| | --- |
| | tags: |
| | - image-to-text |
| | language: ar |
| | model-index: |
| | - name: ArOCR |
| | results: |
| | - task: |
| | name: Optical Charater Recogntion |
| | type: image-to-text |
| | metrics: |
| | - name: Test CER |
| | type: cer |
| | value: 0.02 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # ArOCR |
| |
|
| | 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: 0.0407 |
| | - Cer: 0.0200 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Cer | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | | 1.6164 | 0.59 | 1000 | 1.4109 | 0.5793 | |
| | | 0.3434 | 1.18 | 2000 | 0.3876 | 0.2176 | |
| | | 0.1679 | 1.77 | 3000 | 0.2262 | 0.1186 | |
| | | 0.0816 | 2.37 | 4000 | 0.1274 | 0.0634 | |
| | | 0.0421 | 2.96 | 5000 | 0.0817 | 0.0381 | |
| | | 0.0067 | 3.55 | 6000 | 0.0520 | 0.0265 | |
| | | 0.0044 | 4.14 | 7000 | 0.0469 | 0.0215 | |
| | | 0.0027 | 4.73 | 8000 | 0.0407 | 0.0200 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.18.0 |
| | - Pytorch 1.9.1 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.11.6 |
| |
|
