beir 1.0.1

A Heterogeneous Benchmark for Information Retrieval

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 HuggingFace (HF) page: https://huggingface.co/BeIR.

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)

: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 HuggingFace.

Dataset	Website	BEIR-Name	Public?	Type	Queries	Corpus	Rel D/Q	Down-load	md5
MSMARCO	Homepage	msmarco	✅	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	8.05	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 HuggingFace'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}
}

The main contributors of this repository are:

• Nandan Thakur, Personal Website: nandan-thakur.com

Contact person: Nandan Thakur, [e-mail]

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
• HuggingFace

: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