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# 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

{"language": "ar", "tags": ["image-to-text"]}
transformers pytorch tensorboard vision-encoder-decoder image-to-text ar model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #image-to-text #ar #model-index #endpoints_compatible #has_space #region-us 

ArOCR
=====


This model is a fine-tuned version of [](URL 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



### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1
* Datasets 2.1.0
* Tokenizers 0.11.6



### 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1
* Datasets 2.1.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #image-to-text #ar #model-index #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1
* Datasets 2.1.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #image-to-text #ar #model-index #endpoints_compatible #has_space #region-us 
### 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### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1
* Datasets 2.1.0
* Tokenizers 0.11.6