beir: A Python repository from VenkteshV

Paper | Installation | Quick Example | Datasets | Wiki | Hugging Face

🍻 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 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)

🍻 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

🍻 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

🍻 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)

🍻 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`

🍻 Additional Information

We also provide a variety of additional information in our Wiki page. Please refer to these pages for the following:

Quick Start

Datasets

Models

Metrics

Metrics Available

Miscellaneous

🍻 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!

🍻 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, 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.