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-mlo-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: 0.4667085427135678
+    - name: F1
+      type: f1
+      value:
+        f1: 0.3786054240333243
+---
+
+<!-- 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-mlo-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: 1.0864
+- Accuracy: {'accuracy': 0.4667085427135678}
+- F1: {'f1': 0.3786054240333243}
+
+## 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                          |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
+| 1.103         | 1.0   | 796  | 1.0452          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
+| 1.0596        | 2.0   | 1592 | 1.0433          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
+| 1.0547        | 3.0   | 2388 | 1.0361          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
+| 1.047         | 4.0   | 3184 | 1.0395          | {'accuracy': 0.46796482412060303} | {'f1': 0.25128840471066954} |
+| 1.0524        | 5.0   | 3980 | 1.0331          | {'accuracy': 0.4648241206030151}  | {'f1': 0.298317360340153}   |
+| 1.0268        | 6.0   | 4776 | 1.0224          | {'accuracy': 0.47675879396984927} | {'f1': 0.23426509831984135} |
+| 1.0043        | 7.0   | 5572 | 1.0609          | {'accuracy': 0.417713567839196}   | {'f1': 0.3663405670841817}  |
+| 0.982         | 8.0   | 6368 | 1.0521          | {'accuracy': 0.44221105527638194} | {'f1': 0.3650005046420297}  |
+| 0.9315        | 9.0   | 7164 | 1.0473          | {'accuracy': 0.47738693467336685} | {'f1': 0.3727220695970696}  |
+| 0.9319        | 10.0  | 7960 | 1.0864          | {'accuracy': 0.4667085427135678}  | {'f1': 0.3786054240333243}  |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1