😷 The Fill-Mask Association Test (掩码填空联系测验).
The
Fill-Mask Association Test
(FMAT) is an integrative and probability-based method using
BERT Models
to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as
propositions
in natural language (
Bao, 2024,
JPSP
).
⚠️
Please update this package to version ≥ 2025.4 for faster and more robust functionality.
Bao, H. W. S., & Gries, P. (2024). Intersectional race–gender stereotypes in natural language.
British Journal of Social Psychology, 63
(4), 1771–1786.
https://doi.org/10.1111/bjso.12748
Bao, H. W. S., & Gries, P. (2025). Biases about Chinese people in English language use: Stereotypes, prejudice and discrimination.
China Quarterly
.
https://doi.org/10.1017/S0305741025100532
Wang, Z., Xia, H., Bao, H. W. S., Jing, Y., & Gu, R. (2025). Artificial intelligence is stereotypically linked more with socially dominant groups in natural language.
Advanced Science
.
https://doi.org/10.1002/advs.202508623
Install
Anaconda
(a recommended package manager that automatically installs Python, its IDEs like Spyder, and a large list of common Python packages).
Specify the Anaconda’s Python interpreter in RStudio.
RStudio → Tools → Global/Project Options
→ Python → Select →
Conda Environments
→ Choose
“…/Anaconda3/python.exe”
Install specific versions of Python packages “
transformers
”, “
torch
”, and “
huggingface-hub
”.
(RStudio Terminal / Anaconda Prompt / Windows Command)
For CPU users:
pip install transformers==4.40.2 torch==2.2.1 huggingface-hub==0.20.3
For GPU (CUDA) users:
pip install transformers==4.40.2 huggingface-hub==0.20.3
pip install torch==2.2.1 --index-url https://download.pytorch.org/whl/cu121
To use some models (e.g.,
microsoft/deberta-v3-base
), “You need to have sentencepiece installed to convert a slow tokenizer to a fast one”:
pip install sentencepiece
See
Guidance for GPU Acceleration
for installation guidance if you have an NVIDIA GPU device on your PC and want to use GPU to accelerate the pipeline.
According to the May 2024 releases, “transformers” ≥ 4.41 depends on “huggingface-hub” ≥ 0.23. The suggested versions of “transformers” (4.40.2) and “huggingface-hub” (0.20.3) ensure the console display of progress bars when downloading BERT models while keeping these packages as new as possible.
Proxy users may use the “global mode” (全局模式) to download models.
If you find the error
HTTPSConnectionPool(host='huggingface.co', port=443)
, please try to (1) reinstall
Anaconda
so that some unknown issues may be fixed, or (2) downgrade the “
urllib3
” package to version ≤ 1.25.11 (
pip install urllib3==1.25.11
) so that it will use HTTP proxies (rather than HTTPS proxies as in later versions) to connect to Hugging Face.
Step 1: Download BERT Models
Use
BERT_download()
to download
BERT models
. Model files are saved in your local cache folder “%USERPROFILE%/.cache/huggingface”. A full list of BERT models are available at
Hugging Face
.
Use
BERT_info()
and
BERT_vocab()
to obtain detailed information of BERT models.
Step 2: Design FMAT Queries
Design queries that conceptually represent the constructs you would measure (see
Bao, 2024,
JPSP
for how to design queries).
Use
FMAT_query()
and/or
FMAT_query_bind()
to prepare a
data.table
of queries.
Step 3: Run FMAT
Use
FMAT_run()
to get raw data (probability estimates) for further analysis.
Several steps of preprocessing have been included in the function for easier use (see
FMAT_run()
for details).
For BERT variants using
<mask>
rather than
[MASK]
as the mask token, the input query will be
automatically
modified so that users can always use
[MASK]
in query design.
For some BERT variants, special prefix characters such as
\u0120
and
\u2581
will be
automatically
added to match the whole words (rather than subwords) for
[MASK]
.
Notes
Improvements are ongoing, especially for adaptation to more diverse (less popular) BERT models.
If you find bugs or have problems using the functions, please report them at
GitHub Issues
or send me an email.
Guidance for GPU Acceleration
By default, the
FMAT
package uses CPU to enable the functionality for all users. But for advanced users who want to accelerate the pipeline with GPU, the
FMAT_run()
function now supports using a GPU device, about
3x faster
than CPU.
Test results (on the developer’s computer, depending on BERT model size):
CPU (Intel 13th-Gen i7-1355U): 500~1000 queries/min
GPU (NVIDIA GeForce RTX 2050): 1500~3000 queries/min
Checklist:
Ensure that you have an NVIDIA GPU device (e.g., GeForce RTX Series) and an NVIDIA GPU driver installed on your system.
Install PyTorch (Python
torch
package) with CUDA support.
Find guidance for installation command at
https://pytorch.org/get-started/locally/
.
CUDA is available only on Windows and Linux, but not on MacOS.
If you have installed a version of
torch
without CUDA support, please first uninstall it (command:
pip uninstall torch
) and then install the suggested one.
You may also install the corresponding version of CUDA Toolkit (e.g., for the
torch
version supporting CUDA 12.1, the same version of
CUDA Toolkit 12.1
may also be installed).
Example code for installing PyTorch with CUDA support:
(RStudio Terminal / Anaconda Prompt / Windows Command)
pip install torch==2.2.1 --index-url https://download.pytorch.org/whl/cu121
BERT Models
The reliability and validity of the following 12 BERT models in the FMAT have been established in our research, but future work is needed to examine the performance of other models.
(model name on Hugging Face - model file size)
bert-base-uncased
(420 MB)
bert-base-cased
(416 MB)
bert-large-uncased
(1283 MB)
bert-large-cased
(1277 MB)
distilbert-base-uncased
(256 MB)
distilbert-base-cased
(251 MB)
albert-base-v1
(45 MB)
albert-base-v2
(45 MB)
roberta-base
(476 MB)
distilroberta-base
(316 MB)
vinai/bertweet-base
(517 MB)
vinai/bertweet-large
(1356 MB)
For details about
BERT
, see:
What is Fill-Mask? [HuggingFace]
An Explorable BERT [HuggingFace]
BERT Model Documentation [HuggingFace]
Illustrated BERT
Visual Guide to BERT
library
(
FMAT
)
models
=
c
(
"bert-base-uncased"
,
"bert-base-cased"
,
"bert-large-uncased"
,
"bert-large-cased"
,
"distilbert-base-uncased"
,
"distilbert-base-cased"
,
"albert-base-v1"
,
"albert-base-v2"
,
"roberta-base"
,
"distilroberta-base"
,
"vinai/bertweet-base"
,
"vinai/bertweet-large"
BERT_download
(
models
)
ℹ Device Info:
R Packages:
FMAT 2024.5
reticulate 1.36.1
Python Packages:
transformers 4.40.2
torch 2.2.1+cu121
NVIDIA GPU CUDA Support:
CUDA Enabled: TRUE
CUDA Version: 12.1
GPU (Device): NVIDIA GeForce RTX 2050
── Downloading model "bert-base-uncased" ──────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 570/570 [00:00<00:00, 114kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 48.0/48.0 [00:00<00:00, 23.9kB/s]
vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 1.50MB/s]
tokenizer.json: 100%|██████████| 466k/466k [00:00<00:00, 1.98MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 440M/440M [00:36<00:00, 12.1MB/s]
✔ Successfully downloaded model "bert-base-uncased"
── Downloading model "bert-base-cased" ────────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 570/570 [00:00<00:00, 63.3kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 49.0/49.0 [00:00<00:00, 8.66kB/s]
vocab.txt: 100%|██████████| 213k/213k [00:00<00:00, 1.39MB/s]
tokenizer.json: 100%|██████████| 436k/436k [00:00<00:00, 10.1MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 436M/436M [00:37<00:00, 11.6MB/s]
✔ Successfully downloaded model "bert-base-cased"
── Downloading model "bert-large-uncased" ─────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 571/571 [00:00<00:00, 268kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 48.0/48.0 [00:00<00:00, 12.0kB/s]
vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 1.50MB/s]
tokenizer.json: 100%|██████████| 466k/466k [00:00<00:00, 1.99MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 1.34G/1.34G [01:36<00:00, 14.0MB/s]
✔ Successfully downloaded model "bert-large-uncased"
── Downloading model "bert-large-cased" ───────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 762/762 [00:00<00:00, 125kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 49.0/49.0 [00:00<00:00, 12.3kB/s]
vocab.txt: 100%|██████████| 213k/213k [00:00<00:00, 1.41MB/s]
tokenizer.json: 100%|██████████| 436k/436k [00:00<00:00, 5.39MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 1.34G/1.34G [01:35<00:00, 14.0MB/s]
✔ Successfully downloaded model "bert-large-cased"
── Downloading model "distilbert-base-uncased" ────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 483/483 [00:00<00:00, 161kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 48.0/48.0 [00:00<00:00, 9.46kB/s]
vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 16.5MB/s]
tokenizer.json: 100%|██████████| 466k/466k [00:00<00:00, 14.8MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 268M/268M [00:19<00:00, 13.5MB/s]
✔ Successfully downloaded model "distilbert-base-uncased"
── Downloading model "distilbert-base-cased" ──────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 465/465 [00:00<00:00, 233kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 49.0/49.0 [00:00<00:00, 9.80kB/s]
vocab.txt: 100%|██████████| 213k/213k [00:00<00:00, 1.39MB/s]
tokenizer.json: 100%|██████████| 436k/436k [00:00<00:00, 8.70MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 263M/263M [00:24<00:00, 10.9MB/s]
✔ Successfully downloaded model "distilbert-base-cased"
── Downloading model "albert-base-v1" ─────────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 684/684 [00:00<00:00, 137kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 25.0/25.0 [00:00<00:00, 3.57kB/s]
spiece.model: 100%|██████████| 760k/760k [00:00<00:00, 4.93MB/s]
tokenizer.json: 100%|██████████| 1.31M/1.31M [00:00<00:00, 13.4MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 47.4M/47.4M [00:03<00:00, 13.4MB/s]
✔ Successfully downloaded model "albert-base-v1"
── Downloading model "albert-base-v2" ─────────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 684/684 [00:00<00:00, 137kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 25.0/25.0 [00:00<00:00, 4.17kB/s]
spiece.model: 100%|██████████| 760k/760k [00:00<00:00, 5.10MB/s]
tokenizer.json: 100%|██████████| 1.31M/1.31M [00:00<00:00, 6.93MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 47.4M/47.4M [00:03<00:00, 13.8MB/s]
✔ Successfully downloaded model "albert-base-v2"
── Downloading model "roberta-base" ───────────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 481/481 [00:00<00:00, 80.3kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 25.0/25.0 [00:00<00:00, 6.25kB/s]
vocab.json: 100%|██████████| 899k/899k [00:00<00:00, 2.72MB/s]
merges.txt: 100%|██████████| 456k/456k [00:00<00:00, 8.22MB/s]
tokenizer.json: 100%|██████████| 1.36M/1.36M [00:00<00:00, 8.56MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 499M/499M [00:38<00:00, 12.9MB/s]
✔ Successfully downloaded model "roberta-base"
── Downloading model "distilroberta-base" ─────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 480/480 [00:00<00:00, 96.4kB/s]
→ (2) Downloading tokenizer...
tokenizer_config.json: 100%|██████████| 25.0/25.0 [00:00<00:00, 12.0kB/s]
vocab.json: 100%|██████████| 899k/899k [00:00<00:00, 6.59MB/s]
merges.txt: 100%|██████████| 456k/456k [00:00<00:00, 9.46MB/s]
tokenizer.json: 100%|██████████| 1.36M/1.36M [00:00<00:00, 11.5MB/s]
→ (3) Downloading model...
model.safetensors: 100%|██████████| 331M/331M [00:25<00:00, 13.0MB/s]
✔ Successfully downloaded model "distilroberta-base"
── Downloading model "vinai/bertweet-base" ────────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 558/558 [00:00<00:00, 187kB/s]
→ (2) Downloading tokenizer...
vocab.txt: 100%|██████████| 843k/843k [00:00<00:00, 7.44MB/s]
bpe.codes: 100%|██████████| 1.08M/1.08M [00:00<00:00, 7.01MB/s]
tokenizer.json: 100%|██████████| 2.91M/2.91M [00:00<00:00, 9.10MB/s]
→ (3) Downloading model...
pytorch_model.bin: 100%|██████████| 543M/543M [00:48<00:00, 11.1MB/s]
✔ Successfully downloaded model "vinai/bertweet-base"
── Downloading model "vinai/bertweet-large" ───────────────────────────────────────
→ (1) Downloading configuration...
config.json: 100%|██████████| 614/614 [00:00<00:00, 120kB/s]
→ (2) Downloading tokenizer...
vocab.json: 100%|██████████| 899k/899k [00:00<00:00, 5.90MB/s]
merges.txt: 100%|██████████| 456k/456k [00:00<00:00, 7.30MB/s]
tokenizer.json: 100%|██████████| 1.36M/1.36M [00:00<00:00, 8.31MB/s]
→ (3) Downloading model...
pytorch_model.bin: 100%|██████████| 1.42G/1.42G [02:29<00:00, 9.53MB/s]
✔ Successfully downloaded model "vinai/bertweet-large"
── Downloaded models: ──
albert-base-v1 45 MB
albert-base-v2 45 MB
bert-base-cased 416 MB
bert-base-uncased 420 MB
bert-large-cased 1277 MB
bert-large-uncased 1283 MB
distilbert-base-cased 251 MB
distilbert-base-uncased 256 MB
distilroberta-base 316 MB
roberta-base 476 MB
vinai/bertweet-base 517 MB
vinai/bertweet-large 1356 MB
✔ Downloaded models saved at C:/Users/Bruce/.cache/huggingface/hub (6.52 GB)
BERT_info
(
models
)
model size vocab dims mask
<fctr> <char> <int> <int> <char>
1: bert-base-uncased 420MB 30522 768 [MASK]
2: bert-base-cased 416MB 28996 768 [MASK]
3: bert-large-uncased 1283MB 30522 1024 [MASK]
4: bert-large-cased 1277MB 28996 1024 [MASK]
5: distilbert-base-uncased 256MB 30522 768 [MASK]
6: distilbert-base-cased 251MB 28996 768 [MASK]
7: albert-base-v1 45MB 30000 128 [MASK]
8: albert-base-v2 45MB 30000 128 [MASK]
9: roberta-base 476MB 50265 768 <mask>
10: distilroberta-base 316MB 50265 768 <mask>
11: vinai/bertweet-base 517MB 64001 768 <mask>
12: vinai/bertweet-large 1356MB 50265 1024 <mask>
(Tested 2024-05-16 on the developer’s computer: HP Probook 450 G10 Notebook PC)
Related Packages
While the FMAT is an innovative method for the
computational intelligent
analysis of psychology and society, you may also seek for an integrative toolbox for other text-analytic methods. Another R package I developed—
PsychWordVec
—is useful and user-friendly for word embedding analysis (e.g., the Word Embedding Association Test, WEAT). Please refer to its documentation and feel free to use it.
Links
View on CRAN
Browse source code
Report a bug