Unstructured Datasets meet Foundation Models.

Meerkat is an open-source Python library, designed to help technical teams interactively wrangle images, videos, text documents and more with foundation models.

Our goal is to make foundation models a more reliable software abstraction for processing unstructured datasets. Read our blogpost to learn more.

Install Meerkat

$ pip install meerkat-ml

Notice

Meerkat is a research project, so users should expect rapid updates and rough edges. The current API is subject to change.

Data Frames for Images

A Meerkat DataFrame is a heterogeneous data structure with an API backed by foundation models.

Structured fields (e.g. numbers and dates) live alongside unstructured objects (e.g. images), and their tensor representations (e.g. embeddings).
Functions like mk.embed abstract away boiler-plate ML code, keeping the focus on the data.

import meerkat as mk 

df = mk.from_csv("paintings.csv")
df["img"] = mk.files("img_path")
df["embedding"] = mk.embed(
	df["img"], 
	engine="clip"
)

Interactivity in Python

Interactive data frame visualizations that allow you to control foundation models as they process your data.

Meerkat visualizations are implemented in Python, so they can be composed and customized in notebooks or data scripts.
Labeling is critical for instructing and validating foundation models. Labeling GUIs are a priority in Meerkat.

match = mk.gui.Match(df, 
	against="embedding", 
	engine="clip"
)
sorted_df = mk.sort(df, 
	by=match.criterion.name, 
	ascending=False
)
gallery = mk.gui.Gallery(sorted_df)
mk.gui.html.div([match, gallery])