Named Entity Recognition. (experimental)

Named Entity Recognition. (experimental)

textNER(
  x,
  model = "dslim/bert-base-NER",
  device = "cpu",
  tokenizer_parallelism = FALSE,
  logging_level = "error",
  return_incorrect_results = FALSE,
  set_seed = 202208L
)

Arguments

x

(string) A variable or a tibble/dataframe with at least one character variable.

model

(string) Specification of a pre-trained language model for token classification that have been fine-tuned on a NER task (e.g., see "dslim/bert-base-NER"). Use for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous).

device

(string) Device to use: 'cpu', 'gpu', or 'gpu:k' where k is a specific device number

tokenizer_parallelism

(boolean) If TRUE this will turn on tokenizer parallelism.

logging_level

(string) Set the logging level. Options (ordered from less logging to more logging): critical, error, warning, info, debug

return_incorrect_results

(boolean) Stop returning some incorrectly formatted/structured results. This setting does CANOT evaluate the actual results (whether or not they make sense, exist, etc.). All it does is to ensure the returned results are formatted correctly (e.g., does the question-answering dictionary contain the key "answer", is sentiments from textClassify containing the labels "positive" and "negative").

set_seed

(Integer) Set seed.

Value

A list with tibble(s) with NER classifications for each column.

See also

see textClassify, textGeneration, textNER, textSum, textQA, textTranslate

Examples

# \donttest{
# ner_example <- textNER("Arnes plays football with Daniel")
# ner_example
# }