Visual Document Retrieval
Transformers
Safetensors
gemma3
image-text-to-text
vision-language
retrieval
colbert
late-interaction
multimodal
multilingual
document-retrieval
22-languages
Eval Results (legacy)
text-generation-inference
Instructions to use Cognitive-Lab/ColNetraEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cognitive-Lab/ColNetraEmbed with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Cognitive-Lab/ColNetraEmbed") model = AutoModelForImageTextToText.from_pretrained("Cognitive-Lab/ColNetraEmbed") - Notebooks
- Google Colab
- Kaggle
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[](https://www.cognitivelab.in/blog/introducing-netraembed)
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[](https://huggingface.co/spaces/AdithyaSK/NetraEmbed)
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[](https://huggingface.co/Cognitive-Lab/ColNetraEmbed/blob/main/ColNetraEmbed_InferenceDemo.ipynb)
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**ColNetraEmbed** is a state-of-the-art multilingual multimodal embedding model for visual document retrieval, powered by the Gemma3 backbone and using Colbert-style multi-vector representations.
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[](https://www.cognitivelab.in/blog/introducing-netraembed)
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[](https://huggingface.co/spaces/AdithyaSK/NetraEmbed)
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[](https://huggingface.co/Cognitive-Lab/ColNetraEmbed/blob/main/ColNetraEmbed_InferenceDemo.ipynb)
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[](https://huggingface.co/Cognitive-Lab/NetraEmbed/blob/main/NetraEmbed_Gradio_Demo_final.ipynb)
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**ColNetraEmbed** is a state-of-the-art multilingual multimodal embedding model for visual document retrieval, powered by the Gemma3 backbone and using Colbert-style multi-vector representations.
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