update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- preprocessed1024_config
+metrics:
+- accuracy
+- f1
+model-index:
+- name: vit-cc-512-birads
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: preprocessed1024_config
+      type: preprocessed1024_config
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value:
+        accuracy: 0.4943467336683417
+    - name: F1
+      type: f1
+      value:
+        f1: 0.3929699341372617
+---
+
+<!-- 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. -->
+
+# vit-cc-512-birads
+
+This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1133
+- Accuracy: {'accuracy': 0.4943467336683417}
+- F1: {'f1': 0.3929699341372617}
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy                          | F1                          |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
+| 1.1037        | 1.0   | 796  | 1.0357          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
+| 1.0588        | 2.0   | 1592 | 1.0446          | {'accuracy': 0.4623115577889447}  | {'f1': 0.33094476503399495} |
+| 1.0486        | 3.0   | 2388 | 1.0408          | {'accuracy': 0.47361809045226133} | {'f1': 0.3313643442345453}  |
+| 1.0288        | 4.0   | 3184 | 1.0186          | {'accuracy': 0.5050251256281407}  | {'f1': 0.3404676010455165}  |
+| 1.0284        | 5.0   | 3980 | 1.0288          | {'accuracy': 0.5037688442211056}  | {'f1': 0.3406391773730375}  |
+| 0.997         | 6.0   | 4776 | 1.0183          | {'accuracy': 0.5087939698492462}  | {'f1': 0.3539488153998284}  |
+| 0.9682        | 7.0   | 5572 | 1.0965          | {'accuracy': 0.4566582914572864}  | {'f1': 0.3695106771946128}  |
+| 0.9313        | 8.0   | 6368 | 1.0554          | {'accuracy': 0.4962311557788945}  | {'f1': 0.38158088397057704} |
+| 0.8938        | 9.0   | 7164 | 1.0930          | {'accuracy': 0.4943467336683417}  | {'f1': 0.38196414933207573} |
+| 0.8697        | 10.0  | 7960 | 1.1133          | {'accuracy': 0.4943467336683417}  | {'f1': 0.3929699341372617}  |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1