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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_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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# profesor_MViT_S_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: 0.2287 |
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- Accuracy: 0.9287 |
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- F1: 0.9287 |
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- Precision: 0.9288 |
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- Recall: 0.9287 |
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- Roc Auc: 0.9790 |
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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: 20 |
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- eval_batch_size: 20 |
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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: 240 |
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- training_steps: 2400 |
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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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| 0.4986 | 2.0333 | 240 | 0.3695 | 0.9075 | 0.9075 | 0.9080 | 0.9075 | 0.9649 | |
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| 0.2786 | 5.0333 | 480 | 0.2486 | 0.915 | 0.9150 | 0.9152 | 0.915 | 0.9791 | |
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| 0.1896 | 8.0333 | 720 | 0.2303 | 0.92 | 0.9200 | 0.9204 | 0.92 | 0.9804 | |
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| 0.1882 | 11.0333 | 960 | 0.2339 | 0.9175 | 0.9175 | 0.9178 | 0.9175 | 0.9801 | |
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| 0.1381 | 14.0333 | 1200 | 0.2263 | 0.93 | 0.9300 | 0.9304 | 0.93 | 0.9807 | |
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| 0.1401 | 17.0333 | 1440 | 0.2477 | 0.9275 | 0.9275 | 0.9280 | 0.9275 | 0.9795 | |
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| 0.1016 | 20.0333 | 1680 | 0.2504 | 0.925 | 0.9250 | 0.9257 | 0.925 | 0.9819 | |
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| 0.1106 | 23.0333 | 1920 | 0.2722 | 0.93 | 0.9300 | 0.9304 | 0.93 | 0.9812 | |
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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 3.0.2 |
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- Tokenizers 0.20.1 |
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