update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- preprocessed1024_config
+metrics:
+- accuracy
+- f1
+model-index:
+- name: convnext-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: 0.5665829145728644
+    - name: F1
+      type: f1
+      value:
+        f1: 0.5549950963329491
+---
+
+<!-- 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. -->
+
+# convnext-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: 1.1801
+- Accuracy: {'accuracy': 0.5665829145728644}
+- F1: {'f1': 0.5549950963329491}
+
+## 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                          |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
+| 1.3412        | 1.0   | 796  | 1.1931          | {'accuracy': 0.4547738693467337}  | {'f1': 0.31154642522501674} |
+| 1.1149        | 2.0   | 1592 | 1.0845          | {'accuracy': 0.4886934673366834}  | {'f1': 0.40829339044510005} |
+| 1.0531        | 3.0   | 2388 | 1.1650          | {'accuracy': 0.48304020100502515} | {'f1': 0.38992060973001436} |
+| 0.917         | 4.0   | 3184 | 0.9950          | {'accuracy': 0.5452261306532663}  | {'f1': 0.50281030200465}    |
+| 0.8633        | 5.0   | 3980 | 1.0152          | {'accuracy': 0.5552763819095478}  | {'f1': 0.511332789082197}   |
+| 0.7747        | 6.0   | 4776 | 1.0201          | {'accuracy': 0.5703517587939698}  | {'f1': 0.523154780871296}   |
+| 0.7133        | 7.0   | 5572 | 1.0345          | {'accuracy': 0.5640703517587939}  | {'f1': 0.5198008328503952}  |
+| 0.659         | 8.0   | 6368 | 1.0702          | {'accuracy': 0.5785175879396985}  | {'f1': 0.5460580312777853}  |
+| 0.5943        | 9.0   | 7164 | 1.1634          | {'accuracy': 0.5734924623115578}  | {'f1': 0.5501266468657362}  |
+| 0.5699        | 10.0  | 7960 | 1.1801          | {'accuracy': 0.5665829145728644}  | {'f1': 0.5549950963329491}  |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.12.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1