Popular ~100-language instruction-tuned embedding model built on XLM-RoBERTa-large.
A strong 0.56B-parameter dense embedding model from intfloat (Microsoft Research). Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing.
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A 560M-parameter multilingual embedding model from Microsoft Research's intfloat, initialized from XLM-RoBERTa-large and trained in two stages, weakly-supervised contrastive pre-training on 1B pairs followed by instruction-tuned fine-tuning using E5-Mistral synthetic data. Supports ~100 languages and uses natural-language task instructions to customize query embeddings.
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