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FMAT 2024.3

CRAN release: 2024-03-22

  • The FMAT methodology paper has been accepted (March 14, 2024) for publication in the Journal of Personality and Social Psychology: Attitudes and Social Cognition (DOI: 10.1037/pspa0000396)!
  • Added BERT_download() (downloading models to local cache folder “%USERPROFILE%/.cache/huggingface”) to differentiate from FMAT_load() (loading saved models from local cache). But indeed FMAT_load() can also download models silently if they have not been downloaded.
  • Added gpu parameter (see Guidance for GPU Acceleration) in FMAT_run() to allow for specifying an NVIDIA GPU device on which the fill-mask pipeline will be allocated. GPU roughly performs 3x faster than CPU for the fill-mask pipeline. By default, FMAT_run() would automatically detect and use any available GPU with an installed CUDA-supported Python torch package (if not, it would use CPU).
  • Added running speed information (queries/min) for FMAT_run().
  • Added device information for BERT_download(), FMAT_load(), and FMAT_run().
  • Deprecated parallel in FMAT_run(): FMAT_run(model.names, data, gpu=TRUE) is the fastest.
  • A progress bar is displayed by default for progress in FMAT_run().

FMAT 2023.8

CRAN release: 2023-08-11

  • CRAN package publication.
  • Fixed bugs and improved functions.
  • Provided more examples.
  • Now use “YYYY.M” as package version number.

FMAT 0.0.9 (May 2023)

  • Initial public release on GitHub.

FMAT 0.0.1 (Jan 2023)

  • Designed basic functions.