snorkel VS skweak

Code for https://arxiv.org/abs/1605.07723 found: https://github.com/HazyResearch/snorkel

Thanks for pointing me in the right direction. Seems like there’s a few other approaches with weak supervision: https://github.com/NorskRegnesentral/skweak

Code for https://arxiv.org/abs/2104.09683 found: https://github.com/NorskRegnesentral/skweak

Hi Raza here, one of the other co-founders.

I know that HN likes to nerd out over technical details so thought I’d share a bit more on how we aggregate the noisy labels to clean them up.

At the moment we use the great Skweak [1] open source library to do this. Skweak uses an HMM to infer the most likely unobserved label given the evidence of the votes from each of the labelling functions.

This whole strategy of first training a label model and then training a neural net was pioneered by Snorkel. We’ve used this approach for now but we actually think there are big opportunities for improvement.

We’re working on an end-to-end approach that de-noises the labelling function and trains the model at the same time. So far we’ve seen improvements on the standard benchmarks [2] and are planning to submit to Neurips.

R

[1]: Skweak package: https://github.com/NorskRegnesentral/skweak

skweak.

For more advanced unsupervised labeling, you should check skweak

I'm the main developer behind skweak by the way, happy to hear you're interested in our toolkit :-) We do already have a small list of products (see https://github.com/NorskRegnesentral/skweak/blob/main/data/products.json) extracted from DBPedia and Wikidata, but it may not be exactly the type of products you're looking for.