query-id
int64 3
1.19M
| corpus-id
int64 0
8.84M
| score
int64 1
1
|
---|---|---|
1,185,869 | 0 | 1 |
1,185,868 | 16 | 1 |
597,651 | 49 | 1 |
403,613 | 60 | 1 |
1,183,785 | 389 | 1 |
312,651 | 616 | 1 |
80,385 | 723 | 1 |
645,590 | 944 | 1 |
645,337 | 1,054 | 1 |
186,154 | 1,160 | 1 |
457,407 | 1,172 | 1 |
441,383 | 1,389 | 1 |
683,408 | 1,605 | 1 |
1,164,799 | 1,713 | 1 |
484,187 | 1,822 | 1 |
460,668 | 1,939 | 1 |
666,321 | 2,152 | 1 |
182,487 | 2,277 | 1 |
564,233 | 2,488 | 1 |
455,279 | 2,599 | 1 |
208,108 | 2,704 | 1 |
733,739 | 2,816 | 1 |
1,164,798 | 2,924 | 1 |
402,608 | 3,036 | 1 |
443,797 | 3,146 | 1 |
662,502 | 3,257 | 1 |
1,184,679 | 3,368 | 1 |
14,562 | 3,382 | 1 |
602,162 | 3,597 | 1 |
545,059 | 3,702 | 1 |
708,236 | 3,815 | 1 |
310,130 | 3,923 | 1 |
693,161 | 4,029 | 1 |
186,617 | 4,251 | 1 |
573,027 | 4,360 | 1 |
1,173,772 | 4,462 | 1 |
541,973 | 4,583 | 1 |
273,090 | 4,698 | 1 |
441,269 | 4,809 | 1 |
642,237 | 4,918 | 1 |
503,515 | 5,025 | 1 |
637,443 | 5,250 | 1 |
1,164,796 | 5,359 | 1 |
749,988 | 5,469 | 1 |
749,988 | 5,470 | 1 |
135,841 | 5,585 | 1 |
295,446 | 6,021 | 1 |
653,051 | 6,127 | 1 |
691,147 | 6,236 | 1 |
410,621 | 6,458 | 1 |
410,621 | 6,461 | 1 |
1,164,795 | 6,564 | 1 |
598,443 | 6,673 | 1 |
596,451 | 6,685 | 1 |
651,441 | 6,793 | 1 |
452,286 | 7,012 | 1 |
308,543 | 7,115 | 1 |
202,126 | 7,223 | 1 |
114,820 | 7,334 | 1 |
501,778 | 7,445 | 1 |
531,029 | 7,553 | 1 |
651,110 | 7,662 | 1 |
594,127 | 7,766 | 1 |
1,164,794 | 7,777 | 1 |
396,032 | 7,885 | 1 |
705,580 | 8,103 | 1 |
658,203 | 8,217 | 1 |
387,734 | 8,328 | 1 |
655,102 | 8,439 | 1 |
224,712 | 8,652 | 1 |
411,732 | 8,764 | 1 |
1,164,793 | 8,991 | 1 |
605,902 | 9,323 | 1 |
581,014 | 9,432 | 1 |
559,240 | 9,533 | 1 |
608,711 | 9,755 | 1 |
535,936 | 9,972 | 1 |
130,335 | 9,990 | 1 |
147,535 | 10,087 | 1 |
1,164,792 | 10,204 | 1 |
595,576 | 10,417 | 1 |
569,308 | 10,747 | 1 |
753,706 | 10,854 | 1 |
627,871 | 10,964 | 1 |
673,608 | 11,081 | 1 |
510,071 | 11,102 | 1 |
113,839 | 11,321 | 1 |
1,164,791 | 11,431 | 1 |
460,953 | 11,640 | 1 |
685,235 | 12,191 | 1 |
650,643 | 12,528 | 1 |
1,183,784 | 12,640 | 1 |
1,164,790 | 12,753 | 1 |
96,740 | 12,858 | 1 |
26,666 | 12,967 | 1 |
490,046 | 13,184 | 1 |
485,823 | 13,300 | 1 |
635,632 | 13,310 | 1 |
534,505 | 13,630 | 1 |
498,612 | 13,852 | 1 |
Dataset Card for BEIR Benchmark
Dataset Summary
BEIR is a heterogeneous benchmark that has been 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
All these datasets have been preprocessed and can be used for your experiments.
Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
Languages
All tasks are in English (en
).
Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
corpus
file: a.jsonl
file (jsonlines) that contains a list of dictionaries, each with three fields_id
with unique document identifier,title
with document title (optional) andtext
with document paragraph or passage. For example:{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}
queries
file: a.jsonl
file (jsonlines) that contains a list of dictionaries, each with two fields_id
with unique query identifier andtext
with query text. For example:{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}
qrels
file: a.tsv
file (tab-seperated) that contains three columns, i.e. thequery-id
,corpus-id
andscore
in this order. Keep 1st row as header. For example:q1 doc1 1
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},
}
Data Fields
Examples from all configurations have the following features:
Corpus
corpus
: adict
feature representing the document title and passage text, made up of:_id
: astring
feature representing the unique document idtitle
: astring
feature, denoting the title of the document.text
: astring
feature, denoting the text of the document.
Queries
queries
: adict
feature representing the query, made up of:_id
: astring
feature representing the unique query idtext
: astring
feature, denoting the text of the query.
Qrels
qrels
: adict
feature representing the query document relevance judgements, made up of:_id
: astring
feature representing the query id_id
: astring
feature, denoting the document id.score
: aint32
feature, denoting the relevance judgement between query and document.
Data Splits
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? |
Dataset Creation
Curation Rationale
[Needs More Information]
Source Data
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
[Needs More Information]
Annotations
Annotation process
[Needs More Information]
Who are the annotators?
[Needs More Information]
Personal and Sensitive Information
[Needs More Information]
Considerations for Using the Data
Social Impact of Dataset
[Needs More Information]
Discussion of Biases
[Needs More Information]
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
[Needs More Information]
Licensing Information
[Needs More Information]
Citation Information
Cite as:
@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}
}
Contributions
Thanks to @Nthakur20 for adding this dataset.
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