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README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- generated_from_trainer
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datasets:
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- medmnist-v2
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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base_model: facebook/deit-base-patch16-224
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model-index:
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- name: organc-deit-base-finetuned
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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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# organc-deit-base-finetuned
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This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0745
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- Accuracy: 0.9870
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- Precision: 0.9877
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- Recall: 0.9863
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- F1: 0.9868
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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: 0.005
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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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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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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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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.7947 | 1.0 | 203 | 0.3123 | 0.8976 | 0.9090 | 0.8450 | 0.8632 |
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| 0.6703 | 2.0 | 406 | 0.1400 | 0.9607 | 0.9590 | 0.9543 | 0.9535 |
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| 0.5941 | 3.0 | 609 | 0.1182 | 0.9699 | 0.9647 | 0.9681 | 0.9649 |
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| 0.5837 | 4.0 | 813 | 0.1016 | 0.9678 | 0.9558 | 0.9586 | 0.9551 |
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| 0.5193 | 5.0 | 1016 | 0.0800 | 0.9791 | 0.9701 | 0.9684 | 0.9675 |
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| 0.5513 | 6.0 | 1219 | 0.0579 | 0.9862 | 0.9831 | 0.9855 | 0.9840 |
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| 0.4343 | 7.0 | 1422 | 0.0775 | 0.9833 | 0.9858 | 0.9818 | 0.9835 |
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| 0.3942 | 8.0 | 1626 | 0.0782 | 0.9833 | 0.9813 | 0.9827 | 0.9817 |
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| 0.2971 | 9.0 | 1829 | 0.0839 | 0.9862 | 0.9884 | 0.9866 | 0.9873 |
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| 0.3242 | 9.99 | 2030 | 0.0745 | 0.9870 | 0.9877 | 0.9863 | 0.9868 |
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### Framework versions
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- PEFT 0.10.0
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- Transformers 4.38.2
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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