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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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model-index: |
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- name: git-base-pokemon |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# git-base-pokemon |
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This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co./microsoft/git-base) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0429 |
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- Wer Score: 1.9591 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer Score | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:| |
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| 7.3666 | 1.06 | 50 | 4.4430 | 21.5287 | |
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| 2.1581 | 2.13 | 100 | 0.2911 | 0.9783 | |
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| 0.0896 | 3.19 | 150 | 0.0328 | 0.3665 | |
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| 0.0269 | 4.26 | 200 | 0.0274 | 0.3487 | |
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| 0.0208 | 5.32 | 250 | 0.0284 | 0.4189 | |
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| 0.0168 | 6.38 | 300 | 0.0287 | 1.1673 | |
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| 0.0133 | 7.45 | 350 | 0.0296 | 6.0881 | |
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| 0.0106 | 8.51 | 400 | 0.0306 | 1.7969 | |
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| 0.0076 | 9.57 | 450 | 0.0322 | 7.1852 | |
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| 0.0053 | 10.64 | 500 | 0.0329 | 14.8889 | |
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| 0.0039 | 11.7 | 550 | 0.0338 | 12.2720 | |
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| 0.0027 | 12.77 | 600 | 0.0356 | 5.1533 | |
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| 0.0016 | 13.83 | 650 | 0.0371 | 8.4253 | |
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| 0.001 | 14.89 | 700 | 0.0379 | 6.7344 | |
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| 0.0006 | 15.96 | 750 | 0.0385 | 7.7586 | |
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| 0.0005 | 17.02 | 800 | 0.0392 | 9.0294 | |
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| 0.0004 | 18.09 | 850 | 0.0385 | 7.5083 | |
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| 0.0004 | 19.15 | 900 | 0.0394 | 5.1188 | |
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| 0.0004 | 20.21 | 950 | 0.0397 | 5.0600 | |
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| 0.0004 | 21.28 | 1000 | 0.0399 | 4.4125 | |
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| 0.0003 | 22.34 | 1050 | 0.0405 | 3.7803 | |
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| 0.0003 | 23.4 | 1100 | 0.0406 | 3.3397 | |
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| 0.0003 | 24.47 | 1150 | 0.0408 | 3.3218 | |
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| 0.0003 | 25.53 | 1200 | 0.0411 | 2.8212 | |
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| 0.0003 | 26.6 | 1250 | 0.0411 | 2.7165 | |
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| 0.0003 | 27.66 | 1300 | 0.0414 | 2.7625 | |
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| 0.0003 | 28.72 | 1350 | 0.0416 | 2.4330 | |
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| 0.0003 | 29.79 | 1400 | 0.0416 | 2.2350 | |
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| 0.0003 | 30.85 | 1450 | 0.0419 | 2.1699 | |
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| 0.0003 | 31.91 | 1500 | 0.0421 | 2.0026 | |
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| 0.0003 | 32.98 | 1550 | 0.0420 | 2.1609 | |
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| 0.0003 | 34.04 | 1600 | 0.0421 | 2.0307 | |
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| 0.0003 | 35.11 | 1650 | 0.0422 | 1.9668 | |
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| 0.0003 | 36.17 | 1700 | 0.0423 | 1.9387 | |
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| 0.0003 | 37.23 | 1750 | 0.0425 | 1.9464 | |
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| 0.0003 | 38.3 | 1800 | 0.0427 | 1.8761 | |
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| 0.0003 | 39.36 | 1850 | 0.0427 | 1.8940 | |
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| 0.0003 | 40.43 | 1900 | 0.0428 | 1.9068 | |
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| 0.0003 | 41.49 | 1950 | 0.0428 | 1.8774 | |
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| 0.0003 | 42.55 | 2000 | 0.0429 | 1.8352 | |
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| 0.0002 | 43.62 | 2050 | 0.0428 | 2.0907 | |
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| 0.0002 | 44.68 | 2100 | 0.0429 | 2.0319 | |
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| 0.0002 | 45.74 | 2150 | 0.0429 | 2.0179 | |
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| 0.0002 | 46.81 | 2200 | 0.0429 | 1.9706 | |
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| 0.0002 | 47.87 | 2250 | 0.0429 | 1.9604 | |
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| 0.0002 | 48.94 | 2300 | 0.0429 | 1.9540 | |
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| 0.0002 | 50.0 | 2350 | 0.0429 | 1.9591 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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