Named Entity Recognition STILL UNDER DEVELOPMENT

Named Entity Recognition STILL UNDER DEVELOPMENT

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

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) Many models are not created to be able to provide NER classifications - this setting stops them from returning incorrect results.

Value

A tibble with NER classifications.

See also

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

Examples

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