[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

A curated list of programmatic weak supervision papers and resources

SPEAR: Programmatically label and build training data quickly.

Data programming by demonstration for information extraction and span annotation

Data Programming by Demonstration (DPBD) for Document Classification

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

Source code for the CSE 163: Intermediate Data Programming book (with code for practice problems)

Code for the KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler

Mongolian Polarity Detection in Weakly Supervised manner

This repository contains source code of our ACL 2021 paper **Data Programming using Semi-Supervision and Subset Selection**

A tool for automatically labelling discharge summaries into disease categories.

Source code of our ACL 2022 paper 'Learning to robustly aggregate labeling functions for semi-supervised data programming'

Process flow to generate labels on Text data using Snorkel and maintain DB to repurpose unlabelled data

An approach to curating naturally adversarial datasets.

Prepare a data set based on the model that you already have, then experiment with 'training' your 'Machine Learning' tool on this data. Did it recognize the model? Sorry, that was a stupid question. Why didn't it? You can find out with the help of these tools here.

Minimal weak-supervision framework for NLP with label functions, one-step weighting, and a denoised-labels classifier. Deterministic, tested, and demoable.

Interactive command-line exploration of US bikeshare data for Chicago, New York City, and Washington — filter by city and time period to view ride statistics. Built in Python for the Udacity Data Programming Nanodegree.

One common repo for all of my R projects

F# tutorial: building applications, data programming and tests

🔍 Monitor user activity and system events in real-time with Spear’s extensible framework for logging, reporting, and alerting.