| import json |
|
|
| import datasets |
| from datasets import Value, Sequence, Features |
|
|
|
|
| _CITATION = """\\n@article{srinivasan2021wit, |
| title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning}, |
| author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc}, |
| journal={arXiv preprint arXiv:2103.01913}, |
| year={2021} |
| } |
| """ |
|
|
| _DESCRIPTION = """\\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. 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. Its |
| size enables WIT to be used as a pretraining dataset for multimodal machine learning models. |
| """ |
|
|
| _HOMEPAGE = "https://github.com/google-research-datasets/wit" |
|
|
| _URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wit/" |
| _URLS = { |
| |
| 'train': [_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(10)] |
| } |
|
|
| class Wit(datasets.GeneratorBasedBuilder): |
| """WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning""" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=Features({ |
| 'b64_bytes': Value('string'), |
| 'embedding': Sequence(Value('float64')), |
| 'image_url': Value('string'), |
| 'metadata_url': Value('string'), |
| 'wit_features': Sequence({ |
| "language": Value('string'), |
| "page_url": Value('string'), |
| "image_url": Value('string'), |
| "attribution_passes_lang_id": Value("string"), |
| "caption_alt_text_description": Value('string'), |
| "caption_attribution_description": Value('string'), |
| "caption_reference_description": Value('string'), |
| "caption_title_and_reference_description": Value('string'), |
| "context_page_description": Value('string'), |
| "context_section_description": Value('string'), |
| "hierarchical_section_title": Value('string'), |
| "is_main_image": Value('string'), |
| "mime_type": Value('string'), |
| "original_height": Value('string'), |
| "original_width": Value('string'), |
| "page_changed_recently": Value('string'), |
| "page_title": Value('string'), |
| "section_title": Value('string'), |
| }) |
| }), |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| urls_to_download = _URLS |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}), |
| ] |
|
|
| def _generate_examples(self, filepaths): |
| """Yields examples.""" |
| wit_feature_names = self.info.features['wit_features'].feature.keys() |
| for filepath in filepaths: |
| with open(filepath, "rb") as f: |
| for i, line in enumerate(f): |
| line = line.strip() |
| row_data = json.loads(line, encoding='utf-8') |
| for feature in row_data['wit_features']: |
| for fname in wit_feature_names: |
| if fname not in feature: |
| feature[fname] = None |
| yield str(i), row_data |
|
|
