irds/beir_nfcorpus_test

`beir/nfcorpus/test`数据集由ir-datasets包提供，主要用于文本检索任务。该数据集包含323个查询（queries）和12,334个相关性评估（qrels）。文档数据需要从`irds/beir_nfcorpus`数据集中获取。数据集的使用可以通过HuggingFace的`load_dataset`函数进行加载，加载后数据将以🤗 Dataset格式存储。

The `beir/nfcorpus/test` dataset is provided by the ir-datasets package and is primarily used for text retrieval tasks. It contains 323 queries and 12,334 relevance judgments (qrels). The corpus documents need to be retrieved from the `irds/beir_nfcorpus` dataset. This dataset can be loaded using HuggingFace's `load_dataset` function, and the loaded data will be stored in the 🤗 Dataset format.

原始信息汇总

数据集概述

数据集名称

beir/nfcorpus/test

数据集来源

由ir-datasets提供。

数据内容

• queries（查询）: 数量=323
• qrels（相关性评估）: 数量=12,334
• docs（文档）: 使用irds/beir_nfcorpus

数据使用示例

python from datasets import load_dataset

queries = load_dataset(irds/beir_nfcorpus_test, queries) for record in queries: record # {query_id: ..., text: ...}

qrels = load_dataset(irds/beir_nfcorpus_test, qrels) for record in qrels: record # {query_id: ..., doc_id: ..., relevance: ..., iteration: ...}

引用信息

@inproceedings{Boteva2016Nfcorpus, title="A Full-Text Learning to Rank Dataset for Medical Information Retrieval", author = "Vera Boteva and Demian Gholipour and Artem Sokolov and Stefan Riezler", booktitle = "Proceedings of the European Conference on Information Retrieval ({ECIR})", location = "Padova, Italy", publisher = "Springer", year = 2016 } @article{Thakur2021Beir, title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models", author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.08663", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.08663", }

搜集汇总

背景与挑战

背景概述

irds/beir_nfcorpus_test是一个医学信息检索数据集，包含323个查询和12,334个相关性评估，文档数据需从irds/beir_nfcorpus获取，适用于零样本信息检索模型的评估。