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Pyserini

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Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with our group's Anserini IR toolkit, which is built on Lucene. Retrieval using dense representations is provided via integration with Facebook's Faiss library.

Pyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture. Our toolkit is self-contained as a standard Python package and comes with queries, relevance judgments, pre-built indexes, and evaluation scripts for many commonly used IR test collections With Pyserini, it's easy to reproduce runs on a number of standard IR test collections!

For additional details, our paper in SIGIR 2021 provides a nice overview.

🎬 Installation

Install via PyPI (requires Python 3.10+):

pip install pyserini

Sparse retrieval depends on Anserini, which is itself built on Lucene, and thus Java 11.

Dense retrieval depends on neural networks and requires a more complex set of dependencies. A pip installation will automatically pull in the πŸ€— Transformers library to satisfy the package requirements. Pyserini also depends on PyTorch and Faiss, but since these packages may require platform-specific custom configuration, they are not explicitly listed in the package requirements. We leave the installation of these packages to you.

The software ecosystem is rapidly evolving and a potential source of frustration is incompatibility among different versions of underlying dependencies. We provide additional detailed installation instructions here.

If you're planning on just using Pyserini, then the pip instructions above are fine. However, if you're planning on contributing to the codebase or want to work with the latest not-yet-released features, you'll need a development installation. Instructions are provided here.

πŸ™‹ How do I search?

Pyserini supports the following classes of retrieval models:

See this guide (same as the links above) for details on how to search common corpora in IR and NLP research (e.g., MS MARCO, NaturalQuestions, BEIR, etc.) using indexes that we have already built for you.

Once you get the top-k results, you'll actually want to fetch the document text... See this guide for how.

πŸ™‹ How do I index my own corpus?

Well, it depends on what type of retrieval model you want to search with:

The steps are different for different classes of models: this guide (same as the links above) describes the details.

πŸ™‹ Additional FAQs

βš—οΈ Reproducibility

With Pyserini, it's easy to reproduce runs on a number of standard IR test collections! We provide a number of pre-built indexes that directly support reproducibility "out of the box".

In our SIGIR 2022 paper, we introduced "two-click reproductions" that allow anyone to reproduce experimental runs with only two clicks (i.e., copy and paste). Documentation is organized into reproduction matrices for different corpora that provide a summary of different experimental conditions and query sets:

For more details, see our paper on Building a Culture of Reproducibility in Academic Research.

Additional reproduction guides below provide detailed step-by-step instructions.

Sparse Retrieval

Sparse Retrieval

Dense Retrieval

Dense Retrieval

Hybrid Sparse-Dense Retrieval

Hybrid Sparse-Dense Retrieval

Available Corpora

Available Corpora

Corpora Size Checksum
MS MARCO V1 passage: uniCOIL (noexp) 2.7 GB f17ddd8c7c00ff121c3c3b147d2e17d8
MS MARCO V1 passage: uniCOIL (d2q-T5) 3.4 GB 78eef752c78c8691f7d61600ceed306f
MS MARCO V1 doc: uniCOIL (noexp) 11 GB 11b226e1cacd9c8ae0a660fd14cdd710
MS MARCO V1 doc: uniCOIL (d2q-T5) 19 GB 6a00e2c0c375cb1e52c83ae5ac377ebb
MS MARCO V2 passage: uniCOIL (noexp) 24 GB d9cc1ed3049746e68a2c91bf90e5212d
MS MARCO V2 passage: uniCOIL (d2q-T5) 41 GB 1949a00bfd5e1f1a230a04bbc1f01539
MS MARCO V2 doc: uniCOIL (noexp) 55 GB 97ba262c497164de1054f357caea0c63
MS MARCO V2 doc: uniCOIL (d2q-T5) 72 GB c5639748c2cbad0152e10b0ebde3b804

πŸ“ƒ Additional Documentation

πŸ“œοΈ Release History

older... (and historic notes)

πŸ“œοΈ Historical Notes

⁉️ Lucene 8 to Lucene 9 Transition. In 2022, Pyserini underwent a transition from Lucene 8 to Lucene 9. Most of the pre-built indexes have been rebuilt using Lucene 9, but there are a few still based on Lucene 8.

More details:

Explanations:

  • What's the impact? Indexes built with Lucene 8 are not fully compatible with Lucene 9 code (see Anserini #1952). The workaround is to disable consistent tie-breaking, which happens automatically if a Lucene 8 index is detected by Pyserini. However, Lucene 9 code running on Lucene 8 indexes will give slightly different results than Lucene 8 code running on Lucene 8 indexes. Note that Lucene 8 code is not able to read indexes built with Lucene 9.

  • Why is this necessary? Although disruptive, an upgrade to Lucene 9 is necessary to take advantage of Lucene's HNSW indexes, which will increase the capabilities of Pyserini and open up the design space of dense/sparse hybrids.

With v0.11.0.0 and before, Pyserini versions adopted the convention of X.Y.Z.W, where X.Y.Z tracks the version of Anserini, and W is used to distinguish different releases on the Python end. Starting with Anserini v0.12.0, Anserini and Pyserini versions have become decoupled.

Anserini is designed to work with JDK 11. There was a JRE path change above JDK 9 that breaks pyjnius 1.2.0, as documented in this issue, also reported in Anserini here and here. This issue was fixed with pyjnius 1.2.1 (released December 2019). The previous error was documented in this notebook and this notebook documents the fix.

✨ References

If you use Pyserini, please cite the following paper:

@INPROCEEDINGS{Lin_etal_SIGIR2021_Pyserini,
   author = "Jimmy Lin and Xueguang Ma and Sheng-Chieh Lin and Jheng-Hong Yang and Ronak Pradeep and Rodrigo Nogueira",
   title = "{Pyserini}: A {Python} Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations",
   booktitle = "Proceedings of the 44th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021)",
   year = 2021,
   pages = "2356--2362",
}

πŸ™ Acknowledgments

This research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.