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# vit-molecul

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5737
- Accuracy: 0.71
- F1: 0.7086

## 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: 3e-06
- train_batch_size: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.723         | 1.0   | 8    | 0.6790          | 0.61     | 0.6096 |
| 0.6915        | 2.0   | 16   | 0.6661          | 0.62     | 0.5924 |
| 0.6689        | 3.0   | 24   | 0.6470          | 0.69     | 0.6892 |
| 0.6517        | 4.0   | 32   | 0.6356          | 0.64     | 0.6377 |
| 0.6368        | 5.0   | 40   | 0.6289          | 0.72     | 0.7199 |
| 0.621         | 6.0   | 48   | 0.6217          | 0.73     | 0.7293 |
| 0.6061        | 7.0   | 56   | 0.6197          | 0.69     | 0.6862 |
| 0.5924        | 8.0   | 64   | 0.6087          | 0.73     | 0.7293 |
| 0.5767        | 9.0   | 72   | 0.6003          | 0.72     | 0.7199 |
| 0.5633        | 10.0  | 80   | 0.5953          | 0.72     | 0.7196 |
| 0.5491        | 11.0  | 88   | 0.5885          | 0.72     | 0.7199 |
| 0.5351        | 12.0  | 96   | 0.5869          | 0.71     | 0.7100 |
| 0.5239        | 13.0  | 104  | 0.5867          | 0.7      | 0.6995 |
| 0.5118        | 14.0  | 112  | 0.5804          | 0.71     | 0.7100 |
| 0.502         | 15.0  | 120  | 0.5752          | 0.71     | 0.7100 |
| 0.4942        | 16.0  | 128  | 0.5738          | 0.72     | 0.7199 |
| 0.4885        | 17.0  | 136  | 0.5771          | 0.71     | 0.7086 |
| 0.4831        | 18.0  | 144  | 0.5751          | 0.71     | 0.7086 |
| 0.4793        | 19.0  | 152  | 0.5743          | 0.71     | 0.7086 |
| 0.4774        | 20.0  | 160  | 0.5737          | 0.71     | 0.7086 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.1
- Tokenizers 0.13.3

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