| | --- |
| | base_model: kavg/TrOCR-SIN-DeiT |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: TrOCR_Printed_Sinahala_1 |
| | 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_Printed_Sinahala_1 |
| | |
| | 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: 0.6949 |
| | - Cer: 0.1689 |
| | |
| | ## 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: 4 |
| | - eval_batch_size: 4 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - training_steps: 1000 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Cer | |
| | |:-------------:|:------:|:----:|:---------------:|:------:| |
| | | 0.0989 | 0.0444 | 100 | 1.0041 | 0.1787 | |
| | | 0.1265 | 0.0889 | 200 | 0.7479 | 0.2270 | |
| | | 0.0302 | 0.1333 | 300 | 0.9396 | 0.1863 | |
| | | 0.0483 | 0.1778 | 400 | 0.6949 | 0.1689 | |
| | | 0.0637 | 0.2222 | 500 | 0.6022 | 0.1816 | |
| | | 0.0726 | 0.2667 | 600 | 0.6801 | 0.2083 | |
| | | 0.0169 | 0.3111 | 700 | 0.5828 | 0.1721 | |
| | | 0.0084 | 0.3556 | 800 | 0.5437 | 0.1750 | |
| | | 0.0239 | 0.4 | 900 | 0.5451 | 0.1744 | |
| | | 0.0215 | 0.4444 | 1000 | 0.5470 | 0.1717 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.41.2 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| | |
