---
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: 0.0371
- Wer Score: 2.4731

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3268        | 2.13  | 50   | 4.4847          | 21.4974   |
| 2.2464        | 4.26  | 100  | 0.3519          | 11.4118   |
| 0.1049        | 6.38  | 150  | 0.0302          | 0.7468    |
| 0.0223        | 8.51  | 200  | 0.0270          | 0.4668    |
| 0.0137        | 10.64 | 250  | 0.0280          | 3.5742    |
| 0.0073        | 12.77 | 300  | 0.0304          | 7.1240    |
| 0.0034        | 14.89 | 350  | 0.0309          | 6.4885    |
| 0.0018        | 17.02 | 400  | 0.0326          | 5.0499    |
| 0.0011        | 19.15 | 450  | 0.0335          | 5.2302    |
| 0.0009        | 21.28 | 500  | 0.0342          | 4.3645    |
| 0.0007        | 23.4  | 550  | 0.0346          | 5.1445    |
| 0.0006        | 25.53 | 600  | 0.0351          | 4.0639    |
| 0.0006        | 27.66 | 650  | 0.0355          | 3.8862    |
| 0.0006        | 29.79 | 700  | 0.0359          | 3.4514    |
| 0.0006        | 31.91 | 750  | 0.0363          | 3.0486    |
| 0.0006        | 34.04 | 800  | 0.0363          | 2.8645    |
| 0.0006        | 36.17 | 850  | 0.0366          | 2.7199    |
| 0.0006        | 38.3  | 900  | 0.0369          | 2.6675    |
| 0.0006        | 40.43 | 950  | 0.0369          | 2.6304    |
| 0.0006        | 42.55 | 1000 | 0.0370          | 2.4910    |
| 0.0006        | 44.68 | 1050 | 0.0370          | 2.4834    |
| 0.0006        | 46.81 | 1100 | 0.0371          | 2.4629    |
| 0.0006        | 48.94 | 1150 | 0.0371          | 2.4731    |


### Framework versions

- Transformers 4.29.1
- Pytorch 1.12.1
- Datasets 2.11.0
- Tokenizers 0.11.0