Text Embeddings by Weakly-Supervised Contrastive Pre-training

• URL: http://arxiv.org/abs/2212.03533v2
• Date: Thu, 22 Feb 2024 06:21:51 GMT
• Title: Text Embeddings by Weakly-Supervised Contrastive Pre-training
• Authors: Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei
• Abstract summary: E5 is a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts.
• Score: 98.31785569325402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. For zero-shot settings, E5 is the first model that outperforms the strong BM25 baseline on the BEIR retrieval benchmark without using any labeled data. When fine-tuned, E5 obtains the best results on the MTEB benchmark, beating existing embedding models with 40x more parameters.

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