tensorflow_datasets/structured/web_nlg/web_nlg.py [61:84]:
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  VERSION = tfds.core.Version('0.1.0')

  def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        # This is the description that will appear on the datasets page.
        description=_DESCRIPTION,
        # tfds.features.FeatureConnectors
        features=tfds.features.FeaturesDict({
            'input_text': {
                'table':  # Each row will be one triple fact.
                    tfds.features.Sequence({
                        # we'll only have subject/predicate/object headers
                        'column_header': tf.string,
                        'row_number': tf.int16,
                        'content': tf.string,
                    }),
                # context will be the category
                'context':
                    tf.string,
            },
            'target_text': tf.string,
        }),
        supervised_keys=('input_text', 'target_text'),
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tensorflow_datasets/structured/wiki_bio.py [78:98]:
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  VERSION = tfds.core.Version('0.1.0')

  def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            'input_text': {
                'table':
                    tfds.features.Sequence({
                        'column_header': tf.string,
                        'row_number': tf.int16,
                        'content': tf.string,
                    }),
                # context will be the article's title
                'context':
                    tf.string,
            },
            'target_text': tf.string,
        }),
        supervised_keys=('input_text', 'target_text'),
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