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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Cognitive-Lab/ColNetraEmbed") model = AutoModelForMultimodalLM.from_pretrained("Cognitive-Lab/ColNetraEmbed") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -112,7 +112,7 @@ model-index:
|
|
| 112 |
[](https://github.com/adithya-s-k/colpali)
|
| 113 |
[](https://huggingface.co/Cognitive-Lab/ColNetraEmbed)
|
| 114 |
[](https://www.cognitivelab.in/blog/introducing-netraembed)
|
| 115 |
-
[](https://
|
| 116 |
|
| 117 |
|
| 118 |
**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.
|
|
|
|
| 112 |
[](https://github.com/adithya-s-k/colpali)
|
| 113 |
[](https://huggingface.co/Cognitive-Lab/ColNetraEmbed)
|
| 114 |
[](https://www.cognitivelab.in/blog/introducing-netraembed)
|
| 115 |
+
[](https://huggingface.co/spaces/AdithyaSK/NetraEmbed)
|
| 116 |
|
| 117 |
|
| 118 |
**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.
|