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Releases: ORNL/tx2

v1.2.1

21 Sep 18:37
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Added

  • Profiler script.

Changed

  • Performance enhancements by using batch operations in hugging face and torch.
    All interaction functions now need to support accepting an array of texts.
    (The encoding function has changed as a result.)

Fixed

  • Corrected bug in how summation is occuring in sort_salience_map() and added unit test.

v1.1.0

30 Aug 19:50
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Added

  • Suggested dev_env.yml environment for contributors.
  • API update to devices where "mps" is now checked in addition to "cuda".

Changed

  • Stopwords will be from tx2 init rather than nltk download.
  • Bumped package versions.
  • Specified package versions in requirements and setup.py.
  • Updating example jupyter notebooks to use new versions of packages.
  • Datasources in jupyter example notebooks.

Fixed

  • Updated to patched numpy version 1.22.
  • Potential issue in calc.frequent_words_in_cluster() where clusters of empty
    string values would stop computation.

v1.0.2

07 Apr 16:42
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Fixed

  • Wrapper function still expecting pandas series instead of numpy array.
  • missing nltk.download("stopwords")

v1.0.1

22 Mar 19:19
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Added

  • Example notebook demonstrating using TX2 with a huggingface model with
    sequence classification head, rather than a custom torch implementation.
  • Pre-commit hooks.

Changed

  • Add support for huggingface sequence classification head to default
    interaction functions.

Fixed

  • Code formatting to fix flake8-indicated issues.

v1.0.0

21 Dec 14:49
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We're considering this library's API relatively stable at this point, so we're making this our 1.0 release and will be following semver for any future updates!

Notable changes from pre-1.0 version:

  • The wrapper class now takes numpy arrays and/or pandas series rather than dataframes and column names for the training and testing data. (See the wrapper docs and the second code block in basic usage#default-approach for an example)
  • We have unit tests now! They can be run with pytest from the project root
  • The cuda torch device can be overridden for the default encoding handler, this is passed to the wrapper constructor with cuda_device
  • Version specifications are added for several of the required dependencies
  • Many various bug fixes