| --- |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: TrOCR-SIN(DeiT) |
| 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. --> |
|
|
| # TrOCR-SIN(DeiT) |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4335 |
| - Cer: 0.1445 |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
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| More information needed |
|
|
| ## Training and evaluation data |
|
|
| 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: |
| - learning_rate: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - training_steps: 75000 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Cer | Validation Loss | |
| |:-------------:|:-----:|:-----:|:------:|:---------------:| |
| | 1.3019 | 1.78 | 5000 | 0.6416 | 1.7769 | |
| | 0.6387 | 3.55 | 10000 | 0.4048 | 0.8457 | |
| | 0.3402 | 5.33 | 15000 | 0.2808 | 0.6898 | |
| | 0.1332 | 7.11 | 20000 | 0.2377 | 0.5765 | |
| | 0.1141 | 8.89 | 25000 | 0.2223 | 0.4460 | |
| | 0.0481 | 10.66 | 30000 | 0.1868 | 0.4128 | |
| | 0.0391 | 12.44 | 35000 | 0.1563 | 0.4172 | |
| | 0.0357 | 14.22 | 40000 | 0.1981 | 0.4756 | |
| | 0.0215 | 16.0 | 45000 | 0.1983 | 0.5838 | |
| | 0.0129 | 17.77 | 50000 | 0.1757 | 0.5511 | |
| | 0.0087 | 19.55 | 55000 | 0.1699 | 0.5568 | |
| | 0.003 | 21.33 | 60000 | 0.1648 | 0.4532 | |
| | 0.0042 | 23.11 | 65000 | 0.1582 | 0.4650 | |
| | 0.0066 | 24.88 | 70000 | 0.1654 | 0.4740 | |
| | 0.0014 | 26.66 | 75000 | 0.1448 | 0.4337 | |
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| ### Framework versions |
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|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.1 |
|
|
