NPLM Experiments

NPLM is a programmatic weak supervision system that supports (partial) labeling functions with supervision granularites ranging from class to a set of classes.

For the underlying weak supervision system, further documentation and tutorials, please see NPLM repo.

To use the experimental code, please first install the nplm package from NPLM repo.

This is the codebase for the experiments mentioned in Learning from Multiple Noisy Partial Labelers.

Code Organization & General Instructions

The code is organized by task, please refer to corresponding <dataset_name>pipeline.ipynb and run_end_model<dataset_name>.py (for text tasks). Please refer to votes for plf votes for the image tasks and annotators/ for interfaces and plfs for bot image and text tasks. For Object detection (vision) tasks, label modeling and end model training are included in <dataset_name>_pipeline.ipynb. For text classification tasks, PLFs curation and label modeling are in <dataset_name>pipeline.ipynb, and it can produce the probabilistic labels to train the end model with run_end_model<dataset_name>.py.

Environment Setupinstall

conda create --name nplm python=3.7
conda activate nplm
pip install -r requirements.txt
git clone https://github.com/BatsResearch/nplm.git
cd nplm; pip install .

Data

Preprocessed text data is included in this repo. To download the image data, please run

./download_image_dataset.sh

Checklist

• Code (v0.1)
• Text Data
• Image Data (Configuring Data Hosting)
• Instructions
• Documentation

Citation

Please cite the following paper if you are using our tool. Thank you!

Peilin Yu, Tiffany Ding, Stephen H. Bach. "Learning from Multiple Noisy Partial Labelers". Artificial Intelligence and Statistics (AISTATS), 2022.

@inproceedings{yu2022nplm,
  title = {Learning from Multiple Noisy Partial Labelers}, 
  author = {Yu, Peilin and Ding, Tiffany and Bach, Stephen H.}, 
  booktitle = {Artificial Intelligence and Statistics (AISTATS)}, 
  year = 2022, 
}