Anserini + PyTerrier
Project description
Anserini is a retrieval toolkit built on top of Lucene. pyterrier-anserini provides a PyTerrier-compatible interface to Anserini, allowing you to easily run experiments and combine it with other systems.
Quick Start
You can install pyterrier-anserini with pip:
$ pip install git+https://github.com/seanmacavaney/pyterrier-anserini
pyterrier_anserini.AnseriniIndex is the main class for working with Anserini. For instance, you can download a pre-built index from HuggingFace and retrieve with BM25 using the following snippet:
>>> from pyterrier_anserini import AnseriniIndex
>>> index = AnseriniIndex.from_hf('macavaney/msmarco-passage.anserini')
>>> bm25 = index.bm25(include_fields=['contents'])
>>> bm25.search('terrier breeds')
qid query docno score rank contents
0 1 terrier breeds 5785957 11.9588 0 The Jack Russell Terrier and the Russell ...
1 1 terrier breeds 7455374 11.9343 1 FCI, ANKC, and IKC recognize the shorts a...
2 1 terrier breeds 1406578 11.8640 2 Norfolk terrier (English breed of small t...
3 1 terrier breeds 3984886 11.7518 3 Terrier Group is the name of a breed Grou...
4 1 terrier breeds 7728131 11.5660 4 The Yorkshire Terrier didn't begin as the...
...
Acknowledgements
This extension uses the Anserini package. If you use it, please be sure to cite Anserini:
@inproceedings{DBLP:conf/sigir/Yang0L17,
author = {Peilin Yang and
Hui Fang and
Jimmy Lin},
title = {Anserini: Enabling the Use of Lucene for Information Retrieval Research},
booktitle = {Proceedings of the 40th International {ACM} {SIGIR} Conference on
Research and Development in Information Retrieval, Shinjuku, Tokyo,
Japan, August 7-11, 2017},
pages = {1253--1256},
publisher = {{ACM}},
year = {2017},
url = {https://doi.org/10.1145/3077136.3080721},
doi = {10.1145/3077136.3080721}
}
This extension was built as part of the PyTerrier project. If you use it, please be sure to cite PyTerrier:
@inproceedings{DBLP:conf/cikm/MacdonaldTMO21,
author = {Craig Macdonald and
Nicola Tonellotto and
Sean MacAvaney and
Iadh Ounis},
title = {PyTerrier: Declarative Experimentation in Python from {BM25} to Dense
Retrieval},
booktitle = {{CIKM} '21: The 30th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Queensland, Australia, November
1 - 5, 2021},
pages = {4526--4533},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3459637.3482013},
doi = {10.1145/3459637.3482013}
}
This extension was written by Sean MacAvaney at the University of Glasgow and was based on an original implementation that was part of PyTerrier, written by Craig Macdonald. Check out the GitHub for a full list of contributors.
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