| | 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, |
| | |
| | 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 |
| |
|
