SFR-Embedding-2_R is a state-of-the-art text embedding model from Salesforce Research. It is designed for feature extraction and performs strongly on the MTEB benchmark. The model is compatible with sentence-transformers and the Hugging Face Transformers library, making it suitable for semantic search, RAG pipelines, and classification tasks.
SFR Embedding 2 R is a Foundation models & chat project. It focuses on generating high-quality dense embeddings for semantic search, retrieval, and classification without building custom embedding models. It is built as an open-source project for developers building RAG and semantic search systems. SFR Embedding 2 R is open source under the Open Source license. It runs on the web and API.
It is developed by Salesforce (United States), and it first shipped in 2024. Key capabilities include Feature Extraction, Sentence Transformers, and MTEB Evaluation.
Summary written by a language model from the project’s public pages.
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