metadata

annotations_creators: []
language_creators: []
language:
  - en
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
  msmarco:
    - 1M<n<10M
  trec-covid:
    - 100k<n<1M
  nfcorpus:
    - 1K<n<10K
  nq:
    - 1M<n<10M
  hotpotqa:
    - 1M<n<10M
  fiqa:
    - 10K<n<100K
  arguana:
    - 1K<n<10K
  touche-2020:
    - 100K<n<1M
  cqadupstack:
    - 100K<n<1M
  quora:
    - 100K<n<1M
  dbpedia:
    - 1M<n<10M
  scidocs:
    - 10K<n<100K
  fever:
    - 1M<n<10M
  climate-fever:
    - 1M<n<10M
  scifact:
    - 1K<n<10K
source_datasets: []
task_categories:
  - text-retrieval

NFCorpus: 20 generated queries (BEIR Benchmark)

This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.

DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
id (str): unique document id in NFCorpus in the BEIR benchmark (corpus.jsonl).
Questions generated: 20
Code used for generation: evaluate_anserini_docT5query_parallel.py

Below contains the old dataset card for the BEIR benchmark.

Dataset Card for BEIR Benchmark

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://github.com/UKPLab/beir
Repository: https://github.com/UKPLab/beir
Paper: https://openreview.net/forum?id=wCu6T5xFjeJ
Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
Point of Contact: [email protected]

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) and text 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 and text 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. the query-id, corpus-id and score 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 massâ€“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 WeiÃŸbier 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: a dict feature representing the document title and passage text, made up of:
- _id: a string feature representing the unique document id
  - title: a string feature, denoting the title of the document.
  - text: a string feature, denoting the text of the document.

Queries

queries: a dict feature representing the query, made up of:
- _id: a string feature representing the unique query id
- text: a string feature, denoting the text of the query.

Qrels

qrels: a dict feature representing the query document relevance judgements, made up of:
- _id: a string feature representing the query id
  - _id: a string feature, denoting the document id.
  - score: a int32 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.Top-20 generated queries for every passage in NFCorpus

Dataset Card for BEIR Benchmark

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://github.com/UKPLab/beir
Repository: https://github.com/UKPLab/beir
Paper: https://openreview.net/forum?id=wCu6T5xFjeJ
Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
Point of Contact: [email protected]

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) and text 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 and text 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. the query-id, corpus-id and score 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 massâ€“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 WeiÃŸbier 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: a dict feature representing the document title and passage text, made up of:
- _id: a string feature representing the unique document id
  - title: a string feature, denoting the title of the document.
  - text: a string feature, denoting the text of the document.

Queries

queries: a dict feature representing the query, made up of:
- _id: a string feature representing the unique query id
- text: a string feature, denoting the text of the query.

Qrels

qrels: a dict feature representing the query document relevance judgements, made up of:
- _id: a string feature representing the query id
  - _id: a string feature, denoting the document id.
  - score: a int32 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.

NFCorpus: 20 generated queries (BEIR Benchmark)

Dataset Card for BEIR Benchmark

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Corpus

Queries

Qrels

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions

Dataset Card for BEIR Benchmark

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Corpus

Queries

Qrels

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions