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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\\nWikipedia-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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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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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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print(filepath) |
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with open(filepath, encoding="utf-8") as f: |
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lines = f.readlines() |
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headline = line[0] |
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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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for id_, line in enumerate(lines[1:]): |
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field_values = line.strip().split("\t") |
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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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yield id_, {key: value for key, value in zip(data_fields, field_values)} |
