---
license: mit
tags:
- generated_from_trainer
base_model: microsoft/git-base
datasets:
- imagefolder
model-index:
- name: git-base-captioning
  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-captioning

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: 0.3853
- Wer Score: 0.8299

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Score |
|:-------------:|:------:|:----:|:---------------:|:---------:|
| 1.6087        | 0.4202 | 50   | 0.4992          | 0.8373    |
| 0.435         | 0.8403 | 100  | 0.3918          | 0.8254    |
| 0.3572        | 1.2605 | 150  | 0.3769          | 0.8254    |
| 0.3207        | 1.6807 | 200  | 0.3699          | 0.8130    |
| 0.2761        | 2.1008 | 250  | 0.3683          | 0.8130    |
| 0.2635        | 2.5210 | 300  | 0.3684          | 0.8102    |
| 0.2551        | 2.9412 | 350  | 0.3662          | 0.8350    |
| 0.225         | 3.3613 | 400  | 0.3681          | 0.8113    |
| 0.2364        | 3.7815 | 450  | 0.3683          | 0.8249    |
| 0.2091        | 4.2017 | 500  | 0.3720          | 3.0723    |
| 0.1939        | 4.6218 | 550  | 0.3740          | 0.8215    |
| 0.1839        | 5.0420 | 600  | 0.3723          | 1.8362    |
| 0.1713        | 5.4622 | 650  | 0.3769          | 0.8243    |
| 0.1664        | 5.8824 | 700  | 0.3753          | 0.8209    |
| 0.1634        | 6.3025 | 750  | 0.3786          | 0.8249    |
| 0.1444        | 6.7227 | 800  | 0.3805          | 0.8220    |
| 0.1536        | 7.1429 | 850  | 0.3807          | 0.8322    |
| 0.1469        | 7.5630 | 900  | 0.3832          | 0.8350    |
| 0.1303        | 7.9832 | 950  | 0.3834          | 0.8271    |
| 0.1341        | 8.4034 | 1000 | 0.3841          | 0.8232    |
| 0.1268        | 8.8235 | 1050 | 0.3836          | 0.8277    |
| 0.1348        | 9.2437 | 1100 | 0.3852          | 0.8260    |
| 0.1247        | 9.6639 | 1150 | 0.3853          | 0.8299    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1