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Releases: Ars-Linguistica/mlconjug3

v3.10.4

05 Sep 19:25
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Full Changelog: v3.10.3...v3.10.4

mlconjug3 v3.10.3

06 Feb 17:27
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Fixed display bug for single row tenses.

mlconjug3 v3.10.2

03 Feb 18:45
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-Retrained models with scikit-learn 1.2.1

mlconjug3 version 3.10.1

29 Jan 22:17
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We're thrilled to announce the release of mlconjug3 v3.10.1, packed with new features and improvements to make your conjugation experience smoother and faster than ever! 🚀

Here's what's new in this release:

  • Fixed display bug for English infinitives: You can now conjugate English verbs without encountering display issues. 🎉
  • More pervading caching for better performance: You'll now experience faster conjugation thanks to our improved caching mechanism. 💨
  • Improved documentation: We've made it easier for you to understand how mlconjug3 works and how you can use it in your own projects. 📚
  • Migrated project to ARS Linguistic: mlconjug3 is now a proud member of the ARS Linguistica organization, dedicated to developing free and accessible linguistic tools and resources for all. 🤝
  • We hope you enjoy the new release of mlconjug3 and its improved performance, and we're excited to continue working with ARS Linguistica to make linguistic tools and resources accessible to all. 💪

Join us in our mission to advance linguistic research, documentation, and education, and preserve linguistic diversity! 🧬

Full Changelog: 3.10.0...3.10.1

mlconjug3 version 3.10

27 Jan 00:07
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3.10.0 (2023-26-01)

We are excited to announce the release of mlconjug3 version 3.10! This release brings a number of new features and improvements to make your experience even better.

  • Config files for ease of use of the command line: You can now use config files to specify your preferences for the command line interface. This allows you to easily set your language, subject, output file, and file format, as well as customize the styling of the conjugation table.
  • Caching of xml conjugation files for faster loading: We have implemented caching of the xml conjugation files, which will greatly speed up the loading time of the conjugator.
  • Complete overhaul of the documentation: We have completely overhauled the documentation for mlconjug3, making it more comprehensive and user-friendly.
  • Signed release using sigstore: We have signed this release using sigstore, which ensures that the release has not been tampered with and comes from a trusted source.

mlconjug3v3.9.0

25 Jan 00:19
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  • Added support for Python 3.11
  • Updated dependencies to scikit-learn 1.2.0 with a noticeable speed inrease in training and inference performance.
  • Added ConjugatorTrainer class to easily and flexibly train new conjugators.
  • Added support for the rich library for better visuals. Now the conjugation tables are pretty printed and formatted.
  • Added multi-processing conjugation of multiple verbs for better performance on server applications using mlconjug3.
  • Added LRU cache to the the Conjugator.conjugate() method for better performance on applications with long running time.
  • Updated documentation with the new classes and more detailed information and visuals.
  • Refactor of internal classes, but keeping the same public interface in preparation for mlconjug3 v4.

mlconjug3 v3.8.3

02 Mar 09:04
v3.8.3
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  • Tweaked optimal learning parameters for the various language models.
  • Project Metadata in pyproject.toml file for building the package from source is 100% compliant.
  • Migrated build system to poetry / PyPa build.
  • Updated dependencies to scikit-learn 1.0.2 with a noticeable speed increase in training and inference performance.
  • Updated documentation.

mlconjug3 v3.8.2

30 Oct 12:26
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What's Changed

3.8.2 (2021-28-10)

  • Fixed issue with the pyproject.toml file which caused the dependency solver to fail on install when using pip instead of poetry.

3.8.1 (2021-28-10)

  • Updated documentation and Package metadata.

3.8.0 (2021-28-10)

  • Fixed bug that would silently skip uppercase characters in the CountVectorizer. See: scikit-learn/scikit-learn#19401
  • Retrained all models with fixed CountVectorizer.
  • Added full support for pyproject.toml file for building the package from source.
  • Migrated build system to poetry.
  • Updated dependen

Full Changelog: SekouDiaoNlp/mlconjug3@v3.7.22...v3.8.2

3.7.22

22 Oct 17:45
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Fixed installation issue.

3.7.21

22 Oct 13:14
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  • Retrained all models with scikit-learn 1.0.
  • Updated documentation.
  • Updated dependencies.
  • Removed support for python 3.6.