|
--- |
|
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 |
|
|
