Resource library

In this episode of Science Talks, Explosion AI’s Ines Montani sat down with Snorkel AI’s Braden Hancock to discuss her path into machine learning, key design decisions behind the popular spaCy library for industrial-strength NLP, the importance of bringing together different stakeholders in the ML development process, and more.This episode is part of the #ScienceTalks video series hosted by the Snorkel AI team. You…

This work showcases the Reference-based Weak Supervision (RWS), a fully automatic large-scale data pipeline that harvests high-quality weakly- supervised answer sentences from Web data, only requiring a question-reference pair as input.

This paper introduces a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.

Over the past year, we’ve worked hard to deliver Snorkel Flow, the first AI platform to provide all the power of machine learning without the pains of hand-labeling. Snorkel Flow lets you label data programmatically, train models flexibly, improve performance iteratively, and deploy AI applications quickly. We are incredibly proud of the value that our customers, including two of the…

Labeling data for modern machine learning is expensive and time-consuming. Latent variable models can be used to infer labels from weaker, easier-to-acquire sources operating on unlabeled data. Such models can also be trained using labeled data, presenting a key question: should a user invest in few labeled or many unlabeled points? We answer this via a framework centered on model…

Are the data in a large US electronic health record (EHR) complete and accurate enough to track trends in implant use and to assess the durability of implants (hereafter referred to as implant survivorship)? In this cohort study, EHR records of patients who had total hip arthroplasty in all Veterans Health Administration hospitals since 2000 were automatically reviewed using novel…

In this episode of Science Talks, Sebastian Ruder, Research Scientist at DeepMind, shares his thoughts on making AI practical with Snorkel AI’s Braden Hancock. This conversation covers progress made in the NLP domain with emerging research, new benchmarks like SuperGLUE, rich repositories and news sources that keep you in the loop and on top of what’s new in NLP, and more.

In this episode of ScienceTalks, Snorkel AI’s Braden Hancock Hugging Face’s Chief Science Officer, Thomas Wolf. Thomas shares his story about how he got into machine learning and discusses important design decisions behind the widely adopted Transformers library, as well as the challenges of bringing research projects into production. ScienceTalks is an interview series from Snorkel AI, highlighting some of the best work and ideas to make AI practical.

We’ll analyze major sources of errors during the four steps of building AI applications: data labeling, feature engineering, model training, and model evaluation.