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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Programming: Creating Large Training Sets, Quickly · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1605.07723/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1605.07723/feed.xml" rel="self" type="application/rss+xml" /><item><title>Data Programming: Creating Large Training Sets, Quickly — HN submission</title><link>https://news.ycombinator.com/item?id=30955699</link><guid isPermaLink="false">urn:hackerpapers:submission:1605.07723:30955699</guid><pubDate>Fri, 08 Apr 2022 11:35:31 GMT</pubDate><description>&lt;p&gt;Show HN: Programmatic – a REPL for creating labeled data&lt;/p&gt;&lt;p&gt;Hey HN, I’m Jordan cofounder of Humanloop (YC S20) and I’m excited to show you Programmatic — an annotation tool for building large labeled datasets for NLP without manual annotation. Programmatic is like a REPL for data annotation. You: 1. Write simple rules/functions that can approximately label the data 2. Get near-instant feedback across your entire corpus 3. Iterate and improve your rules Finally, it uses a Bayesian label model [1] to convert these noisy annotations into a single, large, clean dataset, which you can then use for training machine learning models. You can programmatically…&lt;/p&gt;&lt;p&gt;26 points; 5 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1605.07723/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
