0.9467 | |
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| 0.1071 | 4.0 | 360 | 0.1868 | 0.9520 | 0.9086 | 0.8096 | 0.8447 | 0.9486 | |
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| 0.0817 | 5.0 | 450 | 0.2031 | 0.9526 | 0.8980 | 0.8393 | 0.8635 | 0.9508 | |
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| 0.065 | 6.0 | 540 | 0.2240 | 0.9501 | 0.8908 | 0.8084 | 0.8389 | 0.9469 | |
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| 0.0574 | 7.0 | 630 | 0.2219 | 0.9501 | 0.8625 | 0.8701 | 0.8662 | 0.9504 | |
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| 0.0481 | 8.0 | 720 | 0.2503 | 0.9469 | 0.8752 | 0.8266 | 0.8472 | 0.9451 | |
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| 0.0362 | 9.0 | 810 | 0.2489 | 0.9495 | 0.8822 | 0.8121 | 0.8392 | 0.9466 | |
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| 0.0319 | 10.0 | 900 | 0.2584 | 0.9501 | 0.8784 | 0.8413 | 0.8577 | 0.9488 | |
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| 0.0263 | 11.0 | 990 | 0.2774 | 0.9488 | 0.8800 | 0.8281 | 0.8498 | 0.9469 | |
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| 0.0199 | 12.0 | 1080 | 0.2790 | 0.9501 | 0.8780 | 0.8416 | 0.8577 | 0.9488 | |
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| 0.0114 | 13.0 | 1170 | 0.2955 | 0.9476 | 0.8733 | 0.8393 | 0.8546 | 0.9463 | |
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| 0.0126 | 14.0 | 1260 | 0.3105 | 0.9501 | 0.8953 | 0.8331 | 0.8586 | 0.9481 | |
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| 0.0125 | 15.0 | 1350 | 0.3147 | 0.9482 | 0.8773 | 0.8397 | 0.8564 | 0.9469 | |
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| 0.0106 | 16.0 | 1440 | 0.3247 | 0.9469 | 0.8861 | 0.8350 | 0.8567 | 0.9453 | |
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| 0.0065 | 17.0 | 1530 | 0.3419 | 0.9476 | 0.8751 | 0.8274 | 0.8476 | 0.9458 | |
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| 0.0072 | 18.0 | 1620 | 0.3406 | 0.9469 | 0.8933 | 0.8185 | 0.8475 | 0.9444 | |
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| 0.0058 | 19.0 | 1710 | 0.3389 | 0.9495 | 0.8904 | 0.8328 | 0.8566 | 0.9476 | |
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| 0.0064 | 20.0 | 1800 | 0.3362 | 0.9482 | 0.8854 | 0.8318 | 0.8543 | 0.9464 | |
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### Framework versions |
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- Transformers 4.55.0 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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