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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_B_RLVS |
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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_B_RLVS |
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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: 0.0380 |
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- Accuracy: 0.9908 |
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- F1: 0.9908 |
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- Precision: 0.9908 |
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- Recall: 0.9908 |
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- Roc Auc: 0.9992 |
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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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| 0.427 | 2.0333 | 240 | 0.2509 | 0.9661 | 0.9660 | 0.9671 | 0.9661 | 0.9961 | |
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| 0.1551 | 5.0333 | 480 | 0.1313 | 0.9687 | 0.9687 | 0.9695 | 0.9687 | 0.9974 | |
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| 0.0783 | 8.0333 | 720 | 0.1090 | 0.9713 | 0.9713 | 0.9719 | 0.9713 | 0.9974 | |
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| 0.0547 | 11.0333 | 960 | 0.0647 | 0.9843 | 0.9843 | 0.9843 | 0.9843 | 0.9974 | |
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| 0.0379 | 14.0333 | 1200 | 0.0472 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9987 | |
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| 0.0337 | 17.0333 | 1440 | 0.0552 | 0.9869 | 0.9869 | 0.9870 | 0.9869 | 0.9986 | |
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| 0.0299 | 20.0333 | 1680 | 0.0786 | 0.9843 | 0.9843 | 0.9844 | 0.9843 | 0.9992 | |
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| 0.0308 | 23.0333 | 1920 | 0.0753 | 0.9817 | 0.9817 | 0.9818 | 0.9817 | 0.9995 | |
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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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