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-0.0.14.tar.gz
(95.1 kB
view hashes)
Built Distribution
selfclean-0.0.14-py3-none-any.whl
(156.6 kB
view hashes)
Close
Hashes for selfclean-0.0.14-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2b2df677e92da421f734fd066d26c2e791677df45cf0d21ca6323db97d4f62b5 |
|
MD5 | 6b374040227da246fdfa682e1ced1839 |
|
BLAKE2b-256 | cdcaa37eb1df41e1568a87a1f9f6d6fa723141ce1331df287e0917c6442356ea |