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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-v2.2.2 |
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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-v2.2.2 |
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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: 0.6359 |
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- Accuracy: 0.9235 |
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- F1 Score: 0.8747 |
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- Recall: 0.8709 |
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- Precision: 0.8816 |
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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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- label_smoothing_factor: 0.1 |
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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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| 1.7287 | 1.05 | 100 | 1.5513 | 0.5041 | 0.1117 | 0.1667 | 0.0840 | |
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| 1.4006 | 2.11 | 200 | 1.1607 | 0.6608 | 0.3075 | 0.3196 | 0.4739 | |
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| 1.0635 | 3.16 | 300 | 0.8875 | 0.8212 | 0.5457 | 0.5578 | 0.5393 | |
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| 0.8514 | 4.21 | 400 | 0.7688 | 0.8522 | 0.5716 | 0.5872 | 0.5581 | |
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| 0.761 | 5.26 | 500 | 0.7055 | 0.8746 | 0.6412 | 0.6401 | 0.7368 | |
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| 0.6727 | 6.32 | 600 | 0.6545 | 0.9023 | 0.7811 | 0.7644 | 0.8581 | |
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| 0.6059 | 7.37 | 700 | 0.6360 | 0.9109 | 0.8464 | 0.8196 | 0.8859 | |
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| 0.5726 | 8.42 | 800 | 0.6340 | 0.9119 | 0.8564 | 0.8416 | 0.8743 | |
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| 0.5411 | 9.47 | 900 | 0.6197 | 0.9159 | 0.8692 | 0.8554 | 0.8868 | |
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| 0.5237 | 10.53 | 1000 | 0.6127 | 0.9192 | 0.8718 | 0.8474 | 0.9042 | |
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| 0.5055 | 11.58 | 1100 | 0.6201 | 0.9215 | 0.8703 | 0.8603 | 0.8839 | |
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| 0.5005 | 12.63 | 1200 | 0.6259 | 0.9231 | 0.8790 | 0.8680 | 0.8944 | |
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| 0.4846 | 13.68 | 1300 | 0.6159 | 0.9225 | 0.8726 | 0.8703 | 0.8759 | |
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| 0.4798 | 14.74 | 1400 | 0.6205 | 0.9244 | 0.8779 | 0.8636 | 0.8969 | |
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| 0.4744 | 15.79 | 1500 | 0.6254 | 0.9248 | 0.8742 | 0.8620 | 0.8909 | |
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| 0.4637 | 16.84 | 1600 | 0.6342 | 0.9228 | 0.8717 | 0.8653 | 0.8819 | |
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| 0.4584 | 17.89 | 1700 | 0.6359 | 0.9235 | 0.8747 | 0.8709 | 0.8816 | |
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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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