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""" |
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SEA Crowd Data Loader for Bloom Captioning. |
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""" |
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from typing import Dict, List, Tuple |
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import datasets |
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from datasets.download.download_manager import DownloadManager |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks |
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_CITATION = r""" |
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@inproceedings{leong-etal-2022-bloom, |
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title = "Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks", |
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author = "Leong, Colin and |
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Nemecek, Joshua and |
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Mansdorfer, Jacob and |
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Filighera, Anna and |
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Owodunni, Abraham and |
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Whitenack, Daniel", |
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editor = "Goldberg, Yoav and |
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Kozareva, Zornitsa and |
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Zhang, Yue", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, United Arab Emirates", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.emnlp-main.590", |
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doi = "10.18653/v1/2022.emnlp-main.590", |
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pages = "8608--8621", |
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} |
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""" |
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logger = datasets.logging.get_logger(__name__) |
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_LANG_CONFIG = { |
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"abc": "Ambala Ayta", |
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"ahk": "Akha", |
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"bfn": "Bunak", |
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"bjn": "Banjar", |
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"bkx": "Baikeno", |
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"brb": "Brao", |
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"brv": "Western Bru", |
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"bya": "Batak", |
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"bzi": "Bisu", |
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"ceb": "Cebuano", |
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"cgc": "Kagayanen", |
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"cmo": "Central Mnong", |
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"ddg": "Fataluku", |
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"dmg": "Upper Kinabatangan", |
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"dnw": "Western Dani", |
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"dtp": "Kadazan Dusun", |
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"dtr": "Lotud", |
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"enc": "En", |
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"fil": "Filipino", |
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"gal": "Galolen", |
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"hil": "Hiligaynon", |
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"hre": "Hre", |
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"hro": "Haroi", |
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"idt": "Idaté", |
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"ilo": "Ilocano", |
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"ind": "Indonesian", |
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"jra": "Jarai", |
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"kak": "Kalanguya", |
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"khb": "Lü", |
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"khm": "Khmer", |
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"kqr": "Kimaragang", |
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"krr": "Krung", |
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"ksw": "S’gaw Karen", |
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"lhu": "Lahu", |
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"llg": "Lole", |
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"lsi": "Lacid", |
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"lwl": "Eastern Lawa", |
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"mdr": "Mandar", |
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"mgm": "Mambae", |
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"mhx": "Lhao Vo", |
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"mkz": "Makasae", |
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"mnw": "Mon", |
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"mqj": "Mamasa", |
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"mry": "Mandaya", |
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"msb": "Masbatenyo", |
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"mya": "Burmese", |
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"nod": "Northern Thai", |
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"nst": "Tangshang Naga", |
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"nxa": "Nauete", |
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"nxl": "South Nuaulu", |
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"pag": "Pangasinan", |
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"pce": "Ruching Palaung", |
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"pdu": "Kayan", |
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"pea": "Peranakan Indonesian", |
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"pmf": "Pamona", |
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"sea": "Semai", |
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"sgd": "Surigaonon", |
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"shn": "Shan", |
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"sml": "Central Sama", |
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"snl": "Sangil", |
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"tdt": "Tetun Dili", |
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"tet": "Tetun", |
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"tha": "Thai", |
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"tkd": "Tukudede", |
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"tnt": "Tontemboan", |
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"tom": "Tombulu", |
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"tpu": "Tampuan", |
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"vie": "Vietnamese", |
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"war": "Waray-Waray", |
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"wms": "Wambon", |
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"wnk": "Wanukaka", |
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"xmm": "Manado Malay", |
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"yet": "Yetfa", |
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"zlm": "Malay", |
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} |
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_LOCAL = False |
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_LANGUAGES = list(_LANG_CONFIG.keys()) |
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_DATASETNAME = "bloom_captioning" |
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_DESCRIPTION = r""" |
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This is a Bloom Library dataset developed for the image captioning task. |
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It covers 74 languages indigenous to SEA overall, amounting to total data of 21K. |
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This dataset belongs to a CC license, where its datapoints has specific license attached to it. |
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Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/sil-ai/bloom-captioning and use huggingface-cli login for authentication. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/sil-ai/bloom-captioning" |
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_LICENSE = Licenses.CC.value |
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_URL = "https://huggingface.co/datasets/sil-ai/bloom-captioning" |
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_HF_REMOTE_REF = "/".join(_URL.split("/")[-2:]) |
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_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING] |
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_SOURCE_VERSION = "0.1.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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CONFIG_SUFFIXES_FOR_TASK = [TASK_TO_SCHEMA.get(task).lower() for task in _SUPPORTED_TASKS] |
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def construct_configs_on_langs(languages: list = None) -> List[SEACrowdConfig]: |
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""" |
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The function `construct_configs` constructs a list of SEACrowdConfig objects based on the provided |
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languages or a default language, and returns the list. |
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input: |
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languages (list, default None): The `languages` parameter is a list that specifies the languages for which the |
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configurations need to be constructed. If no languages are provided (value=None), the first value in language config |
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will be used. |
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output: |
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a list of `SEACrowdConfig` objects based on instantiated init variables |
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""" |
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config_list = [] |
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TASKS_AND_CONFIG_SUFFIX_PAIRS = list(zip(_SUPPORTED_TASKS, CONFIG_SUFFIXES_FOR_TASK)) |
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version, config_name_prefix = _SOURCE_VERSION, "source" |
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config_list += [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}", |
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version=datasets.Version(version), |
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description=f"{_DATASETNAME} {config_name_prefix} schema for language code {_LANG}", |
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schema=f"{config_name_prefix}", |
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subset_id=_LANG, |
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) |
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for _LANG in languages |
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] |
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version, config_name_prefix = _SEACROWD_VERSION, "seacrowd" |
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for task_obj, config_name_suffix in TASKS_AND_CONFIG_SUFFIX_PAIRS: |
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config_list += [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}_{config_name_suffix}", |
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version=datasets.Version(version), |
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description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name} and language code {_LANG}", |
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schema=f"{config_name_prefix}_{config_name_suffix}", |
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subset_id=_LANG, |
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) |
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for _LANG in languages |
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] |
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return config_list |
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class BloomCaptioningDataset(datasets.GeneratorBasedBuilder): |
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"""Bloom Captioning dataset, subsetted from https://huggingface.co/datasets/sil-ai/bloom-captioning""" |
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BUILDER_CONFIGS = construct_configs_on_langs(_LANGUAGES) |
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def _info(self) -> datasets.DatasetInfo: |
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_config_schema_name = self.config.schema |
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logger.info(f"Received schema name: {self.config.schema}") |
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if _config_schema_name == "source": |
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features = datasets.Features( |
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{ |
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"image_id": datasets.Value("string"), |
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"image_url": datasets.Value("string"), |
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"caption": datasets.Value("string"), |
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"story_id": datasets.Value("string"), |
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"album_id": datasets.Value("string"), |
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"license": datasets.Value("string"), |
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"original_bloom_language_tag": datasets.Value("string"), |
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"index_in_story": datasets.Value("uint16"), |
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} |
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) |
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elif _config_schema_name == "seacrowd_imtext": |
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features = schemas.image_text_features() |
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else: |
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raise ValueError(f"Received unexpected config schema of {_config_schema_name}!") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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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: DownloadManager) -> List[datasets.SplitGenerator]: |
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hf_dset_dict = datasets.load_dataset(_HF_REMOTE_REF, self.config.subset_id) |
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return [datasets.SplitGenerator(name=datasets.Split(dset_key), gen_kwargs={"hf_dset": dset}) for dset_key, dset in hf_dset_dict.items() if dset.num_rows > 0] |
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def _generate_examples(self, hf_dset) -> Tuple[int, Dict]: |
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_config_schema_name = self.config.schema |
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_idx = 0 |
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for datapoints in hf_dset: |
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if _config_schema_name == "source": |
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yield _idx, {colname: datapoints[colname] for colname in self.info.features} |
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elif _config_schema_name == "seacrowd_imtext": |
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yield _idx, {"id": _idx, "image_paths": [datapoints["image_url"]], "texts": datapoints["caption"], "metadata": {"context": "", "labels": []}} |
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else: |
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raise ValueError(f"Received unexpected config schema of {_config_schema_name}!") |
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_idx += 1 |
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