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---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: git-base-food
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-food
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: 0.0444
- Wer Score: 10.0470
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| No log | 1.05 | 20 | 7.3069 | 113.3758 |
| No log | 2.11 | 40 | 5.3566 | 3.2282 |
| No log | 3.16 | 60 | 3.4409 | 1.1879 |
| No log | 4.21 | 80 | 1.7218 | 1.1007 |
| No log | 5.26 | 100 | 0.5834 | 1.0872 |
| No log | 6.32 | 120 | 0.1684 | 1.3020 |
| No log | 7.37 | 140 | 0.0720 | 2.9732 |
| No log | 8.42 | 160 | 0.0507 | 2.0805 |
| No log | 9.47 | 180 | 0.0467 | 3.0336 |
| No log | 10.53 | 200 | 0.0415 | 10.6107 |
| No log | 11.58 | 220 | 0.0425 | 7.7383 |
| No log | 12.63 | 240 | 0.0426 | 14.1745 |
| No log | 13.68 | 260 | 0.0434 | 6.0067 |
| No log | 14.74 | 280 | 0.0447 | 10.5503 |
| No log | 15.79 | 300 | 0.0434 | 9.1678 |
| No log | 16.84 | 320 | 0.0439 | 10.8591 |
| No log | 17.89 | 340 | 0.0446 | 10.0470 |
| No log | 18.95 | 360 | 0.0444 | 10.1208 |
| No log | 20.0 | 380 | 0.0444 | 10.0470 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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