Model save

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+datasets:
+- medmnist-v2
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
+model-index:
+- name: derma-beit-base-finetuned
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# derma-beit-base-finetuned
+
+This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the medmnist-v2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6096
+- Accuracy: 0.7727
+- Precision: 0.6427
+- Recall: 0.5346
+- F1: 0.5283
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.005
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.9135        | 1.0   | 109  | 0.7698          | 0.7198   | 0.5179    | 0.3103 | 0.3050 |
+| 0.8352        | 2.0   | 219  | 0.7352          | 0.7298   | 0.5362    | 0.4231 | 0.3884 |
+| 0.7891        | 3.0   | 328  | 0.7575          | 0.7178   | 0.3954    | 0.4000 | 0.3667 |
+| 0.7649        | 4.0   | 438  | 0.6879          | 0.7418   | 0.5009    | 0.3972 | 0.4146 |
+| 0.8146        | 5.0   | 547  | 0.7471          | 0.7178   | 0.4490    | 0.4141 | 0.3641 |
+| 0.6831        | 6.0   | 657  | 0.7007          | 0.7368   | 0.4777    | 0.4148 | 0.4252 |
+| 0.695         | 7.0   | 766  | 0.6797          | 0.7428   | 0.4638    | 0.5334 | 0.4841 |
+| 0.6646        | 8.0   | 876  | 0.6534          | 0.7537   | 0.6130    | 0.5077 | 0.4933 |
+| 0.675         | 9.0   | 985  | 0.6238          | 0.7667   | 0.6518    | 0.5431 | 0.5308 |
+| 0.6145        | 9.95  | 1090 | 0.6096          | 0.7727   | 0.6427    | 0.5346 | 0.5283 |
+
+
+### Framework versions
+
+- PEFT 0.9.0
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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