Visual Document Retrieval
Transformers
Safetensors
vision-language
retrieval
multimodal
multilingual
document-retrieval
matryoshka-embeddings
dense-retrieval
22-languages
Eval Results (legacy)
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 AutoModel model = AutoModel.from_pretrained("Cognitive-Lab/NetraEmbed", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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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/NetraEmbed/blob/main/NetraEmbed_InferenceDemo.ipynb)
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**NetraEmbed** is a state-of-the-art multilingual multimodal embedding model for visual document retrieval with Matryoshka representation learning, powered by the Gemma3 backbone.
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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/NetraEmbed/blob/main/NetraEmbed_InferenceDemo.ipynb)
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[](https://huggingface.co/Cognitive-Lab/NetraEmbed/blob/main/NetraEmbed_Gradio_Demo_final.ipynb)
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**NetraEmbed** is a state-of-the-art multilingual multimodal embedding model for visual document retrieval with Matryoshka representation learning, powered by the Gemma3 backbone.
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