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Please check the latest news (change log) and keep this package updated.

FMAT 2024.5

• Added BERT_info().
• Added add.tokens and add.method parameters for BERT_vocab() and FMAT_run(): An experimental functionality to add new tokens (e.g., out-of-vocabulary words, compound words, or even phrases) as [MASK] options. Validation is still needed for this novel practice (one of my ongoing projects), so currently please only use at your own risk, waiting until the publication of my validation work.
• All functions except BERT_download() now import local model files only, without automatically downloading models. Users must first use BERT_download() to download models.
• Deprecating FMAT_load(): Better to use FMAT_run() directly.

FMAT 2024.4

• 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().

FMAT 2024.3

• 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 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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