Visual Document Retrieval
Transformers
Safetensors
gemma3
image-text-to-text
vision-language
retrieval
multimodal
multilingual
document-retrieval
matryoshka-embeddings
dense-retrieval
22-languages
Eval Results (legacy)
text-generation-inference
Instructions to use Cognitive-Lab/NetraEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cognitive-Lab/NetraEmbed with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Cognitive-Lab/NetraEmbed") model = AutoModelForImageTextToText.from_pretrained("Cognitive-Lab/NetraEmbed") - Notebooks
- Google Colab
- Kaggle
File size: 570 Bytes
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"do_normalize": true,
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"image_processor_type": "Gemma3ImageProcessor",
"image_seq_length": 256,
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"pan_and_scan_max_num_crops": null,
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"processor_class": "Gemma3Processor",
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