CPU Optimized Embeddings with 🤗 Optimum Intel and fastRAG
Embedding models are useful for many applications such as retrieval, reranking, clustering, and classification. The research community has witnessed significant advancements in recent years in embedding models, leading to substantial enhancements in all applications building on semantic representation. Models such as BGE, GTE, and E5 are placed at the top of the MTEB benchmark and in some cases outperform proprietary embedding services. There are a variety of model sizes found in Hugging Face’s Model hub, from lightweight (100-350M parameters) to 7B models (such as Salesforce/SFR-Embedding-Mistral). The lightweight models based on an encoder