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--- |
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license: mit |
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base_model: tangminhanh/results |
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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: results |
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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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# results |
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This model is a fine-tuned version of [tangminhanh/results](https://huggingface.co/tangminhanh/results) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0214 |
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- Accuracy: 0.8673 |
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- F1: 0.8828 |
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- Precision: 0.8907 |
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- Recall: 0.8750 |
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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: 16 |
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- seed: 42 |
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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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- 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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| 0.0959 | 1.0 | 816 | 0.0287 | 0.7330 | 0.7966 | 0.8666 | 0.7371 | |
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| 0.0223 | 2.0 | 1632 | 0.0203 | 0.8256 | 0.8587 | 0.8922 | 0.8277 | |
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| 0.0171 | 3.0 | 2448 | 0.0197 | 0.8348 | 0.8639 | 0.8824 | 0.8463 | |
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| 0.0116 | 4.0 | 3264 | 0.0194 | 0.8486 | 0.8708 | 0.8873 | 0.8548 | |
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| 0.0101 | 5.0 | 4080 | 0.0198 | 0.8532 | 0.8704 | 0.8798 | 0.8612 | |
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| 0.008 | 6.0 | 4896 | 0.0200 | 0.8550 | 0.8742 | 0.8872 | 0.8615 | |
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| 0.0065 | 7.0 | 5712 | 0.0204 | 0.8614 | 0.8775 | 0.8867 | 0.8686 | |
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| 0.0056 | 8.0 | 6528 | 0.0208 | 0.8587 | 0.8768 | 0.8858 | 0.8679 | |
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| 0.0048 | 9.0 | 7344 | 0.0214 | 0.8624 | 0.8781 | 0.8875 | 0.8689 | |
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| 0.0044 | 10.0 | 8160 | 0.0214 | 0.8673 | 0.8828 | 0.8907 | 0.8750 | |
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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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