,

Skip to contents

Preloads a masked language model

Source: R/tr_masked.R
masked_preload.Rd

Preloads a masked language model to speed up next runs.

Usage

masked_preload(
  model = getOption("pangoling.masked.default"),
  add_special_tokens = NULL,
  config_model = NULL,
  config_tokenizer = NULL
)

Arguments

model

Name of a pre-trained model or folder.

add_special_tokens

Whether to include special tokens. It has the same default as the AutoTokenizer method in Python.

config_model

List with other arguments that control how the model from Hugging Face is accessed.

config_tokenizer

List with other arguments that control how the tokenizer from Hugging Face is accessed.

Value

Nothing.

Details

A masked language model (also called BERT-like, or encoder model) is a type of large language model that can be used to predict the content of a mask in a sentence.

If not specified, the masked model that will be used is the one set in specified in the global option pangoling.masked.default, this can be accessed via getOption("pangoling.masked.default") (by default "bert-base-uncased"). To change the default option use options(pangoling.masked.default = "newmaskedmodel").

A list of possible masked can be found in Hugging Face website.

Using the config_model and config_tokenizer arguments, it's possible to control how the model and tokenizer from Hugging Face is accessed, see the python method from_pretrained for details. In case of errors check the status of https://status.huggingface.co/

See also

Other masked model functions: masked_config(), masked_lp(), masked_tokens_tbl()

Examples

if (FALSE) { # interactive()
causal_preload(model = "bert-base-uncased")
}