Translation STILL UNDER DEVELOPMENT

Translation STILL UNDER DEVELOPMENT

textTranslate(
  x,
  source_lang = "",
  target_lang = "",
  model = "xlm-roberta-base",
  device = "cpu",
  tokenizer_parallelism = FALSE,
  logging_level = "warning",
  return_incorrect_results = FALSE,
  return_tensors = FALSE,
  return_text = TRUE,
  clean_up_tokenization_spaces = FALSE
)

Arguments

x

(string) The text to be translated.

source_lang

(string) The input language. Might be needed for multilingual models (it will not have any effect for single pair translation models). using ISO 639-1 Code, such as: "en", "zh", "es", "fr", "de", "it", "sv", "da", "nn".

target_lang

(string) The desired language output. Might be required for multilingual models (will not have any effect for single pair translation models).

model

(string) Specify a pre-trained language model that have been fine-tuned on a translation task.

device

(string) Name of 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 translation, so this setting stops them from returning incorrect results.

return_tensors

(boolean) Whether or not to include the predictions' tensors as token indices in the outputs.

return_text

(boolean) Whether or not to also output the decoded texts.

clean_up_tokenization_spaces

(boolean) Whether or not to clean the output from potential extra spaces.

Value

A tibble with.

See also

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

Examples

# \donttest{
# translation_example <- text::textTranslate(
#  Language_based_assessment_data_8[1,1:2],
#  source_lang = "en",
#  target_lang = "fr",
#  model = "t5-base")
# }