Granite Embedding English R2 is an open-source text embedding model developed by IBM Granite. It is optimized for semantic similarity, retrieval, and representation learning tasks. The model integrates with sentence-transformers and can be used to encode sentences into dense vectors for downstream applications such as search and clustering.
Granite Embedding English R2 sits in PulseGate's Foundation models & chat category. It focuses on generating high-quality dense vector embeddings for English text retrieval and semantic search tasks. It is built as an open-source project for developers. Granite Embedding English R2 is open source under the Apache-2.0 license. The product ships for the web, the command line, and API, and it can be self-hosted.
IBM Granite builds and maintains Granite Embedding English R2, and the product first shipped in 2024. Development happens publicly on GitHub with 77 stars and 27 commits in the last 90 days. Key capabilities include Text Embeddings, Semantic Similarity, and MTEB Benchmark. It exposes integrations via a public API.
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