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--- |
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library_name: transformers |
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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: estudiante_S3D_RWF2000 |
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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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# estudiante_S3D_RWF2000 |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4851 |
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- Accuracy: 0.8925 |
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- F1: 0.8924 |
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- Precision: 0.8937 |
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- Recall: 0.8925 |
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- Roc Auc: 0.9471 |
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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: 75 |
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- eval_batch_size: 75 |
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- seed: 42 |
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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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- lr_scheduler_warmup_steps: 189 |
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- training_steps: 1890 |
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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 | F1 | Precision | Recall | Roc Auc | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| |
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| 0.4756 | 3.0116 | 94 | 0.4573 | 0.77 | 0.7623 | 0.8102 | 0.77 | 0.9241 | |
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| 0.3034 | 7.0106 | 188 | 0.4936 | 0.775 | 0.7641 | 0.8374 | 0.775 | 0.9375 | |
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| 0.2149 | 11.0095 | 282 | 0.7740 | 0.77 | 0.7583 | 0.8348 | 0.77 | 0.9408 | |
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| 0.1536 | 15.0085 | 376 | 0.7817 | 0.795 | 0.7877 | 0.8418 | 0.795 | 0.9405 | |
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| 0.1242 | 19.0074 | 470 | 0.7977 | 0.7875 | 0.7793 | 0.8375 | 0.7875 | 0.9390 | |
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| 0.0884 | 23.0063 | 564 | 0.7113 | 0.83 | 0.8263 | 0.8603 | 0.83 | 0.9438 | |
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| 0.0742 | 27.0053 | 658 | 0.6809 | 0.8325 | 0.8290 | 0.8619 | 0.8325 | 0.9446 | |
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| 0.055 | 31.0042 | 752 | 0.5733 | 0.835 | 0.8319 | 0.8613 | 0.835 | 0.9422 | |
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| 0.0441 | 35.0032 | 846 | 0.5877 | 0.85 | 0.8485 | 0.8646 | 0.85 | 0.9428 | |
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| 0.0304 | 39.0021 | 940 | 0.4630 | 0.865 | 0.8641 | 0.8746 | 0.865 | 0.9444 | |
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| 0.0242 | 43.0011 | 1034 | 0.5108 | 0.8675 | 0.8667 | 0.8765 | 0.8675 | 0.9440 | |
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| 0.0201 | 46.0127 | 1128 | 0.5144 | 0.87 | 0.8694 | 0.8774 | 0.87 | 0.9451 | |
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| 0.0165 | 50.0116 | 1222 | 0.3905 | 0.8825 | 0.8822 | 0.8860 | 0.8825 | 0.9463 | |
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| 0.0232 | 54.0106 | 1316 | 0.4735 | 0.8825 | 0.8822 | 0.8860 | 0.8825 | 0.9466 | |
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| 0.0098 | 58.0095 | 1410 | 0.4823 | 0.8825 | 0.8823 | 0.8847 | 0.8825 | 0.9457 | |
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| 0.0132 | 62.0085 | 1504 | 0.4508 | 0.88 | 0.8798 | 0.8824 | 0.88 | 0.9475 | |
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| 0.0105 | 66.0074 | 1598 | 0.4055 | 0.885 | 0.8849 | 0.8869 | 0.885 | 0.9477 | |
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| 0.0082 | 70.0063 | 1692 | 0.4190 | 0.885 | 0.8849 | 0.8869 | 0.885 | 0.9464 | |
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| 0.0072 | 74.0053 | 1786 | 0.4812 | 0.8875 | 0.8874 | 0.8891 | 0.8875 | 0.9476 | |
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| 0.0056 | 78.0042 | 1880 | 0.4603 | 0.8975 | 0.8975 | 0.8980 | 0.8975 | 0.9484 | |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.1 |
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