Granite Embedding Small English R2 Model Summary: Granite embedding small english r2 is a 47M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 based on context length of upto 8192 tokens. Compared to most other open source models, this model was only trained using open source relevance pair datasets with permissive, enterprise friendly license, plus IBM collected and generated datasets. The r2 models show strong performance across standard and IBM built information retrieval benchmarks (BEIR, ClapNQ), code retrieval (COIR), long document search benchmarks (MLDR, LongEmbed), conversational multi turn (MTRAG), table retrieval (NQTables, OTT QA, AIT QA, MultiHierTT, OpenWikiTables), and on many enterprise use cases. These models use a bi encoder architecture to generate high quality embeddings from text inputs such as queries, passages, and documents, enabling seamless comparison through cosine similarity. Built using retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging, granite embedding small english…
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