import json import os from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = r"""\ @article{lewis2019mlqa, author={Lewis, Patrick and O\{g}uz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, title={MLQA: Evaluating Cross-lingual Extractive Question Answering}, journal={arXiv preprint arXiv:1910.07475}, year={2019} } """ _DATASETNAME = "mlqa" _DESCRIPTION = """\ MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average. """ _HOMEPAGE = "https://github.com/facebookresearch/MLQA" _LICENSE = Licenses.CC_BY_SA_3_0.value _LANGUAGES = ["vie"] _URL = "https://dl.fbaipublicfiles.com/MLQA/" _DEV_TEST_URL = "MLQA_V1.zip" _TRANSLATE_TEST_URL = "mlqa-translate-test.tar.gz" _TRANSLATE_TRAIN_URL = "mlqa-translate-train.tar.gz" _SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" _LOCAL = False class MLQADataset(datasets.GeneratorBasedBuilder): """ MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average. """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) subsets = [ "mlqa-translate-test.vi", "mlqa-translate-train.vi", "mlqa.vi.ar", "mlqa.vi.de", "mlqa.vi.zh", "mlqa.vi.en", "mlqa.vi.es", "mlqa.vi.hi", "mlqa.vi.vi", "mlqa.ar.vi", "mlqa.de.vi", "mlqa.zh.vi", "mlqa.en.vi", "mlqa.es.vi", "mlqa.hi.vi", ] BUILDER_CONFIGS = [ SEACrowdConfig( name="{sub}_source".format(sub=subset), version=datasets.Version(_SOURCE_VERSION), description="{sub} source schema".format(sub=subset), schema="source", subset_id="{sub}".format(sub=subset), ) for subset in subsets ] + [ SEACrowdConfig( name="{sub}_seacrowd_qa".format(sub=subset), version=datasets.Version(_SEACROWD_VERSION), description="{sub} SEACrowd schema".format(sub=subset), schema="seacrowd_qa", subset_id="{sub}".format(sub=subset), ) for subset in subsets ] DEFAULT_CONFIG_NAME = "mlqa.vi.vi_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( {"context": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.Features({"answer_start": [datasets.Value("int64")], "text": [datasets.Value("string")]}), "id": datasets.Value("string")} ) elif self.config.schema == "seacrowd_qa": features = schemas.qa_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: name_split = self.config.name.split("_") url = "" data_path = "" if name_split[0].startswith("mlqa-translate-train"): config_name, lang = name_split[0].split(".") url = _URL + _TRANSLATE_TRAIN_URL data_path = dl_manager.download(url) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # Whatever you put in gen_kwargs will be passed to _generate_examples gen_kwargs={ "filepath": f"{config_name}/{lang}_squad-translate-train-train-v1.1.json", "files": dl_manager.iter_archive(data_path), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": f"{config_name}/{lang}_squad-translate-train-dev-v1.1.json", "files": dl_manager.iter_archive(data_path), "split": "test", }, ), ] elif name_split[0].startswith("mlqa-translate-test"): config_name, lang = name_split[0].split(".") url = _URL + _TRANSLATE_TEST_URL data_path = dl_manager.download(url) return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": f"{config_name}/translate-test-context-{lang}-question-{lang}.json", "files": dl_manager.iter_archive(data_path), "split": "test", }, ), ] elif name_split[0].startswith("mlqa."): url = _URL + _DEV_TEST_URL data_path = dl_manager.download_and_extract(url) ctx_lang, qst_lang = name_split[0].split(".")[1:] return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join( os.path.join(data_path, "MLQA_V1/dev"), f"dev-context-{ctx_lang}-question-{qst_lang}.json", ), "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join( os.path.join(data_path, "MLQA_V1/test"), f"test-context-{ctx_lang}-question-{qst_lang}.json", ), "split": "test", }, ), ] elif name_split[0] == "mlqa": url = _URL + _DEV_TEST_URL data_path = dl_manager.download_and_extract(url) ctx_lang = qst_lang = "vi" return [ datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join( os.path.join(data_path, "MLQA_V1/dev"), f"dev-context-{ctx_lang}-question-{qst_lang}.json", ), "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join( os.path.join(data_path, "MLQA_V1/test"), f"test-context-{ctx_lang}-question-{qst_lang}.json", ), "split": "test", }, ), ] def _generate_examples(self, filepath: Path, split: str, files=None) -> Tuple[int, Dict]: is_config_ok = True if self.config.name.startswith("mlqa-translate"): for path, f in files: if path == filepath: data = json.loads(f.read().decode("utf-8")) break elif self.config.schema == "source" or self.config.schema == "seacrowd_qa": with open(filepath, encoding="utf-8") as f: data = json.load(f) else: is_config_ok = False raise ValueError(f"Invalid config: {self.config.name}") if is_config_ok: count = 0 for examples in data["data"]: for example in examples["paragraphs"]: context = example["context"] for qa in example["qas"]: question = qa["question"] id_ = qa["id"] answers = qa["answers"] answers_start = [answer["answer_start"] for answer in answers] answers_text = [answer["text"] for answer in answers] if self.config.schema == "source": yield count, { "context": context, "question": question, "answers": {"answer_start": answers_start, "text": answers_text}, "id": id_, } count += 1 elif self.config.schema == "seacrowd_qa": yield count, {"question_id": id_, "context": context, "question": question, "answer": {"answer_start": answers_start[0], "text": answers_text[0]}, "id": id_, "choices": [], "type": "extractive", "document_id": count, "meta":{}} count += 1