---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: git-base-pokemon
  results: []
---

<!-- 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. -->

# git-base-pokemon

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0345
- Wer Score: 2.4097

## 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: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3695        | 4.17  | 50   | 4.5700          | 21.4160   |
| 2.3984        | 8.33  | 100  | 0.4696          | 10.9249   |
| 0.1439        | 12.5  | 150  | 0.0305          | 1.1692    |
| 0.02          | 16.67 | 200  | 0.0263          | 1.5229    |
| 0.0084        | 20.83 | 250  | 0.0295          | 2.6539    |
| 0.003         | 25.0  | 300  | 0.0324          | 3.2125    |
| 0.0018        | 29.17 | 350  | 0.0329          | 2.6628    |
| 0.0014        | 33.33 | 400  | 0.0336          | 2.5407    |
| 0.0013        | 37.5  | 450  | 0.0338          | 2.4008    |
| 0.0011        | 41.67 | 500  | 0.0344          | 2.5115    |
| 0.0011        | 45.83 | 550  | 0.0344          | 2.3766    |
| 0.0011        | 50.0  | 600  | 0.0345          | 2.4097    |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2