Model save

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
+datasets:
+- medmnist-v2
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: breastmnist-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. -->
+
+# breastmnist-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: 0.5228
+- Accuracy: 0.7308
+- Precision: 0.3654
+- Recall: 0.5
+- F1: 0.4222
+
+## 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 0.9143 | 8    | 0.8325          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+| 0.7315        | 1.9429 | 17   | 0.5744          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+| 0.6223        | 2.9714 | 26   | 0.5911          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+| 0.5815        | 4.0    | 35   | 0.5743          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+| 0.5627        | 4.9143 | 43   | 0.6546          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+| 0.5552        | 5.9429 | 52   | 0.5381          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+| 0.536         | 6.9714 | 61   | 0.5101          | 0.7949   | 0.8904    | 0.6190 | 0.6308 |
+| 0.5454        | 8.0    | 70   | 0.5273          | 0.7692   | 0.7246    | 0.6165 | 0.6286 |
+| 0.5454        | 8.9143 | 78   | 0.5176          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+| 0.5058        | 9.1429 | 80   | 0.5228          | 0.7308   | 0.3654    | 0.5    | 0.4222 |
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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