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""""WIT (Wikipedia-based Image Text Dataset) dataset (Wikimedia version).""" |
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import base64 |
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import gzip |
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import json |
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import datasets |
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from .corrected_examples import CORRECTED_EXAMPLES |
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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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_DESCRIPTION = """\ |
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Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. |
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It contains more than six million images from Wikipedia articles in 100+ languages, which correspond to almost all captioned images in Google's version of the WIT dataset. |
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Images are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images. |
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This version of the WIT dataset was released by Wikimedia Research team. |
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""" |
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_LICENSE = "CC BY-SA 4.0 international license" |
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_HOMEPAGE = "https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/" |
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_BASE_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wit/" |
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_URLS = [_BASE_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(400)] |
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class Wit(datasets.GeneratorBasedBuilder): |
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"""Builder for WIT dataset (Wikimedia version).""" |
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DEFAULT_WRITER_BATCH_SIZE = 1000 |
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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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"image": datasets.Image(), |
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"image_url": datasets.Value("string"), |
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"embedding": datasets.Sequence(datasets.Value("float64"), length=2048), |
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"metadata_url": datasets.Value("string"), |
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"original_height": datasets.Value("int32"), |
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"original_width": datasets.Value("int32"), |
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"mime_type": datasets.Value("string"), |
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"caption_attribution_description": datasets.Value("string"), |
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"wit_features": datasets.Sequence( |
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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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"attribution_passes_lang_id": datasets.Value("bool"), |
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"caption_alt_text_description": datasets.Value("string"), |
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"caption_reference_description": datasets.Value("string"), |
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"caption_title_and_reference_description": 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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"hierarchical_section_title": datasets.Value("string"), |
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"is_main_image": datasets.Value("bool"), |
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"page_changed_recently": datasets.Value("bool"), |
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"page_title": datasets.Value("string"), |
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"section_title": datasets.Value("string"), |
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} |
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), |
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} |
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), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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downloaded_files = dl_manager.download(_URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_files": downloaded_files}), |
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] |
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def _generate_examples(self, data_files): |
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"""Yields examples.""" |
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wit_feature_names = self.info.features["wit_features"].feature.keys() |
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idx = 0 |
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for data_file_idx, data_file in enumerate(data_files): |
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with gzip.open(open(data_file, "rb"), mode="rt", encoding="utf-8") as f: |
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for row_idx, row in enumerate(f): |
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example = json.loads(row) |
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ex_wit_features_non_empty = [] |
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for feature in example["wit_features"]: |
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for wit_feature_name in wit_feature_names: |
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if wit_feature_name not in feature: |
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feature[wit_feature_name] = None |
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extra_wit_feature_keys = [k for k in feature.keys() if k not in wit_feature_names] |
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for extra_wit_feature_key in extra_wit_feature_keys: |
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extra_wit_feature_value = feature.pop(extra_wit_feature_key) |
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if isinstance(extra_wit_feature_value, list): |
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extra_wit_feature_value = extra_wit_feature_value[0] |
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example[extra_wit_feature_key] = extra_wit_feature_value |
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if any(v is not None for v in feature.values()): |
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ex_wit_features_non_empty.append(feature) |
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example["wit_features"] = ex_wit_features_non_empty |
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missing_keys = [k for k in self.info.features.keys() if k not in example] |
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for missing_key in missing_keys: |
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example[missing_key] = None |
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b64_image_bytes = example.pop("b64_bytes") |
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example["image"] = ( |
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{"path": None, "bytes": base64.b64decode(b64_image_bytes)} |
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if b64_image_bytes is not None |
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else None |
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) |
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corrections = CORRECTED_EXAMPLES.get((data_file_idx, row_idx)) |
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if corrections is not None: |
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assert example["metadata_url"] == corrections["metadata_url"] |
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example.update(corrections) |
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yield idx, example |
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idx += 1 |
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