text
Keep stacking cool stuff and getting better results! After I changed the standard vision encoder to SigLIP, NLLB-CLIP got a 10% average performance improvement. And now, I added matryoshka layers (
Keep stacking cool stuff and getting better results! After I changed the standard vision encoder to SigLIP, NLLB-CLIP got a 10% average performance improvement. And now, I added matryoshka layers (
link
https://arxiv.org/abs/2205.13147
https://arxiv.org/abs/2205.13147
text
) to enable smaller embeddings and got another 6% performance boost! Plus, thanks to MRL, 4.5x smaller embeddings retain 90%+ quality.
) to enable smaller embeddings and got another 6% performance boost! Plus, thanks to MRL, 4.5x smaller embeddings retain 90%+ quality.
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new_line
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The large model is finally SoTA for both image and text multilingual retrieval!
The large model is finally SoTA for both image and text multilingual retrieval!
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new_line
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The models are available on the hub:
The models are available on the hub:
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- 
resource
https://huggingface.co/visheratin/nllb-siglip-mrl-base
model
visheratin/nllb-siglip-mrl-base
https://huggingface.co/visheratin/nllb-siglip-mrl-base
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text
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- 
resource
https://huggingface.co/visheratin/nllb-siglip-mrl-large
model
visheratin/nllb-siglip-mrl-large
https://huggingface.co/visheratin/nllb-siglip-mrl-large
Keep stacking cool stuff and getting better results! After I changed the standard vision encoder to SigLIP, NLLB-CLIP got a 10% average performance improvement. And now, I added matryoshka layers (https://arxiv.org/abs/2205.13147) to enable smaller embeddings and got another 6% performance boost! Plus, thanks to MRL, 4.5x smaller embeddings retain 90%+ quality.

The large model is finally SoTA for both image and text multilingual retrieval!

The models are available on the hub:
- https://huggingface.co/visheratin/nllb-siglip-mrl-base
- https://huggingface.co/visheratin/nllb-siglip-mrl-large
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Alexander Visheratin
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