| --- |
| 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: 0.0407 |
| - Wer Score: 2.1746 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:| |
| | 7.3192 | 2.13 | 50 | 4.4908 | 21.3506 | |
| | 2.2486 | 4.26 | 100 | 0.3526 | 10.3803 | |
| | 0.1047 | 6.38 | 150 | 0.0321 | 0.3635 | |
| | 0.0222 | 8.51 | 200 | 0.0298 | 0.6636 | |
| | 0.0145 | 10.64 | 250 | 0.0286 | 2.1759 | |
| | 0.0081 | 12.77 | 300 | 0.0321 | 2.9690 | |
| | 0.004 | 14.89 | 350 | 0.0340 | 2.2962 | |
| | 0.002 | 17.02 | 400 | 0.0356 | 2.1837 | |
| | 0.0012 | 19.15 | 450 | 0.0370 | 3.1501 | |
| | 0.0009 | 21.28 | 500 | 0.0379 | 2.5821 | |
| | 0.0007 | 23.4 | 550 | 0.0382 | 2.7995 | |
| | 0.0006 | 25.53 | 600 | 0.0386 | 2.8318 | |
| | 0.0006 | 27.66 | 650 | 0.0387 | 2.4541 | |
| | 0.0006 | 29.79 | 700 | 0.0390 | 2.6404 | |
| | 0.0006 | 31.91 | 750 | 0.0395 | 2.5614 | |
| | 0.0006 | 34.04 | 800 | 0.0395 | 2.5317 | |
| | 0.0006 | 36.17 | 850 | 0.0399 | 2.5886 | |
| | 0.0006 | 38.3 | 900 | 0.0403 | 2.3829 | |
| | 0.0006 | 40.43 | 950 | 0.0404 | 2.2937 | |
| | 0.0006 | 42.55 | 1000 | 0.0404 | 2.2173 | |
| | 0.0006 | 44.68 | 1050 | 0.0406 | 2.1617 | |
| | 0.0006 | 46.81 | 1100 | 0.0406 | 2.1669 | |
| | 0.0006 | 48.94 | 1150 | 0.0407 | 2.1746 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.27.4 |
| - Pytorch 1.13.1 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
| |
