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--- |
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base_model: vinai/phobert-base-v2 |
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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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- recall |
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- precision |
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model-index: |
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- name: cls-comment-phobert-base-v2-v2.4.1 |
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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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# cls-comment-phobert-base-v2-v2.4.1 |
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This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3274 |
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- Accuracy: 0.9268 |
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- F1 Score: 0.8919 |
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- Recall: 0.8944 |
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- Precision: 0.8897 |
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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: 1e-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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 4000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:| |
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| 1.7049 | 0.96 | 100 | 1.4950 | 0.4614 | 0.1080 | 0.1681 | 0.2101 | |
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| 1.3066 | 1.91 | 200 | 1.0451 | 0.6598 | 0.2493 | 0.2970 | 0.2150 | |
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| 0.9457 | 2.87 | 300 | 0.7491 | 0.7972 | 0.5219 | 0.5230 | 0.5238 | |
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| 0.6975 | 3.83 | 400 | 0.5574 | 0.8497 | 0.5700 | 0.5935 | 0.7143 | |
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| 0.5187 | 4.78 | 500 | 0.4681 | 0.8665 | 0.6685 | 0.6592 | 0.7077 | |
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| 0.4183 | 5.74 | 600 | 0.4121 | 0.8821 | 0.7747 | 0.7478 | 0.8761 | |
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| 0.3323 | 6.7 | 700 | 0.3488 | 0.9040 | 0.8505 | 0.8391 | 0.8647 | |
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| 0.2705 | 7.66 | 800 | 0.3179 | 0.9124 | 0.8680 | 0.8694 | 0.8683 | |
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| 0.229 | 8.61 | 900 | 0.3109 | 0.9160 | 0.8739 | 0.8778 | 0.8704 | |
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| 0.1964 | 9.57 | 1000 | 0.3028 | 0.9175 | 0.8776 | 0.8813 | 0.8741 | |
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| 0.1771 | 10.53 | 1100 | 0.3032 | 0.9181 | 0.8807 | 0.8877 | 0.8743 | |
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| 0.1518 | 11.48 | 1200 | 0.3151 | 0.9166 | 0.8762 | 0.8702 | 0.8828 | |
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| 0.1368 | 12.44 | 1300 | 0.2938 | 0.9214 | 0.8794 | 0.8800 | 0.8789 | |
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| 0.1116 | 13.4 | 1400 | 0.2971 | 0.9205 | 0.8795 | 0.8815 | 0.8776 | |
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| 0.1136 | 14.35 | 1500 | 0.3011 | 0.9235 | 0.8858 | 0.8825 | 0.8894 | |
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| 0.094 | 15.31 | 1600 | 0.2937 | 0.9268 | 0.8891 | 0.8933 | 0.8855 | |
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| 0.0905 | 16.27 | 1700 | 0.3049 | 0.9265 | 0.8850 | 0.8819 | 0.8886 | |
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| 0.0838 | 17.22 | 1800 | 0.3061 | 0.9244 | 0.8823 | 0.8869 | 0.8784 | |
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| 0.0749 | 18.18 | 1900 | 0.3275 | 0.9205 | 0.8771 | 0.8839 | 0.8717 | |
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| 0.0686 | 19.14 | 2000 | 0.3092 | 0.9295 | 0.8915 | 0.8990 | 0.8846 | |
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| 0.0669 | 20.1 | 2100 | 0.3168 | 0.9250 | 0.8836 | 0.8849 | 0.8825 | |
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| 0.0582 | 21.05 | 2200 | 0.3339 | 0.9235 | 0.8763 | 0.8926 | 0.8631 | |
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| 0.0516 | 22.01 | 2300 | 0.3274 | 0.9268 | 0.8919 | 0.8944 | 0.8897 | |
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| 0.0543 | 22.97 | 2400 | 0.3230 | 0.9295 | 0.8913 | 0.8882 | 0.8946 | |
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| 0.0435 | 23.92 | 2500 | 0.3364 | 0.9253 | 0.8806 | 0.8705 | 0.8918 | |
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| 0.0405 | 24.88 | 2600 | 0.3492 | 0.9241 | 0.8816 | 0.8821 | 0.8819 | |
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| 0.0398 | 25.84 | 2700 | 0.3558 | 0.9238 | 0.8799 | 0.8796 | 0.8807 | |
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| 0.0363 | 26.79 | 2800 | 0.3605 | 0.9223 | 0.8742 | 0.8795 | 0.8698 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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