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
)
(string) A variable or a tibble/dataframe with at least one character variable.
(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).
(string) Device to use: 'cpu', 'gpu', or 'gpu:k' where k is a specific device number
(boolean) If TRUE this will turn on tokenizer parallelism.
(string) Set the logging level. Options (ordered from less logging to more logging): critical, error, warning, info, debug
(boolean) Many models are not created to be able to provide NER classifications - this setting stops them from returning incorrect results.
A tibble with NER classifications.
see textClassify
, textGeneration
, textNER
,
textSum
, textQA
, textTranslate
# \donttest{
# ner_example <- textNER("Arnes plays football with Daniel")
# ner_example
# }