class_docstr: BuilderConfig for TED talk data comparing high/low resource languages.
class_docstr_tok: ["BuilderConfig","for","TED","talk","data","comparing","high\/low","resource","languages","."]
docstr: BuilderConfig for TED talk data comparing high/low resource languages.
  
  The first language in `language_pair` should either be a 2-letter coded
  string or two such strings joined by an underscore (e.g., "az" or "az_tr").
  In cases where it contains two languages, the train data set will contain an
  (unlabelled) mix of the two languages and the validation and test sets
  will contain only the first language. This dataset will refer to the
  source language by the 5-letter string with the underscore. The second
  language in `language_pair` must be a 2-letter coded string.
  
  For example, to get pairings between Russian and English, specify
  `("ru", "en")` as `language_pair`. To get a mix of Belarusian and Russian in
  the training set and purely Belarusian in the validation and test sets,
  specify `("be_ru", "en")`.
  
  Args:
    language_pair: pair of languages that will be used for translation. The
      first will be used as source and second as target in supervised mode.
    **kwargs: keyword arguments forwarded to super.
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code: def __init__(self, *, language_pair=(None, None), **kwargs):
      """BuilderConfig for TED talk data comparing high/low resource languages.
  
      The first language in `language_pair` should either be a 2-letter coded
      string or two such strings joined by an underscore (e.g., "az" or "az_tr").
      In cases where it contains two languages, the train data set will contain an
      (unlabelled) mix of the two languages and the validation and test sets
      will contain only the first language. This dataset will refer to the
      source language by the 5-letter string with the underscore. The second
      language in `language_pair` must be a 2-letter coded string.
  
      For example, to get pairings between Russian and English, specify
      `("ru", "en")` as `language_pair`. To get a mix of Belarusian and Russian in
      the training set and purely Belarusian in the validation and test sets,
      specify `("be_ru", "en")`.
  
      Args:
        language_pair: pair of languages that will be used for translation. The
          first will be used as source and second as target in supervised mode.
        **kwargs: keyword arguments forwarded to super.
      """
      name = "%s_to_%s" % (
          language_pair[0].replace("_", ""),
          language_pair[1],
      )
  
      description = (
          "Translation dataset from %s to %s in plain text."
          % (
              language_pair[0],
              language_pair[1],
          )
      )
      super(TedHrlrConfig, self).__init__(
          name=name, description=description, **kwargs
      )
  
      # Validate language pair.
      assert language_pair in _VALID_LANGUAGE_PAIRS, (
          "Config language pair (%s, %s) not supported" % language_pair
      )
  
      self.language_pair = language_pair
  
file_path: datasets/tensorflow_datasets/datasets/ted_hrlr_translate/ted_hrlr_translate_dataset_builder.py
imports: import os
  from etils import epath
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 to create a new TedHrlrConfig object from inherited base classes: tfds.core with example: obj = TedHrlrConfig()