Datasets:
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
biomedical-information-retrieval
citation-prediction-retrieval
passage-retrieval
news-retrieval
argument-retrieval
zero-shot-information-retrieval
License:
File size: 9,166 Bytes
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language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
task_categories:
- zero-shot-classification
- text-retrieval
task_ids:
- document-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
tags:
- biomedical-information-retrieval
- citation-prediction-retrieval
- passage-retrieval
- news-retrieval
- argument-retrieval
- zero-shot-information-retrieval
- tweet-retrieval
- question-answering-retrieval
- duplication-question-retrieval
- zero-shot-retrieval
configs:
- config_name: corpus
data_files:
- split: corpus
path: corpus/corpus-*
- config_name: queries
data_files:
- split: queries
path: queries/queries-*
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 4993889
num_examples: 8674
download_size: 4993889
dataset_size: 4993889
- config_name: queries
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 949777
num_examples: 1406
download_size: 949777
dataset_size: 949777
---
# Dataset Card for BEIR Benchmark
## Dataset Description
- **Homepage:** https://beir.ai
- **Repository:** https://beir.ai
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark built from 18 diverse datasets representing 9 information retrieval tasks.
This `arguana` subset is the Argument Retrieval task within BEIR.
### Languages
All tasks are in English (`en`).
## Dataset Structure
This dataset uses the standard BEIR retrieval layout and includes:
- `corpus`: one row per document with `_id`, `title`, `text`
- `queries`: one row per query with `_id`, `title`, `text`
### Data Fields
- `_id` (`string`): unique identifier
- `title` (`string`): title (empty string when unavailable)
- `text` (`string`): document/query text
### Data Instances
A high level example of any BEIR dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Splits
| Subset | Split | Rows |
| --- | --- | ---: |
| corpus | corpus | 8,674 |
| queries | queries | 1,406 |
### NFCorpus Data Splits
- `train`, `dev`, `test`
You can also download BEIR datasets directly (without loading through Hugging Face datasets) using the links below.
| Dataset | Website | BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| --- | --- | --- | --- | ---: | ---: | ---: | --- | --- |
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/) | `msmarco` | `train` `dev` `test` | 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | `444067daf65d982533ea17ebd59501e4` |
| 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` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | `nfcorpus` | `train` `dev` `test` | 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | `a89dba18a62ef92f7d323ec890a0d38d` |
| BioASQ | [Homepage](http://bioasq.org) | `bioasq` | `train` `test` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | `nq` | `train` `test` | 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | `d4d3d2e48787a744b6f6e691ff534307` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | `hotpotqa` | `train` `dev` `test` | 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | `f412724f78b0d91183a0e86805e16114` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | `fiqa` | `train` `dev` `test` | 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | `17918ed23cd04fb15047f73e6c3bd9d9` |
| 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) |
| 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) |
| 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` |
| 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` |
| 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` |
| Quora | [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | `quora` | `dev` `test` | 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | `18fb154900ba42a600f84b839c173167` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | `dbpedia-entity` | `dev` `test` | 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | `c2a39eb420a3164af735795df012ac2c` |
| 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` `dev` `test` | 6,666 | 5.42M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | `5a818580227bfb4b35bb6fa46d9b6c03` |
| 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` `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) |
## Citation Information
```bibtex
@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}
}
```
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