hover 0.2.2

Data annotation done right: easy, fun, hyper-productive, and inducing insight.

Hover

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Hover is a machine teaching library that enables intuitive and effecient supervision. In other words, it provides a map where you hover over and label your data... differently. For instance, you can:

• :seedling: annotate an intuitively selected group of data points at a time
• :whale: cross-check with Snorkel-based distant supervision
• :ferris_wheel: UPCOMING throw a model in the loop and take advantage of active learning

Check out @phurwicz/hover-binder for a complete list of demo apps.

Features

Here we attempt a quick comparison with a few other packages that do machine teaching:

Package	Hover	Prodigy	Snorkel
Core idea	supervise like painting a picture	scriptable active learning	programmatic distant supervision
Annotates per	batch of just the size you find right	piece predicted to be the most valuable	the whole dataset as long as it fits in
Supports	all classification (text only atm)	text & images, audio, vidio, & more	text classification (for the most part)
Status	open-source	proprietary	open-source
Devs	indie	Explosion AI	Stanford / Snorkel AI
Related	many imports of the awesome Bokeh	builds on the Thinc/SpaCy stack	Variants: Snorkel Drybell, MeTaL, DeepDive
Vanilla usage	define a vectorizer and annotate away	choose a base model and annotate away	define labeling functions and apply away
Advanced usage	combine w/ active learning & snorkel	patterns / transformers / custom models	transforming / slicing functions
Hardcore usage	exploit hover.core templates	custom @prodigy.recipe	the upcoming Snorkel Flow

Hover claims the best deal of scale vs. precision thanks to

• the flexibility to use, or not use, any technique beyond annotating on a "map";
• the speed, or coarseness, of annotation being literally at your fingertips;
• the interaction between multiple "maps" that each serves a different but connected purpose.

Installation

To get the latest release version, you can use pip:

pip install hover

Installation through conda is not yet supported.

Resources

• Documentation

Dependencies

• ./requirements.txt lists the dependencies for installation.
• ./requirements-test.txt lists additional dependencies for the test suite.
• ./requirements-dev.txt lists the packages for developers.