File size: 4,332 Bytes
f95807c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
---
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: 0.0429
- Wer Score: 1.9591
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3666 | 1.06 | 50 | 4.4430 | 21.5287 |
| 2.1581 | 2.13 | 100 | 0.2911 | 0.9783 |
| 0.0896 | 3.19 | 150 | 0.0328 | 0.3665 |
| 0.0269 | 4.26 | 200 | 0.0274 | 0.3487 |
| 0.0208 | 5.32 | 250 | 0.0284 | 0.4189 |
| 0.0168 | 6.38 | 300 | 0.0287 | 1.1673 |
| 0.0133 | 7.45 | 350 | 0.0296 | 6.0881 |
| 0.0106 | 8.51 | 400 | 0.0306 | 1.7969 |
| 0.0076 | 9.57 | 450 | 0.0322 | 7.1852 |
| 0.0053 | 10.64 | 500 | 0.0329 | 14.8889 |
| 0.0039 | 11.7 | 550 | 0.0338 | 12.2720 |
| 0.0027 | 12.77 | 600 | 0.0356 | 5.1533 |
| 0.0016 | 13.83 | 650 | 0.0371 | 8.4253 |
| 0.001 | 14.89 | 700 | 0.0379 | 6.7344 |
| 0.0006 | 15.96 | 750 | 0.0385 | 7.7586 |
| 0.0005 | 17.02 | 800 | 0.0392 | 9.0294 |
| 0.0004 | 18.09 | 850 | 0.0385 | 7.5083 |
| 0.0004 | 19.15 | 900 | 0.0394 | 5.1188 |
| 0.0004 | 20.21 | 950 | 0.0397 | 5.0600 |
| 0.0004 | 21.28 | 1000 | 0.0399 | 4.4125 |
| 0.0003 | 22.34 | 1050 | 0.0405 | 3.7803 |
| 0.0003 | 23.4 | 1100 | 0.0406 | 3.3397 |
| 0.0003 | 24.47 | 1150 | 0.0408 | 3.3218 |
| 0.0003 | 25.53 | 1200 | 0.0411 | 2.8212 |
| 0.0003 | 26.6 | 1250 | 0.0411 | 2.7165 |
| 0.0003 | 27.66 | 1300 | 0.0414 | 2.7625 |
| 0.0003 | 28.72 | 1350 | 0.0416 | 2.4330 |
| 0.0003 | 29.79 | 1400 | 0.0416 | 2.2350 |
| 0.0003 | 30.85 | 1450 | 0.0419 | 2.1699 |
| 0.0003 | 31.91 | 1500 | 0.0421 | 2.0026 |
| 0.0003 | 32.98 | 1550 | 0.0420 | 2.1609 |
| 0.0003 | 34.04 | 1600 | 0.0421 | 2.0307 |
| 0.0003 | 35.11 | 1650 | 0.0422 | 1.9668 |
| 0.0003 | 36.17 | 1700 | 0.0423 | 1.9387 |
| 0.0003 | 37.23 | 1750 | 0.0425 | 1.9464 |
| 0.0003 | 38.3 | 1800 | 0.0427 | 1.8761 |
| 0.0003 | 39.36 | 1850 | 0.0427 | 1.8940 |
| 0.0003 | 40.43 | 1900 | 0.0428 | 1.9068 |
| 0.0003 | 41.49 | 1950 | 0.0428 | 1.8774 |
| 0.0003 | 42.55 | 2000 | 0.0429 | 1.8352 |
| 0.0002 | 43.62 | 2050 | 0.0428 | 2.0907 |
| 0.0002 | 44.68 | 2100 | 0.0429 | 2.0319 |
| 0.0002 | 45.74 | 2150 | 0.0429 | 2.0179 |
| 0.0002 | 46.81 | 2200 | 0.0429 | 1.9706 |
| 0.0002 | 47.87 | 2250 | 0.0429 | 1.9604 |
| 0.0002 | 48.94 | 2300 | 0.0429 | 1.9540 |
| 0.0002 | 50.0 | 2350 | 0.0429 | 1.9591 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|