JayThibs/Weak-Supervised-Learning-Case-Study
Exploring NLP weak supervision approaches to train text classification models. The project is also a prototype for a semi-automated text data labelling platform. Approaches: Snorkel and Zero-Shot Learning.
This project offers a semi-automated way to label text data for classification tasks. You input raw, unlabelled text documents, and it helps you produce a dataset where each text is categorized. This is useful for data scientists or machine learning engineers who need to prepare text datasets for training classification models.
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Use this if you have a large volume of unlabelled text and need to quickly generate a labelled dataset for machine learning, reducing the manual effort of human annotators.
Not ideal if you require extremely high precision for critical applications where even small labelling errors could have significant consequences, as weak supervision may introduce some noise.
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Feb 18, 2024
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