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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: profesor_MViT_S_VIOPERU |
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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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# profesor_MViT_S_VIOPERU |
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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: 0.2991 |
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- Accuracy: 0.9107 |
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- F1: 0.9107 |
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- Precision: 0.9112 |
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- Recall: 0.9107 |
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- Roc Auc: 0.9576 |
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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: 23 |
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- eval_batch_size: 23 |
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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: 81 |
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- training_steps: 810 |
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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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| 0.6473 | 6.0111 | 81 | 0.6350 | 0.75 | 0.7497 | 0.7513 | 0.75 | 0.8418 | |
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| 0.5438 | 13.0074 | 162 | 0.5559 | 0.7857 | 0.7857 | 0.7857 | 0.7857 | 0.8980 | |
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| 0.4124 | 20.0037 | 243 | 0.4445 | 0.8571 | 0.8564 | 0.8646 | 0.8571 | 0.9439 | |
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| 0.2958 | 26.0148 | 324 | 0.3501 | 0.8929 | 0.8927 | 0.8949 | 0.8929 | 0.9745 | |
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| 0.2126 | 33.0111 | 405 | 0.2827 | 0.8929 | 0.8927 | 0.8949 | 0.8929 | 0.9745 | |
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| 0.1469 | 40.0074 | 486 | 0.3615 | 0.875 | 0.8746 | 0.8794 | 0.875 | 0.9732 | |
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| 0.1063 | 47.0037 | 567 | 0.3208 | 0.8929 | 0.8927 | 0.8949 | 0.8929 | 0.9783 | |
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| 0.0883 | 53.0148 | 648 | 0.4270 | 0.875 | 0.8746 | 0.8794 | 0.875 | 0.9745 | |
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| 0.0631 | 60.0111 | 729 | 0.4191 | 0.8929 | 0.8927 | 0.8949 | 0.8929 | 0.9783 | |
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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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