Cleanlab

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1.0.1

* **The primary purpose of this release** is to preserve the functionality of cleanlab (all versions up to 1.0.1) in the new docs prior to the launch of cleanlab 2.0 which significantly change the API.
* Launched in preparation for Cleanlab 2.0.
* Mostly superficial.

For users (+ sometimes developers):
- This releases the new sphinx docs for cleanlab 1.0 documentation (in preparation for CL 2.0)
- Several superficial bug fixes (reduce error printing, fix broken urls, clarify links)
- Extensive docs/README updates
- Support was added for Conda Installation
- Moved to AGPL-3 license
- Added tutorials and a learning section for Cleanlab

For developers:
- Moved to GitHub Actions CI
- Significantly shrunk the clone size to a few MB from 100MB+

1.0

cleanlab 1.0 supports the most common versions of python (2, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8.) and operating systems (linux, macOS, Windows). It works with any deep learning or machine learning library by working with model outputs, regardless of where they come from. cleanlab also has built-in support now for new research from other scientists (e.g. Co-Teaching) outside of our group at MIT.

More details about new features of cleanlab 1.0 below:

- Added Amazon Reviews NLP to cleanlab/examples
- cleanlab now supports python 2, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8.
- Users have used cleanlab with python version 3.9 (use at your own risk!)
- Added more testing. All tests pass on windows/linux/macOS.
- Update to GNU GPL-3+ License.
- Added documentation: https://cleanlab.readthedocs.io/
- The cleanlab "confident learning" paper is published in the Journal of AI Research: https://jair.org/index.php/jair/article/view/12125
- Added funding, community and contributing guidelines
- Fixed several errors in cleanlab/examples
- cleanlab now supports Windows, macOS, Linux, and unix systems
- Many examples added to the README and docs
- cleanlab now natively supports Co-Teaching for learning with noisy labels (reqs python3, PyTorch 1.4)
- cleanlab built in support with handwritten datasets (besides MNIST)
- cleanlab built in support for CIFAR dataset
- Multiprocessing fixed for windows systems
- Adhered all core modules to PEP-8 styling.
- cleanlab is now installable via conda (besides pip).
- Extensive benchmarking of cleanlab methods published.
- Cleanlab now provides future features planned in cleanlab/version.py
- Added confidentlearning-reproduce as a separate repo to reproduce state-of-the-art results.

0.1.0

Alpha release of cleanlab.

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