Package: FMAT 2024.7

FMAT: The Fill-Mask Association Test

The Fill-Mask Association Test ('FMAT') <doi:10.1037/pspa0000396> is an integrative and probability-based method using Masked Language Models to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as propositions in natural language. Supported language models include 'BERT' <doi:10.48550/arXiv.1810.04805> and its variants available at 'Hugging Face' <https://huggingface.co/models?pipeline_tag=fill-mask>. Methodological references and installation guidance are provided at <https://psychbruce.github.io/FMAT/>.

Authors:Han-Wu-Shuang Bao [aut, cre]

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FMAT.pdf |FMAT.html✨
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Peer review:

Bug tracker:https://github.com/psychbruce/fmat/issues

On CRAN:

aiartificial-intelligencebertbert-modelbert-modelscontextualized-representationfill-in-the-blankfill-maskhuggingfacelanguage-modellanguage-modelslarge-language-modelsmasked-language-modelsnatural-language-processingnatural-language-understandingnlppretrained-modelstransformertransformers

10 exports 7 stars 1.77 score 41 dependencies 2 scripts 419 downloads

Last updated 2 months agofrom:9210be308c. Checks:OK: 7. Indexed: yes.

Exports:.BERT_downloadBERT_infoBERT_vocabFMAT_loadFMAT_queryFMAT_query_bindFMAT_runICC_modelsLPR_reliability

Dependencies:clicpp11crayondata.tabledplyrfansiforcatsgenericsglueGPArotationhereirrjsonlitelatticelifecyclelpSolvemagrittrMatrixmnormtnlmepillarpkgconfigplyrpngpsychpurrrR6rappdirsRcppRcppTOMLreticulaterlangrprojrootstringistringrtibbletidyrtidyselectutf8vctrswithr