reason-embed-data

<div align="center"> <h1> ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval </h1> </div> <p align="center"> <a href="https://arxiv.org/abs/2510.08252" target="_blank" rel="noopener noreferrer"> <img src="https://img.shields.io/badge/arXiv-2510.08252-B31B1B.svg?style=flat-square&logo=arxiv&logoColor=white" alt="arXiv:2510.08252"> </a> </p> This repository contains the synthetic training data introduced in the paper [ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval](https://arxiv.org/abs/2510.08252). The dataset is designed to enhance text embeddings for reasoning-intensive document retrieval tasks. ## Dataset Overview ### v0928 This version corresponds to the 81,659 training samples used in the paper. The statistics of this version are as follows:  For more details about the dataset and its construction, please refer to the [paper](https://arxiv.org/abs/2510.08252). ## Usage You can load the dataset using the `datasets` library as follows: ```python import datasets # available configs: v0928 # avaliable splits: biology, earth_science, economics, psychology, robotics, stackoverflow, sustainable_living, leetcode, pony, theoremqa_questions, theoremqa_theorems dataset = datasets.load_dataset("hanhainebula/reason-embed-data", "v0928", split="biology") print(dataset[0]) ``` ## Citation If you find this repository useful, please consider giving a star ⭐ and citation: ``` @article{chen2025reasonembed, title={ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval}, author={Chen, Jianlyu and Lan, Junwei and Li, Chaofan and Lian, Defu and Liu, Zheng}, journal={arXiv preprint arXiv:2510.08252}, year={2025} } ```