update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-patch16-224-finetuned-hateful-meme-restructured-balanced
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.556
+---
+
+<!-- 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-base-patch16-224-finetuned-hateful-meme-restructured-balanced
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7145
+- Accuracy: 0.556
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.7016        | 0.98  | 47   | 0.7243          | 0.512    |
+| 0.6676        | 1.99  | 95   | 0.7139          | 0.544    |
+| 0.626         | 2.99  | 143  | 0.7145          | 0.556    |
+| 0.6042        | 4.0   | 191  | 0.7342          | 0.556    |
+| 0.5672        | 4.98  | 238  | 0.7481          | 0.548    |
+| 0.5339        | 5.99  | 286  | 0.7458          | 0.532    |
+| 0.5266        | 6.99  | 334  | 0.7662          | 0.536    |
+| 0.5102        | 8.0   | 382  | 0.7832          | 0.544    |
+| 0.4808        | 8.98  | 429  | 0.7898          | 0.53     |
+| 0.4698        | 9.84  | 470  | 0.7844          | 0.534    |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.13.1
+- Tokenizers 0.13.3