Changelog • FMAT

FMAT 2024.4

CRAN release: 2024-04-29

Added BERT_vocab() and ICC_models().
Improved summary.fmat(), FMAT_query(), and FMAT_run() (significantly faster because now it can simultaneously estimate all [MASK] options for each unique query sentence, with running time only depending on the number of unique queries but not on the number of [MASK] options).
If you use the reticulate package version ≥ 1.36.1, then FMAT should be updated to ≥ 2024.4. Otherwise, out-of-vocabulary [MASK] words may not be identified and marked. Now FMAT_run() directly uses model vocabulary and token ID to match [MASK] words. To check if a [MASK] word is in the model vocabulary, please use BERT_vocab().

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().

CRAN release: 2023-08-11