heb-shortcrop-verifier

cyttic/heb-shortcrop-verifier

heb-shortcrop-verifier 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.

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: Jul 11, 2026
Updated: Jul 11, 2026
SHA: f66bb59b

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Base models

cyttic/exp23-directfit-unfrozen

Model card

From the Hugging Face model README

heb-shortcrop-verifier

This model is a fine-tuned version of cyttic/exp23-directfit-unfrozen on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0667
Cer: 0.0269
Exact: 0.9527

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: 1e-05
train_batch_size: 32
eval_batch_size: 64
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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Exact
0.2816	0.4305	1000	0.1008	0.0400	0.9307
0.1388	0.8610	2000	0.0716	0.0304	0.948
0.1454	1.0	2323	0.0667	0.0269	0.9527

Framework versions

Transformers 5.13.1
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
Created: Jul 11, 2026
Updated: Jul 11, 2026