dheeraj7596/ConWea
Code for the paper "Contextualized Weak Supervision for Text Classification"
This project helps data scientists and machine learning engineers classify text documents using minimal labeled data. You provide a dataset of text sentences and a small list of 'seed words' for each category you want to identify. The system then automatically generates a text classifier, even for fine-grained categories, by understanding the context of words.
No commits in the last 6 months.
Use this if you need to categorize a large volume of text but have very few examples of what each category looks like, and manually labeling data is too time-consuming or expensive.
Not ideal if you already have a large, fully labeled dataset for your text classification task, as this project focuses on weak supervision with minimal labels.
Stars
52
Forks
20
Language
Python
License
MIT
Category
Last pushed
Mar 25, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/dheeraj7596/ConWea"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
kk7nc/HDLTex
HDLTex: Hierarchical Deep Learning for Text Classification
richliao/textClassifier
Text classifier for Hierarchical Attention Networks for Document Classification
RandolphVI/Hierarchical-Multi-Label-Text-Classification
The code of CIKM'19 paper《Hierarchical Multi-label Text Classification: An Attention-based...
yumeng5/LOTClass
[EMNLP 2020] Text Classification Using Label Names Only: A Language Model Self-Training Approach
sgrvinod/a-PyTorch-Tutorial-to-Text-Classification
Hierarchical Attention Networks | a PyTorch Tutorial to Text Classification