---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: working_dir
  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. -->

# working_dir

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.3083
- Wer Score: {'bleu': 0.002242953743170335, 'precisions': [0.00878409616273694, 0.004012964963728971, 0.001545833977430824, 0.00046446818392940084], 'brevity_penalty': 1.0, 'length_ratio': 68.30526315789474, 'translation_length': 6489, 'reference_length': 95}

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Score                                                                                                                                                                                                                                                 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 7.9926        | 0.1   | 1    | 7.8580          | {'bleu': 0.0, 'precisions': [0.00648248186448526, 0.0017004173751739063, 0.0006192909119058678, 0.0], 'brevity_penalty': 1.0, 'length_ratio': 68.2, 'translation_length': 6479, 'reference_length': 95}                                                   |
| 7.8988        | 0.2   | 2    | 7.7407          | {'bleu': 0.0, 'precisions': [0.008140531276778063, 0.002717391304347826, 0.0007161271841879118, 0.0], 'brevity_penalty': 1.0, 'length_ratio': 73.70526315789473, 'translation_length': 7002, 'reference_length': 95}                                      |
| 7.8036        | 0.3   | 3    | 7.6263          | {'bleu': 0.0, 'precisions': [0.008062234794908063, 0.002974504249291785, 0.0005673758865248227, 0.0], 'brevity_penalty': 1.0, 'length_ratio': 74.42105263157895, 'translation_length': 7070, 'reference_length': 95}                                      |
| 7.7237        | 0.4   | 4    | 7.5370          | {'bleu': 0.0, 'precisions': [0.008338044092707745, 0.003538069629210303, 0.0005668934240362812, 0.0], 'brevity_penalty': 1.0, 'length_ratio': 74.48421052631579, 'translation_length': 7076, 'reference_length': 95}                                      |
| 7.5959        | 0.5   | 5    | 7.4688          | {'bleu': 0.001689755477270402, 'precisions': [0.008193247633846589, 0.0035365681143018812, 0.0009916418756197763, 0.0002837281883955171], 'brevity_penalty': 1.0, 'length_ratio': 74.51578947368421, 'translation_length': 7079, 'reference_length': 95}  |
| 7.545         | 0.6   | 6    | 7.4154          | {'bleu': 0.0016910162898086155, 'precisions': [0.008244994110718492, 0.0032438808611029196, 0.0010336680448907265, 0.0002957704821058858], 'brevity_penalty': 1.0, 'length_ratio': 71.49473684210527, 'translation_length': 6792, 'reference_length': 95} |
| 7.5008        | 0.7   | 7    | 7.3736          | {'bleu': 0.0027244361260593537, 'precisions': [0.011021452469986223, 0.004732794320646815, 0.0017783046828689982, 0.000593941793704217], 'brevity_penalty': 1.0, 'length_ratio': 53.48421052631579, 'translation_length': 5081, 'reference_length': 95}   |
| 7.4952        | 0.8   | 8    | 7.3412          | {'bleu': 0.0026505451685217172, 'precisions': [0.010477941176470587, 0.004604051565377533, 0.0018450184501845018, 0.00055452865064695], 'brevity_penalty': 1.0, 'length_ratio': 57.26315789473684, 'translation_length': 5440, 'reference_length': 95}    |
| 7.4316        | 0.9   | 9    | 7.3194          | {'bleu': 0.0023042253386690104, 'precisions': [0.009205426356589148, 0.0038822387576835974, 0.0016202203499675956, 0.0004868549172346641], 'brevity_penalty': 1.0, 'length_ratio': 65.17894736842105, 'translation_length': 6192, 'reference_length': 95} |
| 7.4141        | 1.0   | 10   | 7.3083          | {'bleu': 0.002242953743170335, 'precisions': [0.00878409616273694, 0.004012964963728971, 0.001545833977430824, 0.00046446818392940084], 'brevity_penalty': 1.0, 'length_ratio': 68.30526315789474, 'translation_length': 6489, 'reference_length': 95}    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2