Mindie/nq-hard-negative

CRAG Evaluator Hard Negative Dataset

A processed dataset for training a retrieval evaluator for Corrective Retrieval Augmented Generation (CRAG).

Source Dataset

This dataset is derived from [BeiR/nq].

The original dataset was processed to construct query-document pairs suitable for training a retrieval evaluator.

Processing

The dataset was constructed using the following procedure:

1. Extract queries and ground-truth relevant documents from the original dataset.
2. Retrieve candidate documents using Contriever.
3. Remove ground-truth relevant documents from the retrieved candidates.
4. Treat the remaining retrieved documents as hard negatives.
5. Combine positive and hard-negative query-document pairs.
6. Assign binary relevance labels.

Dataset Features

Field	Description
query_id	ID of the original query
corpus_id	ID of the corpus document
query_text	Query text
corpus_text	Corpus document text
label	Binary relevance label

Intended Use

This dataset is intended for research on retrieval evaluation, and hard-negative training.

Relationship to Existing Work

This dataset is a processed derivative of [BeiR/nq]. It is not the original dataset.

The preprocessing procedure was designed for experiments with the CRAG retrieval evaluator, making hard negatives and efficiency of hard negatives for training.

License

This dataset contains data derived from [BeiR/nq]. Please refer to the original dataset's license and terms of use.

Citation

If you use this dataset, please cite both the original dataset

Original Dataset

[BibTeX]

Dataset Card for BEIR Benchmark

nq is one of the datasets from the Question Answering task within BEIR, measuring Wikipedia article retrieval for a web search query.

Dataset Summary

BEIR is a heterogeneous benchmark built from 18 diverse datasets representing 9 information retrieval tasks.

• Fact-checking: FEVER, Climate-FEVER, SciFact
• Question-Answering: NQ, HotpotQA, FiQA-2018
• Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
• News Retrieval: TREC-NEWS, Robust04
• Argument Retrieval: Touche-2020, ArguAna
• Duplicate Question Retrieval: Quora, CqaDupstack
• Citation-Prediction: SCIDOCS
• Tweet Retrieval: Signal-1M
• Entity Retrieval: DBPedia

Languages

All tasks are in English (en).

Dataset Structure

This dataset uses the standard BEIR retrieval layout and includes:

• corpus: one row per document with _id, title, text
• queries: one row per query with _id, title, text

Data Fields

• _id (string): unique identifier
• title (string): title (empty string when unavailable)
• text (string): document/query text

Data Instances

A high level example of any BEIR dataset:

corpus = {
    "doc1" : {
        "title": "Albert Einstein",
        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
                 of the photoelectric effect', a pivotal step in the development of quantum theory."
        },
    "doc2" : {
        "title": "", # Keep title an empty string if not present
        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
    },
}

queries = {
    "q1" : "Who developed the mass-energy equivalence formula?",
    "q2" : "Which beer is brewed with a large proportion of wheat?"
}

qrels = {
    "q1" : {"doc1": 1},
    "q2" : {"doc2": 1},
}

NQ Data Splits

Subset	Split	Rows
corpus	corpus	2,681,468
queries	queries	3,452

BEIR Direct Download

You can also download BEIR datasets directly (without loading through Hugging Face datasets) using the links below.

Dataset	Website	BEIR-Name	Type	Queries	Corpus	Rel D/Q	Down-load	md5
MSMARCO	Homepage	msmarco	train dev test	6,980	8.84M	1.1	Link	444067daf65d982533ea17ebd59501e4
TREC-COVID	Homepage	trec-covid	test	50	171K	493.5	Link	ce62140cb23feb9becf6270d0d1fe6d1
NFCorpus	Homepage	nfcorpus	train dev test	323	3.6K	38.2	Link	a89dba18a62ef92f7d323ec890a0d38d
BioASQ	Homepage	bioasq	train test	500	14.91M	8.05	No	How to Reproduce?
NQ	Homepage	nq	train test	3,452	2.68M	1.2	Link	d4d3d2e48787a744b6f6e691ff534307
HotpotQA	Homepage	hotpotqa	train dev test	7,405	5.23M	2.0	Link	f412724f78b0d91183a0e86805e16114
FiQA-2018	Homepage	fiqa	train dev test	648	57K	2.6	Link	17918ed23cd04fb15047f73e6c3bd9d9
Signal-1M(RT)	Homepage	signal1m	test	97	2.86M	19.6	No	How to Reproduce?
TREC-NEWS	Homepage	trec-news	test	57	595K	19.6	No	How to Reproduce?
ArguAna	Homepage	arguana	test	1,406	8.67K	1.0	Link	8ad3e3c2a5867cdced806d6503f29b99
Touche-2020	Homepage	webis-touche2020	test	49	382K	19.0	Link	46f650ba5a527fc69e0a6521c5a23563
CQADupstack	Homepage	cqadupstack	test	13,145	457K	1.4	Link	4e41456d7df8ee7760a7f866133bda78
Quora	Homepage	quora	dev test	10,000	523K	1.6	Link	18fb154900ba42a600f84b839c173167
DBPedia	Homepage	dbpedia-entity	dev test	400	4.63M	38.2	Link	c2a39eb420a3164af735795df012ac2c
SCIDOCS	Homepage	scidocs	test	1,000	25K	4.9	Link	38121350fc3a4d2f48850f6aff52e4a9
FEVER	Homepage	fever	train dev test	6,666	5.42M	1.2	Link	5a818580227bfb4b35bb6fa46d9b6c03
Climate-FEVER	Homepage	climate-fever	test	1,535	5.42M	3.0	Link	8b66f0a9126c521bae2bde127b4dc99d
SciFact	Homepage	scifact	train test	300	5K	1.1	Link	5f7d1de60b170fc8027bb7898e2efca1
Robust04	Homepage	robust04	test	249	528K	69.9	No	How to Reproduce?

Citation Information

@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}