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Nllb Siglip Mrl Large

Developed by visheratin
NLLB-SigLIP-MRL is a multilingual vision-language model that combines the text encoder from NLLB and the image encoder from SigLIP, supporting 201 languages from Flores-200.
Downloads 297
Release Time : 3/4/2024

Model Overview

This model integrates NLLB's text encoding capabilities with SigLIP's image encoding capabilities, trained through Matryoshka representation learning to support multiple embedding sizes, achieving state-of-the-art performance in multilingual image and text retrieval tasks.

Model Features

Multilingual support
Supports 201 languages from Flores-200, extending the model's multilingual capabilities.
Variable embedding sizes
Supports multiple embedding sizes [32, 64, 128, 256, 512], with 256 and 512 sizes retaining over 90% of full embedding quality.
State-of-the-art performance
Sets new state-of-the-art levels for multilingual image and text retrieval on the XTD10 and Crossmodal-3600 datasets.

Model Capabilities

Multilingual image classification
Multilingual text retrieval
Multilingual image retrieval
Zero-shot learning

Use Cases

Multilingual content retrieval
Cross-language image search
Retrieve relevant images using text queries in different languages
Achieves image retrieval R@1 of 0.6079 on the Crossmodal-3600 dataset
Multilingual image classification
Classify images using labels in different languages
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