Text Embeddings by Weakly-Supervised Contrastive Pre-training

Understand

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks.

• The model is trained in a contrastive manner with weak supervision signals from our curated large-scale text pair dataset (called CCPairs).
• E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts such as retrieval, clustering, and classification, achieving strong performance in both zero-shot and fine-tuned settings.
• We conduct extensive evaluations on 56 datasets from the BEIR and MTEB benchmarks.