Skip to contents

Run the fill-mask pipeline on multiple models (CPU / GPU).

Source: R/FMAT.R
FMAT_run.Rd

Run the fill-mask pipeline on multiple models with CPU or GPU (faster but requires an NVIDIA GPU device).

Usage

FMAT_run(
  models,
  data,
  gpu,
  add.tokens = FALSE,
  add.verbose = FALSE,
  weight.decay = 1,
  pattern.special = special_case(),
  file = NULL,
  progress = TRUE,
  warning = TRUE,
  na.out = TRUE
)

Arguments

models

A character vector of model names at HuggingFace.

data

A data.table returned from FMAT_query() or FMAT_query_bind().

gpu

Use GPU (3x faster than CPU) to run the fill-mask pipeline? Defaults to missing value that will automatically use available GPU (if not available, then use CPU). An NVIDIA GPU device (e.g., GeForce RTX Series) is required to use GPU. See Guidance for GPU Acceleration.

Options passing on to the device parameter in Python:

  • FALSE: CPU (device = -1).

  • TRUE: GPU (device = 0).

  • Others: passing on to transformers.pipeline(device=...) which defines the device (e.g., "cpu", "cuda:0", or a GPU device id like 1) on which the pipeline will be allocated.

add.tokens

Add new tokens (for out-of-vocabulary words or phrases) to model vocabulary? Defaults to FALSE.

  • Default method of producing the new token embeddings is computing the (equally weighted) average subword token embeddings. To change the weights of different subwords, specify weight.decay.

  • It just adds tokens temporarily without changing the raw model file.

add.verbose

Print subwords of each new token? Defaults to FALSE.

weight.decay

Decay factor of relative importance of multiple subwords. Defaults to 1 (see weight_decay() for computational details). A smaller decay value would give greater weight to the former subwords than to the latter subwords. The i-th subword has raw weight = decay ^ i.

  • decay = 1: all subwords are equally important (default)

  • 0 < decay < 1: first subwords are more important

  • decay > 1: last subwords are more important

For example, decay = 0.5 would give 0.5 and 0.25 (with normalized weights 0.667 and 0.333) to two subwords (e.g., "individualism" = 0.667 "individual" + 0.333 "##ism").

pattern.special

See special_case() for details.

file

File name of .RData to save the returned data.

progress

Show a progress bar? Defaults to TRUE.

warning

Alert warning of out-of-vocabulary word(s)? Defaults to TRUE.

na.out

Replace probabilities of out-of-vocabulary word(s) with NA? Defaults to TRUE.

Value

A data.table (class fmat) appending data with these new variables:

  • model: model name.

  • output: complete sentence output with unmasked token.

  • token: actual token to be filled in the blank mask (a note "out-of-vocabulary" will be added if the original word is not found in the model vocabulary).

  • prob: (raw) conditional probability of the unmasked token given the provided context, estimated by the masked language model.

    • Raw probabilities should NOT be directly used or interpreted. Please use summary.fmat() to contrast between a pair of probabilities.

Details

The function automatically adjusts for the compatibility of tokens used in certain models: (1) for uncased models (e.g., ALBERT), it turns tokens to lowercase; (2) for models that use <mask> rather than [MASK], it automatically uses the corrected mask token; (3) for models that require a prefix to estimate whole words than subwords (e.g., ALBERT, RoBERTa), it adds a white space before each mask option word. See special_case() for details.

These changes only affect the token variable in the returned data, but will not affect the M_word variable. Thus, users may analyze data based on the unchanged M_word rather than the token.

Note also that there may be extremely trivial differences (after 5~6 significant digits) in the raw probability estimates between using CPU and GPU, but these differences would have little impact on main results.

See also

set_cache_folder()

BERT_download()

BERT_vocab()

FMAT_query()

FMAT_query_bind()

summary.fmat()

special_case()

weight_decay()

Examples

## Running the examples requires the models downloaded

if (FALSE) { # \dontrun{
models = c("bert-base-uncased", "bert-base-cased")

query1 = FMAT_query(
  c("[MASK] is {TARGET}.", "[MASK] works as {TARGET}."),
  MASK = .(Male="He", Female="She"),
  TARGET = .(Occupation=c("a doctor", "a nurse", "an artist"))
)
data1 = FMAT_run(models, query1)
summary(data1, target.pair=FALSE)

query2 = FMAT_query(
  "The [MASK] {ATTRIB}.",
  MASK = .(Male=c("man", "boy"),
           Female=c("woman", "girl")),
  ATTRIB = .(Masc=c("is masculine", "has a masculine personality"),
             Femi=c("is feminine", "has a feminine personality"))
)
data2 = FMAT_run(models, query2)
summary(data2, mask.pair=FALSE)
summary(data2)
} # }