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# vit-base-newspaper_for_segmetation_classifier

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dugerij/newspaper_parser_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0104
- Accuracy: 1.0

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0429        | 1.0   | 100  | 0.0409          | 0.9929   |
| 0.0159        | 2.0   | 200  | 0.0181          | 1.0      |
| 0.0113        | 3.0   | 300  | 0.0139          | 1.0      |
| 0.0119        | 4.0   | 400  | 0.0112          | 1.0      |
| 0.0093        | 5.0   | 500  | 0.0104          | 1.0      |


### Framework versions

- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0

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