Snorkel - A system for quickly generating training data with weak supervision
Snorkel is a system for rapidly creating, modeling, and managing training data, currently focused on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.
Today's state-of-the-art machine learning models require massive labeled training sets--which usually do not exist for real-world applications. Instead, Snorkel is based around the new data programming paradigm, in which the developer focuses on writing a set of labeling functions, which are just scripts that programmatically label data. The resulting labels are noisy, but Snorkel automatically models this process—learning, essentially, which labeling functions are more accurate than others—and then uses this to train an end model (for example, a deep neural network in TensorFlow).
Today's state-of-the-art machine learning models require massive labeled training sets--which usually do not exist for real-world applications. Instead, Snorkel is based around the new data programming paradigm, in which the developer focuses on writing a set of labeling functions, which are just scripts that programmatically label data. The resulting labels are noisy, but Snorkel automatically models this process—learning, essentially, which labeling functions are more accurate than others—and then uses this to train an end model (for example, a deep neural network in TensorFlow).
http://snorkel.stanford.edu
https://github.com/HazyResearch/snorkel
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