https://github.com/creolehustle/beir

A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.

https://github.com/creolehustle/beir

Repository

A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.

https://github.com/CreoleHustle/beir/blob/main/

    
        
    
    
            
    
    
        
    
    
        
    
    
        
    
    
        
    



    

        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](https://github.com/beir-cellar/beir/wiki).

For **models and datasets**, checkout out **Hugging Face (HF)** page: [https://huggingface.co/BeIR](https://huggingface.co/BeIR).

For **Leaderboard**, checkout out **Eval AI** page: [https://eval.ai/web/challenges/challenge-page/1897](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](https://openreview.net/forum?id=wCu6T5xFjeJ) (NeurIPS 2021, Datasets and Benchmarks Track)
- [Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard](https://arxiv.org/abs/2306.07471) (Arxiv 2023)

## :beers: Installation

Install via pip:

```python
pip install beir
```

If you want to build from source, use:

```python
$ 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](https://github.com/beir-cellar/beir/wiki/Examples-and-tutorials)** Wiki page. 

```python
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](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/)** or on **[Hugging Face](https://huggingface.co/BeIR)**.


| Dataset   | Website| BEIR-Name | Public? | 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    |  4.7 | No | [How to Reproduce?](https://github.com/beir-cellar/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/beir-cellar/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/beir-cellar/beir/blob/main/examples/dataset#1-trec-news) |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``|  | ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/beir-cellar/beir/blob/main/examples/dataset#3-robust04)  |
| 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`` |


## :beers: Additional Information

We also provide a variety of additional information in our **[Wiki](https://github.com/beir-cellar/beir/wiki)** page. 
Please refer to these pages for the following:


### Quick Start

- [Installing BEIR](https://github.com/beir-cellar/beir/wiki/Installing-beir)
- [Examples and Tutorials](https://github.com/beir-cellar/beir/wiki/Examples-and-tutorials)

### Datasets

- [Datasets Available](https://github.com/beir-cellar/beir/wiki/Datasets-available)
- [Multilingual Datasets](https://github.com/beir-cellar/beir/wiki/Multilingual-datasets)
- [Load your Custom Dataset](https://github.com/beir-cellar/beir/wiki/Load-your-custom-dataset)

### Models 
- [Models Available](https://github.com/beir-cellar/beir/wiki/Models-available)
- [Evaluate your Custom Model](https://github.com/beir-cellar/beir/wiki/Evaluate-your-custom-model)

### Metrics

- [Metrics Available](https://github.com/beir-cellar/beir/wiki/Metrics-available)

### Miscellaneous

- [BEIR Leaderboard](https://github.com/beir-cellar/beir/wiki/Leaderboard)
- [Couse Material on IR](https://github.com/beir-cellar/beir/wiki/Course-material-on-ir)

## :beers: Disclaimer

Similar to Tensorflow [datasets](https://github.com/tensorflow/datasets) or Hugging Face's [datasets](https://github.com/huggingface/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](https://arxiv.org/abs/2104.08663):

```
@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](https://arxiv.org/abs/2306.07471)
```
@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](https://github.com/Nthakur20), Personal Website: [nandan-thakur.com](https://nandan-thakur.com)

Contact person: Nandan Thakur, [nandant@gmail.com](mailto: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](http://www.ukp.tu-darmstadt.de/)
- [University of Waterloo](https://uwaterloo.ca/)
- [Hugging Face](https://huggingface.co/)

## :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 Rckl
    

Abhishek Srivastava
  

Owner

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