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-breat_composition
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: preprocessed1024_config
+      type: preprocessed1024_config
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value:
+        accuracy: 0.5791457286432161
+    - name: F1
+      type: f1
+      value:
+        f1: 0.5749067914290308
+---
+
+<!-- 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-breat_composition
+
+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: 1.3123
+- Accuracy: {'accuracy': 0.5791457286432161}
+- F1: {'f1': 0.5749067914290308}
+
+## 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                          |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|
+| 1.2679        | 1.0   | 796  | 1.0281          | {'accuracy': 0.5062814070351759} | {'f1': 0.38950358034816535} |
+| 0.9805        | 2.0   | 1592 | 0.9240          | {'accuracy': 0.5672110552763819} | {'f1': 0.5273112700912543}  |
+| 0.9167        | 3.0   | 2388 | 0.9608          | {'accuracy': 0.5477386934673367} | {'f1': 0.45736748568671376} |
+| 0.8292        | 4.0   | 3184 | 0.8973          | {'accuracy': 0.5891959798994975} | {'f1': 0.5783349603036094}  |
+| 0.7695        | 5.0   | 3980 | 1.0477          | {'accuracy': 0.5571608040201005} | {'f1': 0.5379432393338944}  |
+| 0.6912        | 6.0   | 4776 | 0.9479          | {'accuracy': 0.585427135678392}  | {'f1': 0.5766494177636581}  |
+| 0.61          | 7.0   | 5572 | 1.1280          | {'accuracy': 0.5703517587939698} | {'f1': 0.5560158679652624}  |
+| 0.5591        | 8.0   | 6368 | 1.1866          | {'accuracy': 0.5741206030150754} | {'f1': 0.5541999644498281}  |
+| 0.5021        | 9.0   | 7164 | 1.1537          | {'accuracy': 0.582286432160804}  | {'f1': 0.566315815243799}   |
+| 0.4262        | 10.0  | 7960 | 1.3123          | {'accuracy': 0.5791457286432161} | {'f1': 0.5749067914290308}  |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1