|
--- |
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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 |
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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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- 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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|
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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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Examples from all configurations have the following features: |
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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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### 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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### 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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### 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`` | |
|
| 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) | |
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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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[Needs More Information] |
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#### Who are the source language producers? |
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[Needs More Information] |
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### Annotations |
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#### Annotation process |
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[Needs More Information] |
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#### Who are the annotators? |
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[Needs More Information] |
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### Personal and Sensitive Information |
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|
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[Needs More Information] |
|
|
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## Considerations for Using the Data |
|
|
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### Social Impact of Dataset |
|
|
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[Needs More Information] |
|
|
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### Discussion of Biases |
|
|
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[Needs More Information] |
|
|
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### Other Known Limitations |
|
|
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[Needs More Information] |
|
|
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## Additional Information |
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|
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### Dataset Curators |
|
|
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[Needs More Information] |
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### Licensing Information |
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|
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[Needs More Information] |
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### Citation Information |
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Cite as: |
|
``` |
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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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### Contributions |
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Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
