Create wit-dataset.py
Browse files- wit-dataset.py +212 -0
wit-dataset.py
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """\
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Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
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WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
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Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
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"""
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_CITATION = """
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@article{srinivasan2021wit,
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title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
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author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
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journal={arXiv preprint arXiv:2103.01913},
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year={2021}
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}
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"""
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_URL = "https://github.com/google-research-datasets/wit"
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_DATA_URL = "https://huggingface.co/datasets/keshan/wit-dataset/resolve/7e65a989e0d2e48c33b86309c37e9eadfc063b9f/data/{language}.tar.gz"
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_LANGUAGES = [
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'ms',
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'eu',
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'si',
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'Prakrit',
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'ko',
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'nv',
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'id',
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'tg',
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'mn',
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'fa',
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'bg',
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'ia',
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'ca',
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'jv',
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'vi',
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'ja',
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'bs',
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'te',
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'war',
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'hy',
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'sv',
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'az',
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'lah',
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'ht',
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'sl',
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'pt',
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'an',
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'br',
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'nn',
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'ceb',
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'ce',
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'qu',
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'gl',
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'fy',
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'vec',
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'zh',
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'iw',
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'vo',
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'xmf',
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'nds',
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'bar',
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'ba',
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'sr-Latn',
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'hsb',
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'yue',
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'arz',
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'es',
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'bn',
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'de',
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'mk',
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'pa',
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'zh-TW',
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'io',
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'lb',
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'azb',
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'ga',
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'cs',
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'fi',
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'cv',
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'sr',
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'lv',
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'my',
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'mg',
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'hu',
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'it',
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'kk',
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'be',
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'sq',
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'ru',
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'ar',
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'cy',
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'hr',
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'be-tarask',
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'is',
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'tt',
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'mr',
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'ro',
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'en',
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'fil',
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'uz',
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'af',
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'et',
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'fr',
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'no',
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'ckb',
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'nan',
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'sw',
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'la',
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'lmo',
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'th',
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'ta',
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'ast',
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'eo',
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'tr',
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'uk',
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'ur',
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'ne',
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'kn',
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'da',
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'nl',
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'ka',
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'pl',
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'el',
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'sco',
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'hi',
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'sk',
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'oc',
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'lt',
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'ml'
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]
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class WITConfig(datasets.BuilderConfig):
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"""BuilderConfig for WIT."""
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def __init__(self, *args, languages, **kwargs):
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"""BuilderConfig for WIT.
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Args:
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languages (:obj:`List[str]`): list of languages to load
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**kwargs: keyword arguments forwarded to super.
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"""
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super().__init__(
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*args,
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name="+".join(languages),
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**kwargs,
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)
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self.languages = languages
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class WIT(datasets.GeneratorBasedBuilder):
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"""WIT, WIT to be used as a pretraining dataset for multimodal machine learning models."""
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BUILDER_CONFIGS = [WITConfig(languages=[lang]) for lang in _LANGUAGES]
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BUILDER_CONFIG_CLASS = WITConfig
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"language": datasets.Value("string"),
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"page_url": datasets.Value("string"),
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"image_url": datasets.Value("string"),
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"page_title": datasets.Value("string"),
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"section_title": datasets.Value("string"),
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"hierarchical_section_title": datasets.Value("string"),
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"caption_reference_description": datasets.Value("string"),
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"caption_attribution_description": datasets.Value("string"),
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"caption_alt_text_description": datasets.Value("string"),
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"mime_type": datasets.Value("string"),
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"original_height": datasets.Value("int8"),
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"original_width": datasets.Value("int8"),
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"is_main_image": datasets.Value("bool"),
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"attribution_passes_lang_id": datasets.Value("string"),
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"page_changed_recently": datasets.Value("string"),
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"context_page_description": datasets.Value("string"),
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"context_section_description": datasets.Value("string"),
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}
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),
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supervised_keys=None,
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homepage=_URL,
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citation=_CITATION,
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)
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+
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def _split_generators(self, dl_manager):
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abs_path_to_data = dl_manager.download_and_extract(
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_DATA_URL.format(language=self.config.name)
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)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": abs_path_to_data,
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},
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),
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]
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+
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+
def _generate_examples(self, filepath):
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data_fields = list(self._info().features.keys())
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path_idx = data_fields.index("image_url")
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# ToDO: Remove after debugging..
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print(path_to_data)
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with open(path_to_data, encoding="utf-8") as f:
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lines = f.readlines()
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headline = line[0]
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+
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column_names = headline.strip().split('\t')
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assert (
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column_names == data_fields
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), f"The file should have {data_fields} as column names, but has {column_names}"
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+
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206 |
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for id_, line in enumerate(lines[1:]):
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field_values = line.strip().split("\t")
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# if data is incomplete, fill with empty values
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if len(field_values) < len(data_fields):
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field_values += (len(data_fields) - len(field_values)) * ["''"]
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+
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yield id_, {key: value for key, value in zip(data_fields, field_values)}
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