Programmatic – a REPL for creating labeled data
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…
In plain words
Programmatic is an annotation tool that helps NLP practitioners build labeled datasets without manual labeling. Users write simple rules or functions to approximately label data, receive instant feedback across their entire dataset, and iterate to improve their rules. The tool then uses a Bayesian label model to convert these noisy annotations into a single clean dataset for training machine learning models. It's designed for teams who want to label millions of data points more quickly than traditional hand-labeling methods.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 label millions of datapoints in the time taken to hand-label hundreds. What we do differently from weak supervision packages like Snorkel/skweak[1] is to focus on UI to give near-instantaneous feedback. We love these packages but when we tried to iterate on labeling functions we had to write a ton of boilerplate code and wrestle with pandas to understand what was going on. Building a dataset programmatically requires you to grok the impact of labeling rules on a whole corpus of text. We’ve been told that the exploration tools and feedback makes the process feel game-like and even fun (!!). We built it because we see that getting labeled data remains a blocker for businesses using NLP today. We have a platform for active learning (see our Launch HN [2]) but we wanted to give software engineers and data scientists a way to build the datasets needed themselves and to make best use of subject-matter-experts’ time. The package is free and you can install it now as a pip package [2]. It supports NER / span extraction tasks at the moment and document classification will be added soon. To help improve it, we'd love to hear your feedback or any success/failures you’ve had with weak supervision in the past. [1]: We use a HMM model for NER tasks, and Naive-Bayes for classification using the two approaches given in the papers below: Pierre Lison, Jeremy Barnes, and Aliaksandr Hubin. "skweak: Weak Supervision Made Easy for NLP." https://arxiv.org/abs/2104.09683 (2021) Alex Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Chris Ré. "Data Programming: Creating Large Training Sets, Quickly" https://arxiv.org/abs/1605.07723 (NIPS 2016) [2]: Our Launch HN for our main active learning platform, Humanloop – https://news.ycombinator.com/item?id=23987353 [3]: Can install it directly here https://docs.programmatic.humanloop.com/tutorials/quick-star...
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