flexneuart 1.2.6
FlexNeuART (flex-noo-art) is a Flexible classic and NeurAl Retrieval Toolkit
Requires Python: >=3.6

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## FlexNeuART (flex-noo-art)
Flexible classic and NeurAl Retrieval Toolkit, or shortly `FlexNeuART` (**intended pronunciation** flex-noo-art) 
is a substantially reworked [`knn4qa` package](legacy_docs/knn4qa.md).  The overview can be found in our EMNLP OSS workshop paper: 
[Flexible retrieval with NMSLIB and FlexNeuART, 2020. Leonid Boytsov, Eric Nyberg](https://arxiv.org/abs/2010.14848).

In Aug-Dec 2020, we used this framework to generate best traditional and/or neural runs 
in the [MSMARCO Document ranking task](https://microsoft.github.io/msmarco/#docranking).
In fact, our best traditional (non-neural) run slightly outperformed a couple of neural submissions.
Please, see our write-up for details: [Boytsov, Leonid. "Traditional IR rivals neural models on the MS MARCO Document Ranking Leaderboard." arXiv preprint arXiv:2012.08020 (2020)](https://arxiv.org/abs/2012.08020).

In 2021, after being outsripped by a number of participants, we again advanced to a good position with a help of newly implemented models for ranking long documents. Please, see our write-up for details: Boytsov, L., Lin, T., Gao, F., Zhao, Y., Huang, J., & Nyberg, E. (2022). [Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding](https://arxiv.org/abs/2207.01262). At the moment of writing (October 2022), we have competitive submissions on **both** MS MARCO leaderboards.

Code corresponding to Neural Model 1 is not included as this may be subject to a third party patent. 
This model (together with its non-contextualized variant) is described and evaluated in our ECIR 2021 paper:
[Boytsov, Leonid, and Zico Kolter. "Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits." ECIR 2021](https://arxiv.org/abs/2102.06815).

In terms of pure effectiveness on long documents, other models (CEDR & PARADE) seem to be perform equally well (or somewhat better). They are available [in our codebase](flexneuart/models/README.md). We are not aware of the patents inhibiting the use of the traditional (non-neural) Model 1.

## Objectives

Develop & maintain a (relatively) light-weight modular middleware useful primarily for: 
* Research
* Education
* Evaluation & leaderboarding

## Main features

* Dense, sparse, or dense-sparse retrieval using Lucene and NMSLIB (dense embeddings can be created using any Sentence BERT model).
* Multi-field multi-level forward indices (+parent-child field relations) that can store 
  parsed and "raw" text input as well as sparse and dense vectors.
* Forward indices can be created in append-only mode, which requires much less RAM.
* Pluggable generic rankers (via a server)
* SOTA neural ([PARADE, BERT FirstP/MaxP/Sum, Longformer, COLBERT (re-ranking), dot-product Senence BERT models](flexneuart/models/README.md)) and non-neural models (multi-field BM25, IBM Model 1).
* Multi-GPU training **and** inference with out-of-the box support for ensembling
* Basic experimentation framework (+LETOR)
* Python API to use retrievers and rankers as well as to access indexed data.


## Documentation

* [Usage notebooks covering installation & most functionality (including experimentation and Python API demo)](demo/README.md)
* [Legacy notebooks for MS MARCO and Yahoo Answers](legacy_docs/README.md)
* [Former life (as a knn4qa package), including acknowledgements and publications](legacy_docs/knn4qa.md)

We support [a number of neural BERT-based ranking models](flexneuart/models/README.md) as well as strong traditional
ranking models including IBM Model 1 (description of non-neural rankers to follow).

The framework supports data in generic JSONL format. We provide conversion (and in some cases download) scripts for the following collections:
* [Configurable dataset processing of standard datasets](/scripts/data_convert/ir_datasets/README.md) provided by [ir-datasets](https://ir-datasets.com/).
* MS MARCO [v1](scripts/data_convert/msmarco) and [v2](scripts/data_convert/msmarco_v2) (documents and passages)
* [Wikipedia DPR](scripts/data_convert/wikipedia_dpr/README.md) (Natural Questions, Trivia QA, SQuAD)
* [Yahoo Answers](/scripts/data_convert/yahoo_answers)
* [Cranfield](scripts/data_convert/cranfield/README.md) (a small toy collection)

## Acknowledgements

For neural network training FlexNeuART incorporates
a substantially re-worked variant of CEDR ([MacAvaney et al' 2019](https://github.com/Georgetown-IR-Lab/cedr)).