Classify parts of a Text

Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text.

Usage

hf_ez_token_classification(
  model_id = "dbmdz/bert-large-cased-finetuned-conll03-english",
  use_api = FALSE
)

Arguments

model_id

A model_id. Run hf_search_models(...) for model_ids. Defaults to 'dbmdz/bert-large-cased-finetuned-conll03-english'.

use_api

Whether to use the Inference API to run the model (TRUE) or download and run the model locally (FALSE). Defaults to FALSE

Value

A text2text generation object

See also

https://huggingface.co/docs/api-inference/detailed_parameters#token-classification-task

Examples

if (FALSE) { # \dontrun{
# Load the default named entity recognition model
ez <- hf_ez_token_classification()

# Run NER. Note how the full name is aggregated into one named entity.
ez$infer(string = "My name is Sarah Jessica Parker but you can call me Jessica", aggregation_strategy = 'simple')

# Run NER without aggregation. Note how the full name is separated into distinct named entities.
ez$infer(string = "My name is Sarah Jessica Parker but you can call me Jessica", aggregation_strategy = 'none')
} # }