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exp28-connected-stage1-frozen
cyttic/exp28-connected-stage1-frozen
exp28-connected-stage1-frozen 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
- Jul 15, 2026
- Updated
- Oct 30, 2026
- SHA
- af67da49
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
exp28-connected-stage1-frozen
This model is a fine-tuned version of cyttic/trocr-hebrew-untrained on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1992
- Cer: 0.6595
- Wer: 0.9530
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: 16
- eval_batch_size: 16
- seed: 42
- 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: 0.05
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 5.4750 | 0.0499 | 2500 | 5.2921 | 0.7780 | 1.0498 |
| 5.1373 | 0.0997 | 5000 | 5.0355 | 0.8212 | 1.1426 |
| 4.9213 | 0.1496 | 7500 | 4.8952 | 0.8017 | 1.1251 |
| 4.8041 | 0.1995 | 10000 | 4.7412 | 0.7949 | 1.0785 |
| 4.6828 | 0.2493 | 12500 | 4.5593 | 0.7600 | 1.0510 |
| 4.5887 | 0.2992 | 15000 | 4.4055 | 0.7627 | 1.0710 |
| 4.3930 | 0.3491 | 17500 | 4.2588 | 0.7550 | 1.0665 |
| 4.1659 | 0.3989 | 20000 | 4.1075 | 0.7415 | 1.0555 |
| 4.1116 | 0.4488 | 22500 | 3.9941 | 0.7189 | 1.0115 |
| 3.9639 | 0.4987 | 25000 | 3.8622 | 0.7154 | 1.0110 |
| 3.9089 | 0.5485 | 27500 | 3.7470 | 0.7088 | 1.0091 |
| 3.7613 | 0.5984 | 30000 | 3.6414 | 0.6989 | 1.0056 |
| 3.7173 | 0.6483 | 32500 | 3.5645 | 0.6847 | 0.9785 |
| 3.5710 | 0.6981 | 35000 | 3.4901 | 0.6780 | 0.9697 |
| 3.5911 | 0.7480 | 37500 | 3.4246 | 0.6696 | 0.9554 |
| 3.4808 | 0.7979 | 40000 | 3.3439 | 0.6725 | 0.9748 |
| 3.4458 | 0.8477 | 42500 | 3.2941 | 0.6674 | 0.9600 |
| 3.3879 | 0.8976 | 45000 | 3.2523 | 0.6622 | 0.9569 |
| 3.3574 | 0.9475 | 47500 | 3.2160 | 0.6560 | 0.9540 |
| 3.3047 | 0.9973 | 50000 | 3.1991 | 0.6590 | 0.9521 |
| 3.3368 | 1.0 | 50133 | 3.1992 | 0.6595 | 0.9530 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- mit
- Created
- Jul 15, 2026
- Updated
- Oct 30, 2026