| | --- |
| | base_model: kavg/TrOCR-SIN-DeiT |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: TrOCR-SIN-DeiT-Handwritten |
| | 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-Handwritten |
| |
|
| | This model is a fine-tuned version of [kavg/TrOCR-SIN-DeiT](https://huggingface.co/kavg/TrOCR-SIN-DeiT) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.9839 |
| | - Cer: 0.5253 |
| |
|
| | ## 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: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 100 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Cer | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | | 0.2915 | 3.45 | 100 | 1.8613 | 0.6450 | |
| | | 0.061 | 6.9 | 200 | 1.8118 | 0.5707 | |
| | | 0.0363 | 10.34 | 300 | 2.3998 | 0.6420 | |
| | | 0.0202 | 13.79 | 400 | 2.4144 | 0.6353 | |
| | | 0.0329 | 17.24 | 500 | 2.4393 | 0.6577 | |
| | | 0.0364 | 20.69 | 600 | 1.9231 | 0.5679 | |
| | | 0.004 | 24.14 | 700 | 2.4344 | 0.5866 | |
| | | 0.0167 | 27.59 | 800 | 3.0998 | 0.5744 | |
| | | 0.0269 | 31.03 | 900 | 2.6785 | 0.5804 | |
| | | 0.0151 | 34.48 | 1000 | 2.2443 | 0.5916 | |
| | | 0.0008 | 37.93 | 1100 | 2.1480 | 0.5684 | |
| | | 0.0067 | 41.38 | 1200 | 2.3553 | 0.5625 | |
| | | 0.0198 | 44.83 | 1300 | 2.1915 | 0.5492 | |
| | | 0.0002 | 48.28 | 1400 | 2.0370 | 0.5620 | |
| | | 0.001 | 51.72 | 1500 | 2.4303 | 0.6056 | |
| | | 0.1666 | 55.17 | 1600 | 2.3324 | 0.5627 | |
| | | 0.0001 | 58.62 | 1700 | 2.8753 | 0.5582 | |
| | | 0.0 | 62.07 | 1800 | 2.5749 | 0.5355 | |
| | | 0.0002 | 65.52 | 1900 | 2.8105 | 0.5572 | |
| | | 0.0 | 68.97 | 2000 | 2.5275 | 0.5462 | |
| | | 0.1231 | 72.41 | 2100 | 2.7452 | 0.5477 | |
| | | 0.0 | 75.86 | 2200 | 2.4278 | 0.5403 | |
| | | 0.0 | 79.31 | 2300 | 3.0099 | 0.5487 | |
| | | 0.0 | 82.76 | 2400 | 3.1290 | 0.5467 | |
| | | 0.0 | 86.21 | 2500 | 2.7705 | 0.5263 | |
| | | 0.0 | 89.66 | 2600 | 2.7828 | 0.5275 | |
| | | 0.0 | 93.1 | 2700 | 3.2488 | 0.5345 | |
| | | 0.0 | 96.55 | 2800 | 3.1309 | 0.5273 | |
| | | 0.0 | 100.0 | 2900 | 2.9839 | 0.5253 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.1 |
| |
|
