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nid-ocr-vit-mbart50-2
kavinh07/nid-ocr-vit-mbart50-2
nid-ocr-vit-mbart50-2 is a image-text-to-text model from kavinh07. 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 10, 2026
- Updated
- Mar 13, 2026
- SHA
- 95b423ac
Try a prompt
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. -->
nid-ocr-vit-mbart50-2
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 27.6543
- eval_cer: 8.3999
- eval_wer: 2.2781
- eval_runtime: 775.0458
- eval_samples_per_second: 0.516
- eval_steps_per_second: 0.032
- epoch: 0
- step: 0
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: 4
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 1000
Framework versions
- Transformers 4.54.1
- Pytorch 2.7.1+cu126
- Datasets 4.5.0
- Tokenizers 0.21.4
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Mar 10, 2026
- Updated
- Mar 13, 2026