Changelog • FMAT

FMAT 2025.8

CRAN release: 2025-08-19

CRAN release: 2025-04-08

Added BERT_remove(): Remove models from local cache folder.
Added fill_mask() and fill_mask_check(): These functions are only for technical check (i.e., checking the raw results of fill-mask pipeline). Normal users should usually use FMAT_run().
Added pattern.special argument for FMAT_run(): Regular expression patterns (matching model names) for special model cases that are uncased or require a special prefix character in certain situations.
- WARNING: As the developer is not able to check all models, users are responsible for checking the models they would use and for modifying this argument if necessary.
  - prefix.u2581: adding prefix ⁠\u2581⁠ for all mask words
  - prefix.u0120: adding prefix ⁠\u0120⁠ for only non-starting mask words
Improved set_cache_folder(), BERT_download(), BERT_info(), and BERT_info_date().
- Now model information read from model objects BERT_info() and model initial commit date scraped from HuggingFace BERT_info_date() will be saved in subfolders of local cache: /.info/ and /.date/, respectively.
Deprecated FMAT_load().
Fixed “R Session Aborted” issue on MacOS (see #1).
Set necessary environment variables automatically when library(FMAT):
- Sys.setenv("HF_HUB_DISABLE_SYMLINKS_WARNING" = "1")
- Sys.setenv("TF_ENABLE_ONEDNN_OPTS" = "0")
- Sys.setenv("KMP_DUPLICATE_LIB_OK" = "TRUE")
- Sys.setenv("OMP_NUM_THREADS" = "1")

CRAN release: 2025-03-19

Added set_cache_folder(): Set (change) HuggingFace cache folder temporarily.
- Keep in mind: This function takes effect only for the current R session temporarily, so you should run this each time before you use other FMAT functions in an R session.
Added BERT_info_date(): Scrape the initial commit date of BERT models from HuggingFace.
Improved BERT_download() and BERT_info().
Updated the formal citation format of the JPSP article.

CRAN release: 2024-07-29

Added the DOI link for the online published JPSP article: https://doi.org/10.1037/pspa0000396.

CRAN release: 2024-06-12

Fixed bugs: Now only BERT_download() connects to the Internet, while all the other functions run in an offline way.
Improved installation guidance for Python packages.

CRAN release: 2024-05-19

Added BERT_info().
Added add.tokens and add.method arguments 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.

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 argument (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