156 Open source projects that are alternatives of or similar to ir_datasets

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Solutions of the various test exams of the Information Retrieval course

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Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval" https://arxiv.org/abs/2112.07577

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A curated list of awesome papers related to pre-trained models for information retrieval (a.k.a., pretraining for IR).

Track any ip address with IP-Tracker. IP-Tracker is developed for Linux and Termux. you can retrieve any ip address information using IP-Tracker.

A simple tutorial of Lucene for LIS 501 Introduction to Text Mining students at the University of Wisconsin-Madison (Fall 2021).

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No description or website provided.

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Python implementations of the various methods used in FIRE 2019 conference.

25,100 queries from the Paralex corpus (Fader et al., 2013) annotated with human ratings of whether they are well-formed natural language questions.

The Web IR / NLP Group (WING)'s public reading group at the National University of Singapore.

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Kex is a python library for unsupervised keyword extraction from a document, providing an easy interface and benchmarks on 15 public datasets.

Financial Domain Question Answering with pre-trained BERT Language Model

Large, curated set of benchmark datasets for evaluating automatic keyphrase extraction algorithms.

Source code for: On the Effect of Low-Frequency Terms on Neural-IR Models, SIGIR'19

PowerShell Script to facilitate the processing of SRUM data for on-the-fly forensics and if needed threat hunting

Train a Word2Vec model or LSA model, and Implement Conceptual Search\Semantic Search in Solr\Lucene - Simon Hughes Dice.com, Dice Tech Jobs

SIGIR'21: Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track.

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This is official Pytorch code and datasets of the paper "Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News", EMNLP 2020.

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