trocr-bigram2-BY

cyttic/trocr-bigram2-BY

trocr-bigram2-BY is a image-text-to-text model from cyttic. 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. The card lists the license as mit.

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

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At a glance

Task: Image-Text-to-Text
Library: transformers
License: mit
Model type: vision-encoder-decoder
Access: Public
Created: Aug 27, 2026
Updated: Oct 30, 2026
SHA: 23d62cff

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Base models

cyttic/exp2-frozen-benyehuda-cont

Model card

From the Hugging Face model README

trocr-bigram2-BY

This model is a fine-tuned version of cyttic/exp2-frozen-benyehuda-cont on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5801
Cer: 0.0300
Wer: 0.0860

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 4650
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
3.6979	0.1290	2000	1.5914	0.1658	0.3630
3.2238	0.2581	4000	1.4171	0.1338	0.2922
2.7787	0.3871	6000	1.2767	0.1030	0.2509
2.4498	0.5161	8000	1.1516	0.0866	0.2204
2.3069	0.6452	10000	1.0718	0.0751	0.1970
2.1897	0.7742	12000	0.9861	0.0625	0.1723
2.0271	0.9032	14000	0.9251	0.0590	0.1590
1.6597	1.0323	16000	0.8667	0.0531	0.1460
1.6426	1.1613	18000	0.8350	0.0526	0.1415
1.5105	1.2903	20000	0.7896	0.0452	0.1306
1.4093	1.4194	22000	0.7645	0.0434	0.1275
1.3882	1.5484	24000	0.7459	0.0432	0.1236
1.2272	1.6774	26000	0.7153	0.0412	0.1202
1.2902	1.8065	28000	0.6809	0.0382	0.1091
1.2436	1.9355	30000	0.6629	0.0363	0.1068
1.0390	2.0645	32000	0.6506	0.0326	0.0971
0.9677	2.1935	34000	0.6366	0.0333	0.0984
0.9556	2.3226	36000	0.6188	0.0314	0.0937
0.9923	2.4516	38000	0.6087	0.0320	0.0915
0.9561	2.5806	40000	0.5991	0.0333	0.0946
1.0060	2.7097	42000	0.5879	0.0290	0.0865
0.9450	2.8387	44000	0.5832	0.0290	0.0848
0.9546	2.9677	46000	0.5803	0.0301	0.0864
0.9141	3.0	46500	0.5801	0.0300	0.0860

Framework versions

Transformers 5.15.0
Pytorch 2.11.0+cu128
Datasets 5.0.1
Tokenizers 0.22.2

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
License: mit
Created: Aug 27, 2026
Updated: Oct 30, 2026