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