multilingual-e5-large-instruct

Papers using or citing this model 4

• BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models 2.2K citations · 2021 · arXiv (Cornell University) · arXiv

  Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and...

• MTEB: Massive Text Embedding Benchmark 1.1K citations · 2022 · arXiv (Cornell University) · arXiv

  Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be...

• Multilingual E5 Text Embeddings: A Technical Report 664 citations · 2024 · arXiv (Cornell University) · arXiv

  This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023. Three embedding models of different sizes (small / base / large) are...

• Improving Text Embeddings with Large Language Models 463 citations · 2023 · arXiv (Cornell University) · arXiv

  In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that often depend on multi-stage intermediate...