SPEAR: Programmatically label and build training data quickly.
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Updated
Jun 27, 2024 - Jupyter Notebook
SPEAR: Programmatically label and build training data quickly.
An approach to curating naturally adversarial datasets.
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
Code for the KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
A tool for automatically labelling discharge summaries into disease categories.
A curated list of awesome Weak-Supervision-Sequence-Labeling (WSSL) papers, methods & resources.
A curated list of programmatic weak supervision papers and resources
Source code for the CSE 163: Intermediate Data Programming book (with code for practice problems)
One common repo for all of my R projects
Mongolian Polarity Detection in Weakly Supervised manner
Source code of our ACL 2022 paper 'Learning to robustly aggregate labeling functions for semi-supervised data programming'
This repository contains source code of our ACL 2021 paper **Data Programming using Semi-Supervision and Subset Selection**
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
F# tutorial: building applications, data programming and tests
Process flow to generate labels on Text data using Snorkel and maintain DB to repurpose unlabelled data
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.
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