Models & Labs
Granite Embedding R2: New Multilingual Models Released
Hugging Face Bloghigh confidence
Why it matters
- →The 97M model sets a new benchmark for sub-100M multilingual embeddings.
- →The models support over 200 languages, enhancing cross-lingual capabilities.
- →They offer a 32K-token context window, significantly improving long-sequence processing.
Hugging Face has released two new multilingual embedding models under the Apache 2.0 license. The Granite Embedding Multilingual R2 models include a 97M-parameter compact model and a 311M full-size model, both supporting over 200 languages. The compact model achieves the highest retrieval score for any open multilingual model under 100M parameters, while the full-size model ranks second among models under 500M parameters. These models are designed for broad language coverage and high retrieval quality, making them suitable for diverse multilingual and code retrieval tasks.
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