Model save

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
+model-index:
+- name: chest-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. -->
+
+# chest-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 an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2739
+- Accuracy: 0.8927
+- Precision: 0.8528
+- Recall: 0.8912
+- F1: 0.8685
+
+## 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.4775        | 0.99  | 63   | 0.2264          | 0.9142   | 0.8850    | 0.8962 | 0.8903 |
+| 0.7117        | 1.99  | 127  | 0.4008          | 0.7391   | 0.3695    | 0.5    | 0.4250 |
+| 0.4115        | 3.0   | 191  | 0.4358          | 0.8155   | 0.7871    | 0.8645 | 0.7957 |
+| 0.3631        | 4.0   | 255  | 0.3091          | 0.8798   | 0.8381    | 0.8708 | 0.8518 |
+| 0.3794        | 4.99  | 318  | 0.2802          | 0.8798   | 0.8393    | 0.8623 | 0.8495 |
+| 0.3713        | 5.99  | 382  | 0.2805          | 0.8773   | 0.8371    | 0.8542 | 0.8449 |
+| 0.3953        | 7.0   | 446  | 0.3397          | 0.8584   | 0.8185    | 0.8872 | 0.8367 |
+| 0.3218        | 8.0   | 510  | 0.3072          | 0.8670   | 0.8257    | 0.8898 | 0.8448 |
+| 0.3219        | 8.99  | 573  | 0.2633          | 0.8961   | 0.8582    | 0.8872 | 0.8708 |
+| 0.3049        | 9.88  | 630  | 0.2739          | 0.8927   | 0.8528    | 0.8912 | 0.8685 |
+
+
+### 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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