Tokenize according to different huggingface transformers

Tokenize according to different huggingface transformers

textTokenize(
  texts,
  model = "bert-base-uncased",
  max_token_to_sentence = 4,
  device = "cpu",
  tokenizer_parallelism = FALSE,
  model_max_length = NULL,
  logging_level = "error"
)

Arguments

texts

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

model

Character string specifying pre-trained language model (default 'bert-base-uncased'). For full list of options see pretrained models at HuggingFace. For example use "bert-base-multilingual-cased", "openai-gpt", "gpt2", "ctrl", "transfo-xl-wt103", "xlnet-base-cased", "xlm-mlm-enfr-1024", "distilbert-base-cased", "roberta-base", or "xlm-roberta-base".

max_token_to_sentence

(numeric) Maximum number of tokens in a string to handle before switching to embedding text sentence by sentence.

device

Name of device to use: 'cpu', 'gpu', 'gpu:k' or 'mps'/'mps:k' for MacOS, where k is a specific device number.

tokenizer_parallelism

If TRUE this will turn on tokenizer parallelism. Default FALSE.

model_max_length

The maximum length (in number of tokens) for the inputs to the transformer model (default the value stored for the associated model).

logging_level

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

Value

Returns tokens according to specified huggingface transformer.

See also

see textEmbed

Examples

# \donttest{
# tokens <- textTokenize("hello are you?")
# }