A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.
Project description
SelfClean
SelfClean Paper | Data Cleaning Protocol Paper
A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates, and label errors.
Development Environment
Run make
for a list of possible targets.
Installation
Run these commands to install the project:
make init
make install
To run linters on all files:
pre-commit run --all-files
Code and test conventions
black
for code styleisort
for import sortingpytest
for running tests
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
selfclean-0.0.16.tar.gz
(96.4 kB
view hashes)
Built Distribution
selfclean-0.0.16-py3-none-any.whl
(158.1 kB
view hashes)
Close
Hashes for selfclean-0.0.16-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 984b8b1e0b7bebf348c7b42a811ab43fc0a538e02ad550b426c3268fa53f339c |
|
MD5 | a4480be7d9a1f86fede24b220daa78b0 |
|
BLAKE2b-256 | dcc06f2e012a25021cdbfa8ae80ccece66c899ca7a34e1d74cb8cd4122131fb5 |