decile-team/spear
SPEAR: Programmatically label and build training data quickly.
Building machine learning models often requires a lot of labeled data, which can be time-consuming and expensive to create. This tool helps you quickly generate training data by defining simple rules or heuristics, even if your existing data is mostly unlabeled. It takes your raw, unlabeled data and a set of labeling rules, then outputs high-quality, programmatically labeled datasets ready for training. This is ideal for data scientists and ML engineers looking to accelerate data preparation for their models.
109 stars. No commits in the last 6 months.
Use this if you need to rapidly create labeled datasets for machine learning models from large amounts of raw or weakly labeled data, without extensive manual annotation.
Not ideal if you require highly precise, human-expert-level labels for every single data point and have the resources for extensive manual annotation.
Stars
109
Forks
22
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 27, 2024
Commits (30d)
0
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