|
--- |
|
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: 0.0396 |
|
- Wer Score: 6.0488 |
|
|
|
## 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: 50 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer Score | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:| |
|
| 7.3484 | 1.06 | 50 | 4.4320 | 10.6547 | |
|
| 2.1536 | 2.13 | 100 | 0.2910 | 1.8947 | |
|
| 0.0909 | 3.19 | 150 | 0.0322 | 0.3684 | |
|
| 0.0278 | 4.26 | 200 | 0.0275 | 0.3659 | |
|
| 0.0211 | 5.32 | 250 | 0.0271 | 0.8858 | |
|
| 0.0185 | 6.38 | 300 | 0.0267 | 0.6778 | |
|
| 0.0155 | 7.45 | 350 | 0.0272 | 7.8190 | |
|
| 0.0129 | 8.51 | 400 | 0.0279 | 3.2452 | |
|
| 0.0108 | 9.57 | 450 | 0.0280 | 15.0462 | |
|
| 0.0082 | 10.64 | 500 | 0.0291 | 10.0372 | |
|
| 0.0069 | 11.7 | 550 | 0.0303 | 15.1592 | |
|
| 0.0048 | 12.77 | 600 | 0.0321 | 15.4493 | |
|
| 0.0033 | 13.83 | 650 | 0.0322 | 16.2439 | |
|
| 0.0022 | 14.89 | 700 | 0.0350 | 17.7125 | |
|
| 0.0017 | 15.96 | 750 | 0.0340 | 16.8357 | |
|
| 0.0011 | 17.02 | 800 | 0.0354 | 16.8780 | |
|
| 0.0009 | 18.09 | 850 | 0.0351 | 17.3273 | |
|
| 0.0006 | 19.15 | 900 | 0.0364 | 16.4788 | |
|
| 0.0005 | 20.21 | 950 | 0.0368 | 15.4442 | |
|
| 0.0004 | 21.28 | 1000 | 0.0368 | 16.2336 | |
|
| 0.0004 | 22.34 | 1050 | 0.0375 | 14.1168 | |
|
| 0.0004 | 23.4 | 1100 | 0.0375 | 14.4365 | |
|
| 0.0004 | 24.47 | 1150 | 0.0373 | 12.3890 | |
|
| 0.0004 | 25.53 | 1200 | 0.0379 | 8.7843 | |
|
| 0.0004 | 26.6 | 1250 | 0.0382 | 9.2298 | |
|
| 0.0003 | 27.66 | 1300 | 0.0383 | 8.8562 | |
|
| 0.0003 | 28.72 | 1350 | 0.0384 | 9.5777 | |
|
| 0.0003 | 29.79 | 1400 | 0.0383 | 8.6021 | |
|
| 0.0003 | 30.85 | 1450 | 0.0387 | 7.9782 | |
|
| 0.0003 | 31.91 | 1500 | 0.0387 | 7.7394 | |
|
| 0.0003 | 32.98 | 1550 | 0.0388 | 7.6431 | |
|
| 0.0003 | 34.04 | 1600 | 0.0389 | 6.9037 | |
|
| 0.0003 | 35.11 | 1650 | 0.0391 | 6.8665 | |
|
| 0.0003 | 36.17 | 1700 | 0.0392 | 6.0526 | |
|
| 0.0003 | 37.23 | 1750 | 0.0394 | 5.6996 | |
|
| 0.0003 | 38.3 | 1800 | 0.0393 | 6.1361 | |
|
| 0.0003 | 39.36 | 1850 | 0.0394 | 5.9127 | |
|
| 0.0003 | 40.43 | 1900 | 0.0394 | 5.6816 | |
|
| 0.0003 | 41.49 | 1950 | 0.0394 | 5.3723 | |
|
| 0.0003 | 42.55 | 2000 | 0.0395 | 4.8806 | |
|
| 0.0002 | 43.62 | 2050 | 0.0395 | 6.9178 | |
|
| 0.0002 | 44.68 | 2100 | 0.0395 | 6.2953 | |
|
| 0.0002 | 45.74 | 2150 | 0.0395 | 6.1142 | |
|
| 0.0002 | 46.81 | 2200 | 0.0396 | 6.0642 | |
|
| 0.0002 | 47.87 | 2250 | 0.0396 | 6.0077 | |
|
| 0.0002 | 48.94 | 2300 | 0.0396 | 6.0026 | |
|
| 0.0002 | 50.0 | 2350 | 0.0396 | 6.0488 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.2 |
|
- Pytorch 2.0.1+cu117 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
|
|
