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Datasets:
income
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cqadupstack-stats-top-20-gen-queries

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Text Retrieval
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English
Size:
10K - 100K
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- ---
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- annotations_creators: []
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- language_creators: []
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- language:
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- - en
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- license:
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- - cc-by-sa-4.0
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- multilinguality:
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- - monolingual
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- paperswithcode_id: beir
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- pretty_name: BEIR Benchmark
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- size_categories:
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- msmarco:
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- - 1M<n<10M
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- trec-covid:
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- - 100k<n<1M
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- nfcorpus:
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- - 1K<n<10K
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- nq:
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- - 1M<n<10M
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- hotpotqa:
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- - 1M<n<10M
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- fiqa:
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- - 10K<n<100K
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- arguana:
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- - 1K<n<10K
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- touche-2020:
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- - 100K<n<1M
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- cqadupstack:
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- - 100K<n<1M
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- quora:
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- - 100K<n<1M
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- dbpedia:
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- - 1M<n<10M
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- scidocs:
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- - 10K<n<100K
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- fever:
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- - 1M<n<10M
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- climate-fever:
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- - 1M<n<10M
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- scifact:
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- - 1K<n<10K
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- source_datasets: []
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- task_categories:
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- - text-retrieval
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- ---
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-
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- # NFCorpus: 20 generated queries (BEIR Benchmark)
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-
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- This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
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-
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- - DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
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- - id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
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- - Questions generated: 20
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- - Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
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-
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-
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- Below contains the old dataset card for the BEIR benchmark.
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-
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-
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- # Dataset Card for BEIR Benchmark
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-
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- ## Table of Contents
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- - [Dataset Description](#dataset-description)
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- - [Dataset Summary](#dataset-summary)
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- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- - [Languages](#languages)
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- - [Dataset Structure](#dataset-structure)
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- - [Data Instances](#data-instances)
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- - [Data Fields](#data-fields)
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- - [Data Splits](#data-splits)
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- - [Dataset Creation](#dataset-creation)
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- - [Curation Rationale](#curation-rationale)
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- - [Source Data](#source-data)
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- - [Annotations](#annotations)
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- - [Personal and Sensitive Information](#personal-and-sensitive-information)
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- - [Considerations for Using the Data](#considerations-for-using-the-data)
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- - [Social Impact of Dataset](#social-impact-of-dataset)
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- - [Discussion of Biases](#discussion-of-biases)
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- - [Other Known Limitations](#other-known-limitations)
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- - [Additional Information](#additional-information)
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- - [Dataset Curators](#dataset-curators)
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- - [Licensing Information](#licensing-information)
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- - [Citation Information](#citation-information)
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- - [Contributions](#contributions)
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-
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- ## Dataset Description
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-
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- - **Homepage:** https://github.com/UKPLab/beir
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- - **Repository:** https://github.com/UKPLab/beir
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- - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
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- - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
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- - **Point of Contact:** [email protected]
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-
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- ### Dataset Summary
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-
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- BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
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-
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- - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
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- - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
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- - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
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- - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
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- - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
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- - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
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- - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
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- - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
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- - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
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-
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- All these datasets have been preprocessed and can be used for your experiments.
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-
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-
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- ```python
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-
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- ```
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-
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- ### Supported Tasks and Leaderboards
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-
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- 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.
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-
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- The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
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-
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- ### Languages
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-
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- All tasks are in English (`en`).
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-
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- ## Dataset Structure
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-
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- All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
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- - `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...."}`
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- - `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?"}`
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- - `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`
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-
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- ### Data Instances
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-
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- A high level example of any beir dataset:
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-
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- ```python
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- corpus = {
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- "doc1" : {
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- "title": "Albert Einstein",
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- "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
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- one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
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- its influence on the philosophy of science. He is best known to the general public for his mass–energy \
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- equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
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- Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
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- of the photoelectric effect', a pivotal step in the development of quantum theory."
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- },
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- "doc2" : {
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- "title": "", # Keep title an empty string if not present
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- "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
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- malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
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- with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
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- },
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- }
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-
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- queries = {
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- "q1" : "Who developed the mass-energy equivalence formula?",
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- "q2" : "Which beer is brewed with a large proportion of wheat?"
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- }
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-
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- qrels = {
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- "q1" : {"doc1": 1},
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- "q2" : {"doc2": 1},
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- }
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- ```
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-
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- ### Data Fields
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-
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- Examples from all configurations have the following features:
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-
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- ### Corpus
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- - `corpus`: a `dict` feature representing the document title and passage text, made up of:
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- - `_id`: a `string` feature representing the unique document id
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- - `title`: a `string` feature, denoting the title of the document.
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- - `text`: a `string` feature, denoting the text of the document.
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-
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- ### Queries
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- - `queries`: a `dict` feature representing the query, made up of:
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- - `_id`: a `string` feature representing the unique query id
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- - `text`: a `string` feature, denoting the text of the query.
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-
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- ### Qrels
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- - `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
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- - `_id`: a `string` feature representing the query id
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- - `_id`: a `string` feature, denoting the document id.
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- - `score`: a `int32` feature, denoting the relevance judgement between query and document.
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-
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-
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- ### Data Splits
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-
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- | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
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- | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
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- | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
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- | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
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- | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
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- | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
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- | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
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- | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
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- | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
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- | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
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- | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
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- | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
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- | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
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- | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
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- | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
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- | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
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- | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
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- | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
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- | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
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- | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
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- | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
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-
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-
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- ## Dataset Creation
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-
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- ### Curation Rationale
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-
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- [Needs More Information]
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-
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- ### Source Data
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-
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- #### Initial Data Collection and Normalization
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-
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- [Needs More Information]
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-
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- #### Who are the source language producers?
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-
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- [Needs More Information]
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-
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- ### Annotations
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-
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- #### Annotation process
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-
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- [Needs More Information]
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-
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- #### Who are the annotators?
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-
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- [Needs More Information]
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-
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- ### Personal and Sensitive Information
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-
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- [Needs More Information]
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-
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- ## Considerations for Using the Data
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-
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- ### Social Impact of Dataset
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-
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- [Needs More Information]
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-
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- ### Discussion of Biases
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-
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- [Needs More Information]
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-
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- ### Other Known Limitations
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-
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- [Needs More Information]
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-
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- ## Additional Information
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-
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- ### Dataset Curators
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-
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- [Needs More Information]
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-
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- ### Licensing Information
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-
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- [Needs More Information]
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-
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- ### Citation Information
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-
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- Cite as:
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- ```
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- @inproceedings{
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- thakur2021beir,
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- title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
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- author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
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- booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
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- year={2021},
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- url={https://openreview.net/forum?id=wCu6T5xFjeJ}
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- }
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- ```
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-
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- ### Contributions
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-
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- Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
285
-
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-
287
- # Dataset Card for BEIR Benchmark
288
-
289
- ## Table of Contents
290
- - [Dataset Description](#dataset-description)
291
- - [Dataset Summary](#dataset-summary)
292
- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
293
- - [Languages](#languages)
294
- - [Dataset Structure](#dataset-structure)
295
- - [Data Instances](#data-instances)
296
- - [Data Fields](#data-fields)
297
- - [Data Splits](#data-splits)
298
- - [Dataset Creation](#dataset-creation)
299
- - [Curation Rationale](#curation-rationale)
300
- - [Source Data](#source-data)
301
- - [Annotations](#annotations)
302
- - [Personal and Sensitive Information](#personal-and-sensitive-information)
303
- - [Considerations for Using the Data](#considerations-for-using-the-data)
304
- - [Social Impact of Dataset](#social-impact-of-dataset)
305
- - [Discussion of Biases](#discussion-of-biases)
306
- - [Other Known Limitations](#other-known-limitations)
307
- - [Additional Information](#additional-information)
308
- - [Dataset Curators](#dataset-curators)
309
- - [Licensing Information](#licensing-information)
310
- - [Citation Information](#citation-information)
311
- - [Contributions](#contributions)
312
-
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- ## Dataset Description
314
-
315
- - **Homepage:** https://github.com/UKPLab/beir
316
- - **Repository:** https://github.com/UKPLab/beir
317
- - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
318
- - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
319
- - **Point of Contact:** [email protected]
320
-
321
- ### Dataset Summary
322
-
323
- BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
324
-
325
- - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
326
- - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
327
- - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
328
- - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
329
- - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
330
- - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
331
- - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
332
- - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
333
- - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
334
-
335
- All these datasets have been preprocessed and can be used for your experiments.
336
-
337
-
338
- ```python
339
-
340
- ```
341
-
342
- ### Supported Tasks and Leaderboards
343
-
344
- 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.
345
-
346
- The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
347
-
348
- ### Languages
349
-
350
- All tasks are in English (`en`).
351
-
352
- ## Dataset Structure
353
-
354
- All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
355
- - `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...."}`
356
- - `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?"}`
357
- - `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`
358
-
359
- ### Data Instances
360
-
361
- A high level example of any beir dataset:
362
-
363
- ```python
364
- corpus = {
365
- "doc1" : {
366
- "title": "Albert Einstein",
367
- "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
368
- one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
369
- its influence on the philosophy of science. He is best known to the general public for his mass–energy \
370
- equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
371
- Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
372
- of the photoelectric effect', a pivotal step in the development of quantum theory."
373
- },
374
- "doc2" : {
375
- "title": "", # Keep title an empty string if not present
376
- "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
377
- malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
378
- with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
379
- },
380
- }
381
-
382
- queries = {
383
- "q1" : "Who developed the mass-energy equivalence formula?",
384
- "q2" : "Which beer is brewed with a large proportion of wheat?"
385
- }
386
-
387
- qrels = {
388
- "q1" : {"doc1": 1},
389
- "q2" : {"doc2": 1},
390
- }
391
- ```
392
-
393
- ### Data Fields
394
-
395
- Examples from all configurations have the following features:
396
-
397
- ### Corpus
398
- - `corpus`: a `dict` feature representing the document title and passage text, made up of:
399
- - `_id`: a `string` feature representing the unique document id
400
- - `title`: a `string` feature, denoting the title of the document.
401
- - `text`: a `string` feature, denoting the text of the document.
402
-
403
- ### Queries
404
- - `queries`: a `dict` feature representing the query, made up of:
405
- - `_id`: a `string` feature representing the unique query id
406
- - `text`: a `string` feature, denoting the text of the query.
407
-
408
- ### Qrels
409
- - `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
410
- - `_id`: a `string` feature representing the query id
411
- - `_id`: a `string` feature, denoting the document id.
412
- - `score`: a `int32` feature, denoting the relevance judgement between query and document.
413
-
414
-
415
- ### Data Splits
416
-
417
- | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
418
- | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
419
- | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
420
- | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
421
- | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
422
- | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
423
- | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
424
- | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
425
- | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
426
- | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
427
- | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
428
- | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
429
- | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
430
- | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
431
- | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
432
- | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
433
- | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
434
- | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
435
- | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
436
- | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
437
- | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
438
-
439
-
440
- ## Dataset Creation
441
-
442
- ### Curation Rationale
443
-
444
- [Needs More Information]
445
-
446
- ### Source Data
447
-
448
- #### Initial Data Collection and Normalization
449
-
450
- [Needs More Information]
451
-
452
- #### Who are the source language producers?
453
-
454
- [Needs More Information]
455
-
456
- ### Annotations
457
-
458
- #### Annotation process
459
-
460
- [Needs More Information]
461
-
462
- #### Who are the annotators?
463
-
464
- [Needs More Information]
465
-
466
- ### Personal and Sensitive Information
467
-
468
- [Needs More Information]
469
-
470
- ## Considerations for Using the Data
471
-
472
- ### Social Impact of Dataset
473
-
474
- [Needs More Information]
475
-
476
- ### Discussion of Biases
477
-
478
- [Needs More Information]
479
-
480
- ### Other Known Limitations
481
-
482
- [Needs More Information]
483
-
484
- ## Additional Information
485
-
486
- ### Dataset Curators
487
-
488
- [Needs More Information]
489
-
490
- ### Licensing Information
491
-
492
- [Needs More Information]
493
-
494
- ### Citation Information
495
-
496
- Cite as:
497
- ```
498
- @inproceedings{
499
- thakur2021beir,
500
- title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
501
- author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
502
- booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
503
- year={2021},
504
- url={https://openreview.net/forum?id=wCu6T5xFjeJ}
505
- }
506
- ```
507
-
508
- ### Contributions
509
-
510
- Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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