compose VS snorkel

Compare compose vs snorkel and see what are their differences.


A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning. (by alteryx)


A system for quickly generating training data with weak supervision (by snorkel-team)
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compose snorkel
1 5
421 5,503
1.9% 0.6%
8.8 5.6
14 days ago about 1 month ago
Python Python
BSD 3-clause "New" or "Revised" License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of compose. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-31.


Posts with mentions or reviews of snorkel. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-03.

What are some alternatives?

When comparing compose and snorkel you can also consider the following projects:

skweak - skweak: A software toolkit for weak supervision applied to NLP tasks

argilla - ✨Argilla: the open-source data curation platform for LLMs

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)

caer - High-performance Vision library in Python. Scale your research, not boilerplate.

snorkel-tutorials - A collection of tutorials for Snorkel

pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to:]