**Please check the [latest news (change log)](https://psychbruce.github.io/FMAT/news/index.html) 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](https://psychbruce.github.io/FMAT/#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](https://github.com/psychbruce/FMAT).

# FMAT 0.0.1 (Jan 2023)

-   Designed basic functions.
