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| 1 |
+
---
|
| 2 |
+
annotations_creators: []
|
| 3 |
+
language_creators: []
|
| 4 |
+
languages:
|
| 5 |
+
- en
|
| 6 |
+
licenses:
|
| 7 |
+
- cc-by-sa-4.0
|
| 8 |
+
multilinguality:
|
| 9 |
+
- monolingual
|
| 10 |
+
paperswithcode_id: beir
|
| 11 |
+
pretty_name: BEIR Benchmark
|
| 12 |
+
size_categories:
|
| 13 |
+
msmarco:
|
| 14 |
+
- 1M<n<10M
|
| 15 |
+
trec-covid:
|
| 16 |
+
- 100k<n<1M
|
| 17 |
+
nfcorpus:
|
| 18 |
+
- 1K<n<10K
|
| 19 |
+
nq:
|
| 20 |
+
- 1M<n<10M
|
| 21 |
+
hotpotqa:
|
| 22 |
+
- 1M<n<10M
|
| 23 |
+
fiqa:
|
| 24 |
+
- 10K<n<100K
|
| 25 |
+
arguana:
|
| 26 |
+
- 1K<n<10K
|
| 27 |
+
touche-2020:
|
| 28 |
+
- 100K<n<1M
|
| 29 |
+
cqadupstack:
|
| 30 |
+
- 100K<n<1M
|
| 31 |
+
quora:
|
| 32 |
+
- 100K<n<1M
|
| 33 |
+
dbpedia:
|
| 34 |
+
- 1M<n<10M
|
| 35 |
+
scidocs:
|
| 36 |
+
- 10K<n<100K
|
| 37 |
+
fever:
|
| 38 |
+
- 1M<n<10M
|
| 39 |
+
climate-fever:
|
| 40 |
+
- 1M<n<10M
|
| 41 |
+
scifact:
|
| 42 |
+
- 1K<n<10K
|
| 43 |
+
source_datasets: []
|
| 44 |
+
task_categories:
|
| 45 |
+
- text-retrieval
|
| 46 |
+
- zero-shot-retrieval
|
| 47 |
+
- information-retrieval
|
| 48 |
+
- zero-shot-information-retrieval
|
| 49 |
+
task_ids:
|
| 50 |
+
- passage-retrieval
|
| 51 |
+
- entity-linking-retrieval
|
| 52 |
+
- fact-checking-retrieval
|
| 53 |
+
- tweet-retrieval
|
| 54 |
+
- citation-prediction-retrieval
|
| 55 |
+
- duplication-question-retrieval
|
| 56 |
+
- argument-retrieval
|
| 57 |
+
- news-retrieval
|
| 58 |
+
- biomedical-information-retrieval
|
| 59 |
+
- question-answering-retrieval
|
| 60 |
+
---
|
| 61 |
+
|
| 62 |
+
# Dataset Card for BEIR Benchmark
|
| 63 |
+
|
| 64 |
+
## Table of Contents
|
| 65 |
+
- [Dataset Description](#dataset-description)
|
| 66 |
+
- [Dataset Summary](#dataset-summary)
|
| 67 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 68 |
+
- [Languages](#languages)
|
| 69 |
+
- [Dataset Structure](#dataset-structure)
|
| 70 |
+
- [Data Instances](#data-instances)
|
| 71 |
+
- [Data Fields](#data-fields)
|
| 72 |
+
- [Data Splits](#data-splits)
|
| 73 |
+
- [Dataset Creation](#dataset-creation)
|
| 74 |
+
- [Curation Rationale](#curation-rationale)
|
| 75 |
+
- [Source Data](#source-data)
|
| 76 |
+
- [Annotations](#annotations)
|
| 77 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 78 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 79 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 80 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 81 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 82 |
+
- [Additional Information](#additional-information)
|
| 83 |
+
- [Dataset Curators](#dataset-curators)
|
| 84 |
+
- [Licensing Information](#licensing-information)
|
| 85 |
+
- [Citation Information](#citation-information)
|
| 86 |
+
- [Contributions](#contributions)
|
| 87 |
+
|
| 88 |
+
## Dataset Description
|
| 89 |
+
|
| 90 |
+
- **Homepage:** https://github.com/UKPLab/beir
|
| 91 |
+
- **Repository:** https://github.com/UKPLab/beir
|
| 92 |
+
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
|
| 93 |
+
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
|
| 94 |
+
- **Point of Contact:** nandan.thakur@uwaterloo.ca
|
| 95 |
+
|
| 96 |
+
### Dataset Summary
|
| 97 |
+
|
| 98 |
+
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
|
| 99 |
+
|
| 100 |
+
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
|
| 101 |
+
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
|
| 102 |
+
- 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/)
|
| 103 |
+
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
|
| 104 |
+
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
|
| 105 |
+
- 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/)
|
| 106 |
+
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
|
| 107 |
+
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
|
| 108 |
+
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
|
| 109 |
+
|
| 110 |
+
All these datasets have been preprocessed and can be used for your experiments.
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
```python
|
| 114 |
+
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
### Supported Tasks and Leaderboards
|
| 118 |
+
|
| 119 |
+
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.
|
| 120 |
+
|
| 121 |
+
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
|
| 122 |
+
|
| 123 |
+
### Languages
|
| 124 |
+
|
| 125 |
+
All tasks are in English (`en`).
|
| 126 |
+
|
| 127 |
+
## Dataset Structure
|
| 128 |
+
|
| 129 |
+
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
|
| 130 |
+
- `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...."}`
|
| 131 |
+
- `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?"}`
|
| 132 |
+
- `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`
|
| 133 |
+
|
| 134 |
+
### Data Instances
|
| 135 |
+
|
| 136 |
+
A high level example of any beir dataset:
|
| 137 |
+
|
| 138 |
+
```python
|
| 139 |
+
corpus = {
|
| 140 |
+
"doc1" : {
|
| 141 |
+
"title": "Albert Einstein",
|
| 142 |
+
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
|
| 143 |
+
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
|
| 144 |
+
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
|
| 145 |
+
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
|
| 146 |
+
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
|
| 147 |
+
of the photoelectric effect', a pivotal step in the development of quantum theory."
|
| 148 |
+
},
|
| 149 |
+
"doc2" : {
|
| 150 |
+
"title": "", # Keep title an empty string if not present
|
| 151 |
+
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
|
| 152 |
+
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
|
| 153 |
+
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
|
| 154 |
+
},
|
| 155 |
+
}
|
| 156 |
+
|
| 157 |
+
queries = {
|
| 158 |
+
"q1" : "Who developed the mass-energy equivalence formula?",
|
| 159 |
+
"q2" : "Which beer is brewed with a large proportion of wheat?"
|
| 160 |
+
}
|
| 161 |
+
|
| 162 |
+
qrels = {
|
| 163 |
+
"q1" : {"doc1": 1},
|
| 164 |
+
"q2" : {"doc2": 1},
|
| 165 |
+
}
|
| 166 |
+
```
|
| 167 |
+
|
| 168 |
+
### Data Fields
|
| 169 |
+
|
| 170 |
+
Examples from all configurations have the following features:
|
| 171 |
+
|
| 172 |
+
### Corpus
|
| 173 |
+
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
|
| 174 |
+
- `_id`: a `string` feature representing the unique document id
|
| 175 |
+
- `title`: a `string` feature, denoting the title of the document.
|
| 176 |
+
- `text`: a `string` feature, denoting the text of the document.
|
| 177 |
+
|
| 178 |
+
### Queries
|
| 179 |
+
- `queries`: a `dict` feature representing the query, made up of:
|
| 180 |
+
- `_id`: a `string` feature representing the unique query id
|
| 181 |
+
- `text`: a `string` feature, denoting the text of the query.
|
| 182 |
+
|
| 183 |
+
### Qrels
|
| 184 |
+
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
|
| 185 |
+
- `_id`: a `string` feature representing the query id
|
| 186 |
+
- `_id`: a `string` feature, denoting the document id.
|
| 187 |
+
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
|
| 188 |
+
|
| 189 |
+
|
| 190 |
+
### Data Splits
|
| 191 |
+
|
| 192 |
+
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
|
| 193 |
+
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
|
| 194 |
+
| 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`` |
|
| 195 |
+
| 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`` |
|
| 196 |
+
| 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`` |
|
| 197 |
+
| 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) |
|
| 198 |
+
| 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`` |
|
| 199 |
+
| 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`` |
|
| 200 |
+
| 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`` |
|
| 201 |
+
| 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) |
|
| 202 |
+
| 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) |
|
| 203 |
+
| 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`` |
|
| 204 |
+
| 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`` |
|
| 205 |
+
| 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`` |
|
| 206 |
+
| 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`` |
|
| 207 |
+
| 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`` |
|
| 208 |
+
| 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`` |
|
| 209 |
+
| 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`` |
|
| 210 |
+
| 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`` |
|
| 211 |
+
| 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`` |
|
| 212 |
+
| 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) |
|
| 213 |
+
|
| 214 |
+
|
| 215 |
+
## Dataset Creation
|
| 216 |
+
|
| 217 |
+
### Curation Rationale
|
| 218 |
+
|
| 219 |
+
[Needs More Information]
|
| 220 |
+
|
| 221 |
+
### Source Data
|
| 222 |
+
|
| 223 |
+
#### Initial Data Collection and Normalization
|
| 224 |
+
|
| 225 |
+
[Needs More Information]
|
| 226 |
+
|
| 227 |
+
#### Who are the source language producers?
|
| 228 |
+
|
| 229 |
+
[Needs More Information]
|
| 230 |
+
|
| 231 |
+
### Annotations
|
| 232 |
+
|
| 233 |
+
#### Annotation process
|
| 234 |
+
|
| 235 |
+
[Needs More Information]
|
| 236 |
+
|
| 237 |
+
#### Who are the annotators?
|
| 238 |
+
|
| 239 |
+
[Needs More Information]
|
| 240 |
+
|
| 241 |
+
### Personal and Sensitive Information
|
| 242 |
+
|
| 243 |
+
[Needs More Information]
|
| 244 |
+
|
| 245 |
+
## Considerations for Using the Data
|
| 246 |
+
|
| 247 |
+
### Social Impact of Dataset
|
| 248 |
+
|
| 249 |
+
[Needs More Information]
|
| 250 |
+
|
| 251 |
+
### Discussion of Biases
|
| 252 |
+
|
| 253 |
+
[Needs More Information]
|
| 254 |
+
|
| 255 |
+
### Other Known Limitations
|
| 256 |
+
|
| 257 |
+
[Needs More Information]
|
| 258 |
+
|
| 259 |
+
## Additional Information
|
| 260 |
+
|
| 261 |
+
### Dataset Curators
|
| 262 |
+
|
| 263 |
+
[Needs More Information]
|
| 264 |
+
|
| 265 |
+
### Licensing Information
|
| 266 |
+
|
| 267 |
+
[Needs More Information]
|
| 268 |
+
|
| 269 |
+
### Citation Information
|
| 270 |
+
|
| 271 |
+
Cite as:
|
| 272 |
+
```
|
| 273 |
+
@inproceedings{
|
| 274 |
+
thakur2021beir,
|
| 275 |
+
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
|
| 276 |
+
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
|
| 277 |
+
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
|
| 278 |
+
year={2021},
|
| 279 |
+
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
|
| 280 |
+
}
|
| 281 |
+
```
|
| 282 |
+
|
| 283 |
+
### Contributions
|
| 284 |
+
|
| 285 |
+
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
test.tsv
ADDED
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| 1 |
+
query-id corpus-id score
|
| 2 |
+
test0 doc0 1
|
| 3 |
+
test0 doc1 1
|
| 4 |
+
test1 doc6 1
|
| 5 |
+
test2 doc10 1
|
| 6 |
+
test3 doc17 1
|
| 7 |
+
test3 doc18 1
|
| 8 |
+
test4 doc42 1
|
| 9 |
+
test5 doc50 1
|
| 10 |
+
test6 doc59 1
|
| 11 |
+
test6 doc63 1
|
| 12 |
+
test7 doc67 1
|
| 13 |
+
test8 doc86 1
|
| 14 |
+
test9 doc91 1
|
| 15 |
+
test10 doc118 1
|
| 16 |
+
test11 doc136 1
|
| 17 |
+
test12 doc153 1
|
| 18 |
+
test13 doc172 1
|
| 19 |
+
test14 doc293 1
|
| 20 |
+
test15 doc302 1
|
| 21 |
+
test16 doc305 1
|
| 22 |
+
test17 doc449 1
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