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: organamnist-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. -->
+
+# organamnist-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: 0.0450
+- Accuracy: 0.9831
+- Precision: 0.9880
+- Recall: 0.9862
+- F1: 0.9869
+
+## 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.6786        | 1.0   | 540  | 0.1776          | 0.9339   | 0.9507    | 0.9341 | 0.9385 |
+| 0.7397        | 2.0   | 1081 | 0.1783          | 0.9407   | 0.9539    | 0.9346 | 0.9415 |
+| 0.7151        | 3.0   | 1621 | 0.1297          | 0.9552   | 0.9611    | 0.9555 | 0.9572 |
+| 0.4964        | 4.0   | 2162 | 0.0741          | 0.9735   | 0.9765    | 0.9702 | 0.9730 |
+| 0.5509        | 5.0   | 2702 | 0.0671          | 0.9770   | 0.9776    | 0.9796 | 0.9783 |
+| 0.5746        | 6.0   | 3243 | 0.0642          | 0.9754   | 0.9810    | 0.9788 | 0.9795 |
+| 0.4066        | 7.0   | 3783 | 0.1196          | 0.9566   | 0.9693    | 0.9563 | 0.9614 |
+| 0.4046        | 8.0   | 4324 | 0.0469          | 0.9798   | 0.9853    | 0.9821 | 0.9834 |
+| 0.3314        | 9.0   | 4864 | 0.0388          | 0.9861   | 0.9892    | 0.9860 | 0.9874 |
+| 0.2865        | 9.99  | 5400 | 0.0450          | 0.9831   | 0.9880    | 0.9862 | 0.9869 |
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.39.3
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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