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README.md
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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: 0.0371
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- Wer Score: 2.4731
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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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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| 7.3268 | 2.13 | 50 | 4.4847 | 21.4974 |
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| 2.2464 | 4.26 | 100 | 0.3519 | 11.4118 |
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| 0.1049 | 6.38 | 150 | 0.0302 | 0.7468 |
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| 0.0223 | 8.51 | 200 | 0.0270 | 0.4668 |
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| 0.0137 | 10.64 | 250 | 0.0280 | 3.5742 |
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| 0.0073 | 12.77 | 300 | 0.0304 | 7.1240 |
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| 0.0034 | 14.89 | 350 | 0.0309 | 6.4885 |
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| 0.0018 | 17.02 | 400 | 0.0326 | 5.0499 |
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| 0.0011 | 19.15 | 450 | 0.0335 | 5.2302 |
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| 0.0009 | 21.28 | 500 | 0.0342 | 4.3645 |
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| 0.0007 | 23.4 | 550 | 0.0346 | 5.1445 |
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| 0.0006 | 25.53 | 600 | 0.0351 | 4.0639 |
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| 0.0006 | 27.66 | 650 | 0.0355 | 3.8862 |
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| 0.0006 | 29.79 | 700 | 0.0359 | 3.4514 |
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| 0.0006 | 31.91 | 750 | 0.0363 | 3.0486 |
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| 0.0006 | 34.04 | 800 | 0.0363 | 2.8645 |
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| 0.0006 | 36.17 | 850 | 0.0366 | 2.7199 |
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| 0.0006 | 38.3 | 900 | 0.0369 | 2.6675 |
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| 0.0006 | 40.43 | 950 | 0.0369 | 2.6304 |
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| 0.0006 | 42.55 | 1000 | 0.0370 | 2.4910 |
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| 0.0006 | 44.68 | 1050 | 0.0370 | 2.4834 |
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| 0.0006 | 46.81 | 1100 | 0.0371 | 2.4629 |
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| 0.0006 | 48.94 | 1150 | 0.0371 | 2.4731 |
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### Framework versions
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- Transformers 4.29.1
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- Pytorch 1.12.1
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- Datasets 2.11.0
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- Tokenizers 0.11.0
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