TrOCR-Ar-Small

gagan3012/TrOCR-Ar-Small

TrOCR-Ar-Small is a image-text-to-text model from gagan3012. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image-Text-to-Text
Library: transformers
Model type: vision-encoder-decoder
Access: Public
Created: Mar 21, 2022
Updated: Mar 21, 2022
SHA: 0fcc2872

Model card

From the Hugging Face model README

TrOCR-Ar-Small

This model is a fine-tuned version of microsoft/trocr-small-stage1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.2771
Cer: 0.8211

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: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
3.6363	0.14	1000	2.7594	0.9370
2.7508	0.29	2000	2.6589	0.8901
2.6519	0.43	3000	2.6059	0.8647
2.5936	0.57	4000	2.5360	0.7941
2.5069	0.72	5000	2.4701	0.8262
2.4606	0.86	6000	2.4427	0.7552
2.4046	1.0	7000	2.4262	0.7822
2.3628	1.15	8000	2.3880	0.8186
2.3458	1.29	9000	2.3589	0.8262
2.3062	1.43	10000	2.3704	0.8693
2.2884	1.58	11000	2.3065	0.8034
2.263	1.72	12000	2.3413	0.8545
2.2473	1.86	13000	2.3314	0.7996
2.2318	2.01	14000	2.3034	0.8254
2.2004	2.15	15000	2.3068	0.8461
2.1774	2.29	16000	2.2799	0.8207
2.1684	2.44	17000	2.2746	0.8249
2.1637	2.58	18000	2.2540	0.7797
2.1418	2.72	19000	2.2595	0.7937
2.1309	2.87	20000	2.2771	0.8211

Framework versions

Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
Languages: ar
Created: Mar 21, 2022
Updated: Mar 21, 2022