Keywords
License
- Apache-2.0
- Yesattribution
- Permissivelinking
- Permissivedistribution
- Permissivemodification
- Yespatent grant
- Yesprivate use
- Permissivesublicensing
- Notrademark grant
Downloads
Readme
Paper | Installation | Quick Example | Datasets | Wiki | Hugging Face
:beers: What is it?
BEIR is a heterogeneous benchmark containing diverse IR tasks. It also provides a common and easy framework for evaluation of your NLP-based retrieval models within the benchmark.
For an overview, checkout our new wiki page: https://github.com/beir-cellar/beir/wiki.
For models and datasets, checkout out Hugging Face (HF) page: https://huggingface.co/BeIR.
For Leaderboard, checkout out Eval AI page: https://eval.ai/web/challenges/challenge-page/1897.
For more information, checkout out our publications:
- BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (NeurIPS 2021, Datasets and Benchmarks Track)
- Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard (Arxiv 2023)
:beers: Installation
Install via pip:
pip install beir
If you want to build from source, use:
$ git clone https://github.com/beir-cellar/beir.git
$ cd beir
$ pip install -e .
Tested with python versions 3.6 and 3.7
:beers: Features
- Preprocess your own IR dataset or use one of the already-preprocessed 17 benchmark datasets
- Wide settings included, covers diverse benchmarks useful for both academia and industry
- Includes well-known retrieval architectures (lexical, dense, sparse and reranking-based)
- Add and evaluate your own model in a easy framework using different state-of-the-art evaluation metrics
:beers: Quick Example
For other example codes, please refer to our Examples and Tutorials Wiki page.
from beir import util, LoggingHandler
from beir.retrieval import models
from beir.datasets.data_loader import GenericDataLoader
from beir.retrieval.evaluation import EvaluateRetrieval
from beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES
import logging
import pathlib, os
#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
handlers=[LoggingHandler()])
#### /print debug information to stdout
#### Download scifact.zip dataset and unzip the dataset
dataset = "scifact"
url = "https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip".format(dataset)
out_dir = os.path.join(pathlib.Path(__file__).parent.absolute(), "datasets")
data_path = util.download_and_unzip(url, out_dir)
#### Provide the data_path where scifact has been downloaded and unzipped
corpus, queries, qrels = GenericDataLoader(data_folder=data_path).load(split="test")
#### Load the SBERT model and retrieve using cosine-similarity
model = DRES(models.SentenceBERT("msmarco-distilbert-base-tas-b"), batch_size=16)
retriever = EvaluateRetrieval(model, score_function="dot") # or "cos_sim" for cosine similarity
results = retriever.retrieve(corpus, queries)
#### Evaluate your model with NDCG@k, MAP@K, Recall@K and Precision@K where k = [1,3,5,10,100,1000]
ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)
:beers: Available Datasets
Command to generate md5hash using Terminal: md5sum filename.zip
.
You can view all datasets available here or on Hugging Face.
Dataset Website BEIR-Name Public? Type Queries Corpus Rel D/Q Down-load md5 MSMARCO Homepagemsmarco
✅
train
dev
test
6,980
8.84M
1.1
Link
444067daf65d982533ea17ebd59501e4
TREC-COVID
Homepage
trec-covid
✅
test
50
171K
493.5
Link
ce62140cb23feb9becf6270d0d1fe6d1
NFCorpus
Homepage
nfcorpus
✅
train
dev
test
323
3.6K
38.2
Link
a89dba18a62ef92f7d323ec890a0d38d
BioASQ
Homepage
bioasq
❌
train
test
500
14.91M
4.7
No
How to Reproduce?
NQ
Homepage
nq
✅
train
test
3,452
2.68M
1.2
Link
d4d3d2e48787a744b6f6e691ff534307
HotpotQA
Homepage
hotpotqa
✅
train
dev
test
7,405
5.23M
2.0
Link
f412724f78b0d91183a0e86805e16114
FiQA-2018
Homepage
fiqa
✅
train
dev
test
648
57K
2.6
Link
17918ed23cd04fb15047f73e6c3bd9d9
Signal-1M(RT)
Homepage
signal1m
❌
test
97
2.86M
19.6
No
How to Reproduce?
TREC-NEWS
Homepage
trec-news
❌
test
57
595K
19.6
No
How to Reproduce?
Robust04
Homepage
robust04
❌
test
249
528K
69.9
No
How to Reproduce?
ArguAna
Homepage
arguana
✅
test
1,406
8.67K
1.0
Link
8ad3e3c2a5867cdced806d6503f29b99
Touche-2020
Homepage
webis-touche2020
✅
test
49
382K
19.0
Link
46f650ba5a527fc69e0a6521c5a23563
CQADupstack
Homepage
cqadupstack
✅
test
13,145
457K
1.4
Link
4e41456d7df8ee7760a7f866133bda78
Quora
Homepage
quora
✅
dev
test
10,000
523K
1.6
Link
18fb154900ba42a600f84b839c173167
DBPedia
Homepage
dbpedia-entity
✅
dev
test
400
4.63M
38.2
Link
c2a39eb420a3164af735795df012ac2c
SCIDOCS
Homepage
scidocs
✅
test
1,000
25K
4.9
Link
38121350fc3a4d2f48850f6aff52e4a9
FEVER
Homepage
fever
✅
train
dev
test
6,666
5.42M
1.2
Link
5a818580227bfb4b35bb6fa46d9b6c03
Climate-FEVER
Homepage
climate-fever
✅
test
1,535
5.42M
3.0
Link
8b66f0a9126c521bae2bde127b4dc99d
SciFact
Homepage
scifact
✅
train
test
300
5K
1.1
Link
5f7d1de60b170fc8027bb7898e2efca1
:beers: Additional Information
We also provide a variety of additional information in our Wiki page. Please refer to these pages for the following:
Quick Start
- Installing BEIR
- Examples and Tutorials
Datasets
- Datasets Available
- Multilingual Datasets
- Load your Custom Dataset
Models
- Models Available
- Evaluate your Custom Model
Metrics
- Metrics Available
Miscellaneous
- BEIR Leaderboard
- Couse Material on IR
:beers: Disclaimer
Similar to Tensorflow datasets or Hugging Face’s datasets library, we just downloaded and prepared public datasets. We only distribute these datasets in a specific format, but we do not vouch for their quality or fairness, or claim that you have license to use the dataset. It remains the user’s responsibility to determine whether you as a user have permission to use the dataset under the dataset’s license and to cite the right owner of the dataset.
If you’re a dataset owner and wish to update any part of it, or do not want your dataset to be included in this library, feel free to post an issue here or make a pull request!
If you’re a dataset owner and wish to include your dataset or model in this library, feel free to post an issue here or make a pull request!
:beers: Citing & Authors
If you find this repository helpful, feel free to cite our publication BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models:
@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}
}
If you use any baseline score from the BEIR leaderboard, feel free to cite our publication Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard
@misc{kamalloo2023resources,
title={Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard},
author={Ehsan Kamalloo and Nandan Thakur and Carlos Lassance and Xueguang Ma and Jheng-Hong Yang and Jimmy Lin},
year={2023},
eprint={2306.07471},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
The main contributors of this repository are:
- Nandan Thakur, Personal Website: nandan-thakur.com
Contact person: Nandan Thakur, nandant@gmail.com
Don’t hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn’t be) or if you have further questions.
This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.
:beers: Collaboration
The BEIR Benchmark has been made possible due to a collaborative effort of the following universities and organizations:
- UKP Lab, Technical University of Darmstadt
- University of Waterloo
- Hugging Face
:beers: Contributors
Thanks go to all these wonderful collaborations for their contribution towards the BEIR benchmark:
Nandan Thakur
Nils Reimers
Iryna Gurevych
Jimmy Lin
Andreas Rücklé
Abhishek Srivastava