NanoSciFact-bm25

# NanoBEIR SciFact with BM25 rankings
This dataset is an updated variant of [NanoSciFact](https://huggingface.co/datasets/zeta-alpha-ai/NanoSciFact), which is a subset of the SciFact dataset from the Benchmark for Information Retrieval (BEIR).
SciFact was created as a subset of the rather large BEIR, designed to be more efficient to run. This dataset adds a `bm25-ranked-ids` column to the `relevance` subset, which contains a ranking of every single passage in the corpus to the query.

This dataset is used in Sentence Transformers for evaluating CrossEncoder (i.e. reranker) models on NanoBEIR by reranking the top *k* results from BM25.

# 带有BM25排序结果的NanoBEIR SciFact数据集

本数据集是[NanoSciFact](https://huggingface.co/datasets/zeta-alpha-ai/NanoSciFact)的更新变体，而NanoSciFact是信息检索基准测试集（Benchmark for Information Retrieval，简称BEIR）中SciFact数据集的子集。

SciFact作为体量庞大的BEIR的子集被构建，旨在降低运行开销、提升执行效率。

本数据集为`relevance`子集新增了`bm25-ranked-ids`列，该列存储了语料库中所有段落针对对应查询的BM25排序结果。

本数据集被应用于句子Transformer（Sentence Transformers）框架中，用于通过对BM25返回的前*k*条结果进行重排序，在NanoBEIR任务上评估交叉编码器（CrossEncoder，即重排序器）模型的性能。

搜集汇总

背景与挑战

背景概述

该数据集是NanoSciFact的更新版本，在BEIR的SciFact子集基础上添加了基于BM25的查询-语段排名列。它主要用于在Sentence Transformers中评估CrossEncoder模型，通过重新排序BM25的检索结果来实现。