|
---
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annotations_creators: []
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language_creators: []
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languages:
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- en
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licenses:
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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
|
|
nfcorpus:
|
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- 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
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|
scifact:
|
|
- 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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- zero-shot-retrieval
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- information-retrieval
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- zero-shot-information-retrieval
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task_ids:
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- passage-retrieval
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- entity-linking-retrieval
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- fact-checking-retrieval
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- tweet-retrieval
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|
- citation-prediction-retrieval
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|
- duplication-question-retrieval
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|
- argument-retrieval
|
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- news-retrieval
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- biomedical-information-retrieval
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- question-answering-retrieval
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|
---
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# Dataset Card for BEIR Benchmark
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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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## Dataset Description
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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:** nandan.thakur@uwaterloo.ca
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### Dataset Summary
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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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- 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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All these datasets have been preprocessed and can be used for your experiments.
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```python
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```
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### Supported Tasks and Leaderboards
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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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The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
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### Languages
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All tasks are in English (`en`).
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## Dataset Structure
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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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### Data Instances
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A high level example of any beir dataset:
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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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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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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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### 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:
|
|
- `_id`: a `string` feature representing the unique query id
|
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- `text`: a `string` feature, denoting the text of the query.
|
|
|
|
### Qrels
|
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- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
|
|
- `_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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### Data Splits
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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`` |
|
|
| 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`` |
|
|
| 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) |
|
|
|
|
|
|
## 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](https://github.com/Nthakur20) for adding this dataset. |
