Granite Embedding 97M Multilingual R2 Model Summary: Granite Embedding 97M Multilingual R2 is a 97M parameter dense embedding model from the Granite Embeddings collection for high quality multilingual text embeddings at minimal compute cost. It produces 384 dimensional vectors with a context length of up to 32,768 tokens. The model supports 200+ languages (based on the multilingual pretraining corpus of the underlying encoder), with enhanced support for 52 languages and programming code that receive explicit retrieval pair and cross lingual training. All training data uses permissive, enterprise friendly licenses, plus IBM collected and IBM generated datasets. Granite Embedding 97M Multilingual R2 scores 60.3 on Multilingual MTEB Retrieval (18 tasks) — the highest retrieval score of any open multilingual embedding model under 100M parameters, outperforming the next best model in its size class (multilingual e5 small at 50.9) by +9.4 points — while being roughly 3× smaller than the full size granite embedding 311m multilingual r2. The multilingual R2 model shows strong performance across multilingual information retrieval benchmarks, code retrieval, long document search, conversatio…
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