""" SEA Crowd Data Loader for SEA Wiki. """ import json from itertools import product from typing import Dict, List, Tuple import datasets from datasets import load_dataset from datasets.download.download_manager import DownloadManager from seacrowd.sea_datasets.sea_wiki.lang_config import _LANG_CONFIG from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """ @ONLINE{wikidump, author = "Wikimedia Foundation", title = "Wikimedia Downloads", url = "https://dumps.wikimedia.org"} @ONLINE{wikipedia-hf, title = "Huggingface Wikipedia Dataset", url = "https://huggingface.co/datasets/wikipedia"} @ONLINE{wikipedia-hf, title = "Huggingface SEA Wikipedia Dataset", url = "https://huggingface.co/datasets/sabilmakbar/sea_wiki"} """ logger = datasets.logging.get_logger(__name__) _LOCAL = False _LANGUAGES = list(_LANG_CONFIG.keys()) _DATASETNAME = "sea_wiki" _DESCRIPTION = """\ SEA Lang & Local Langs Wikipedia Archives, dumped from WIkipedia HF and processed by boilerplate removal. This dataset consists of URL of referred Wikipedia Article, its Title, and its Text Data (Article Contents). """ _HOMEPAGE = "https://huggingface.co/datasets/sabilmakbar/sea_wiki" _LICENSE = Licenses.CC_BY_SA_4_0.value # url won't be used since it will implement load_dataset method on HF URL provided _URL = "https://huggingface.co/datasets/sabilmakbar/sea_wiki" _SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING, Tasks.SUMMARIZATION] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" CONFIG_SUFFIXES_FOR_TASK = ["ssp", "t2t"] def conform_init_config(): """Assertion Function for Instantiated Configs""" if len(_LANGUAGES) == 0: raise AssertionError("No Languages detected from config!") if len(CONFIG_SUFFIXES_FOR_TASK) != len(_SUPPORTED_TASKS): raise AssertionError("Config prefixes doesn't matched in terms of `len` with `_SUPPORTED_TASKS`!") if len(CONFIG_SUFFIXES_FOR_TASK) == 0: raise AssertionError("Config prefixes and `_SUPPORTED_TASKS` have `len` of 0!") conform_init_config() # construct zipped arg for config instantiation SCHEMA_PREFIX_AND_VERSION_PAIRS = list(zip(("source", "seacrowd"), (_SOURCE_VERSION, _SEACROWD_VERSION))) CONFIG_NAME_AND_TASKS_PAIRS = list(zip(CONFIG_SUFFIXES_FOR_TASK, _SUPPORTED_TASKS)) def construct_configs(languages: list = None) -> List[SEACrowdConfig]: """ The function `construct_configs` constructs a list of SEACrowdConfig objects based on the provided languages or a default language, and returns the list. input: languages (list, default None): The `languages` parameter is a list that specifies the languages for which the configurations need to be constructed. If no languages are provided (value=None), the first value in language config will be used. output: a list of `SEACrowdConfig` objects based on instantiated init variables """ # set output var config_list = [] # set default task for default config w/o task arg name (set to Tasks.SUMMARIZATION) _DEFAULT_TASK_IDX = [idx for idx, val in enumerate(_SUPPORTED_TASKS) if val == Tasks.SUMMARIZATION] # assert `_DEFAULT_TASK_IDX` to have len of 1 if len(_DEFAULT_TASK_IDX) != 1: raise AssertionError("Unexpected `_DEFAULT_TASK` #item!") _DEFAULT_CONFIG_SUFFIX, _DEFAULT_TASK = list(CONFIG_NAME_AND_TASKS_PAIRS)[_DEFAULT_TASK_IDX[0]] # check `languages` variable and create config accordingly if languages is None: # set languages arg as list of first entry in `_LANGUAGES` if no lang arg received _languages = _LANGUAGES[0] config_list += [ SEACrowdConfig( name=f"{_DATASETNAME}_{config_name_prefix}", version=datasets.Version(version), description=f"{_DATASETNAME} {config_name_prefix} schema for default task arg ({_DEFAULT_TASK})", schema=f"{config_name_prefix}_{_DEFAULT_CONFIG_SUFFIX}", subset_id=_languages, ) for (config_name_prefix, version) in SCHEMA_PREFIX_AND_VERSION_PAIRS ] config_list += [ SEACrowdConfig( name=f"{_DATASETNAME}_{config_name_prefix}_{config_name_suffix}", version=datasets.Version(version), description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name}", schema=f"{config_name_prefix}_{config_name_suffix}", subset_id=_languages, ) for (config_name_prefix, version), (config_name_suffix, task_obj) in product(SCHEMA_PREFIX_AND_VERSION_PAIRS, CONFIG_NAME_AND_TASKS_PAIRS) ] # else, construct configs based on its lang else: for _LANG in languages: config_list += [ SEACrowdConfig( name=f"{_DATASETNAME}_{config_name_prefix}_{_LANG}_{config_name_suffix}", version=datasets.Version(version), description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name} and language code {_LANG}", schema=f"{config_name_prefix}_{config_name_suffix}", subset_id=_LANG, ) for (config_name_prefix, version), (config_name_suffix, task_obj) in product(SCHEMA_PREFIX_AND_VERSION_PAIRS, CONFIG_NAME_AND_TASKS_PAIRS) ] return config_list class SEAWikiDataset(datasets.GeneratorBasedBuilder): """SEA Wiki dataset from https://huggingface.co/datasets/sabilmakbar/sea_wiki""" # get all schema w/o lang arg + get all schema w/ lang arg BUILDER_CONFIGS = construct_configs() + construct_configs(_LANGUAGES) def _info(self) -> datasets.DatasetInfo: _config_schema_name = self.config.schema logger.info(f"Received schema name: {self.config.schema}") # self supervised training schema if CONFIG_SUFFIXES_FOR_TASK[0] in _config_schema_name: if "source" in _config_schema_name: features = datasets.Features({"url": datasets.Value("string"), "text": datasets.Value("string")}) elif "seacrowd" in _config_schema_name: features = schemas.ssp_features else: raise ValueError(f"Unexpected schema received! {_config_schema_name}") # summarization schema elif CONFIG_SUFFIXES_FOR_TASK[1] in _config_schema_name: if "source" in _config_schema_name: features = datasets.Features({"url": datasets.Value("string"), "title": datasets.Value("string"), "text": datasets.Value("string")}) elif "seacrowd" in _config_schema_name: features = schemas.text2text_features else: raise ValueError(f"Unexpected schema received! {_config_schema_name}") else: raise ValueError(f"Received unexpected config schema of {_config_schema_name}!") return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]: # args of dl_manager is a placeholder since this data loader will wrap the hf `load_dataset` from given _URL # directly using `_load_hf_data_from_remote` return [datasets.SplitGenerator(name=datasets.Split.TRAIN)] def _load_hf_data_from_remote(self): # construct remote_hf_reference by the last 2 of string-spliited of "/" _remote_hf_reference = "/".join(_URL.split("/")[-2:]) _lang_args = _LANG_CONFIG[self.config.subset_id]["source_subset"] _split = "train" logger.info(f"Loading dataset from remote HF {_remote_hf_reference} with seacrowd lang args of {self.config.subset_id} and source lang args of {_lang_args} and split args of {_split}") _hf_dataset_source = load_dataset(_remote_hf_reference, lang=_lang_args, split=_split) return _hf_dataset_source def _generate_examples(self) -> Tuple[int, Dict]: _config_schema_name = self.config.schema loaded_data = self._load_hf_data_from_remote() # iterate over datapoints and arrange hf dataset schema in source to match w/ config args: for id_, _data in enumerate(loaded_data): if "source" in _config_schema_name: yield id_, {colname: _data[colname] for colname in self.info.features} # for ssp schema elif "seacrowd" in _config_schema_name and CONFIG_SUFFIXES_FOR_TASK[0] in _config_schema_name: yield id_, {"id": id_, "text": _data["text"]} # for summary schema elif "seacrowd" in _config_schema_name and CONFIG_SUFFIXES_FOR_TASK[1] in _config_schema_name: yield id_, {"id": id_, "text_1": _data["text"], "text_2": _data["title"], "text_1_name": "document", "text_2_name": "title"} else: raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")