Weak Supervised Learning on Ray using Snorkel British Sentiment - PowerPoint PPT Presentation

Aug 05, 2023 •133 likes •219 views

Weak Supervised Learning on Ray using Snorkel British Sentiment Analysis with LSTM Using Noisy Crowd Sami (sa894) - R244 Large-Scale Data Processing and Optimisation Snorkel generating training label Generating training labels with weak-

Weak Supervised Learning on Ray using Snorkel British Sentiment Analysis with LSTM Using Noisy Crowd Sami (sa894) - R244 Large-Scale Data Processing and Optimisation
Snorkel – generating training label • Generating training labels with weak- supervision • Apply to unlabelled large quintinites of data • Quantity over quality
Snorkel using Spark (Snark) [1] • Quantity = scales. Scale = Large heavy systems. But how heavy? • Load Data into SparkSQL and Dataframes • Pre-process the data and convert them to Snorkel format • Load batches of the snorkel data into Spark clusters • Distribute the workload using Spark actors to apply labelling functions • Spark is based on Scala.
Ray – Making Snorkel spark • Distributed execution backend optimised for ML tasks • Lightweight actor model, ideal for iterative process • E.g. going through batches of data and apply labelling function. • Extend the Snorkel codebase to allow an easy to use interface that uses Ray
The desired outcome • Comparison between Ray and Spark as a backend in applying labelling functions. • Labelling functions are short lived, Ray should in theory be better, but will it be? • Open source the interface and making it available to public.
Demo - How’s the weather
Sentiments Analysis - The British Version • The most common asked question in the UK. • Tweets sentiment analysis in regards to the weather: Positive, negative, neutral, not related, and unsure. • Snorkel to resolve conflicts in a noisy crowdsourced dataset, from Crowdflower. • Then use the denoised labels for an LSTM sentiments analysis • The idea is to dogfood the interface • Based on tutorial for learning Snorkel [0]
Progress - Somewhere ✔︐ Read the papers and related work • ✔︐ Go through the tutorial for Ray and Snorkel • ✔︐ Figure out the best level of abstraction (inspired by Snark) • ✔︐ write those slides • • Next • Implement RayAnnotationDistribution • Run the tests provided in the Snorkel package • Evaluate the performance of the test • Evaluate the performance of a real application – Sentiment analysis • Write down the results

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