microsoft/ASTRA
Self-training with Weak Supervision (NAACL 2021)
This framework helps data scientists and machine learning engineers create robust classification models faster when manually labeling large datasets is too expensive. By combining domain-specific rules, a small amount of labeled data, and a large pool of unlabeled data, it automatically generates high-quality weak labels. The output is a trained deep neural network capable of accurately classifying new instances.
163 stars. No commits in the last 6 months.
Use this if you need to train a classification model but lack sufficient manually labeled data and can define some heuristic rules for your domain.
Not ideal if you already have large-scale, high-quality labeled datasets or if your task doesn't easily lend itself to rule-based weak supervision.
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Language
Python
License
MIT
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Last pushed
Jul 24, 2023
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