This paper presents E5111E5:EmbEddings from bidirEctionalEncoder rEpresentations,
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 ondatasets 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 withmore parameters.