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--- |
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license: mit |
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base_model: tangminhanh/cate-ts |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: subcate-ts |
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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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# subcate-ts |
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This model is a fine-tuned version of [tangminhanh/cate-ts](https://huggingface.co/tangminhanh/cate-ts) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0130 |
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- Accuracy: 0.3377 |
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- F1: 0.4963 |
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- Precision: 0.9460 |
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- Recall: 0.3364 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.8242 | 0.9987 | 188 | 0.2821 | 0.0 | 0.0081 | 0.0046 | 0.0321 | |
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| 0.1451 | 1.9973 | 376 | 0.0313 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.0298 | 2.9960 | 564 | 0.0231 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.024 | 4.0 | 753 | 0.0206 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.0212 | 4.9987 | 941 | 0.0181 | 0.1261 | 0.2233 | 0.9922 | 0.1258 | |
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| 0.0187 | 5.9973 | 1129 | 0.0162 | 0.2292 | 0.3696 | 0.9664 | 0.2285 | |
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| 0.0169 | 6.9960 | 1317 | 0.0147 | 0.3081 | 0.4657 | 0.9646 | 0.3070 | |
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| 0.0157 | 8.0 | 1506 | 0.0137 | 0.3184 | 0.4763 | 0.9551 | 0.3172 | |
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| 0.0151 | 8.9987 | 1694 | 0.0132 | 0.3310 | 0.4894 | 0.9486 | 0.3298 | |
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| 0.0145 | 9.9867 | 1880 | 0.0130 | 0.3377 | 0.4963 | 0.9460 | 0.3364 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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