| 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 |
|
|
