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# vit-base-patch16-224-finetuned-hateful-meme-restructured

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: 0.7152
- Accuracy: 0.552

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6546        | 0.99  | 66   | 0.7185          | 0.52     |
| 0.6222        | 2.0   | 133  | 0.7152          | 0.552    |
| 0.5986        | 2.99  | 199  | 0.7344          | 0.542    |
| 0.5535        | 4.0   | 266  | 0.7782          | 0.514    |
| 0.5377        | 4.99  | 332  | 0.8329          | 0.514    |
| 0.5115        | 6.0   | 399  | 0.7596          | 0.528    |
| 0.5133        | 6.99  | 465  | 0.8151          | 0.512    |
| 0.511         | 8.0   | 532  | 0.7897          | 0.538    |
| 0.4712        | 8.99  | 598  | 0.8539          | 0.514    |
| 0.4626        | 9.92  | 660  | 0.8449          | 0.522    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3

hate nohate