Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Size:
100K - 1M
License:
| """TODO(tydiqa): Add a description here.""" | |
| from __future__ import absolute_import, division, print_function | |
| import json | |
| import textwrap | |
| import datasets | |
| # TODO(tydiqa): BibTeX citation | |
| _CITATION = """\ | |
| @article{tydiqa, | |
| title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, | |
| author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki} | |
| year = {2020}, | |
| journal = {Transactions of the Association for Computational Linguistics} | |
| } | |
| """ | |
| # TODO(tydiqa): | |
| _DESCRIPTION = """\ | |
| TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. | |
| The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language | |
| expresses -- such that we expect models performing well on this set to generalize across a large number of the languages | |
| in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic | |
| information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but | |
| don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without | |
| the use of translation (unlike MLQA and XQuAD). | |
| """ | |
| _URL = "https://storage.googleapis.com/tydiqa/" | |
| _PRIMARY_URLS = { | |
| "train": _URL + "v1.0/tydiqa-v1.0-train.jsonl.gz", | |
| "dev": _URL + "v1.0/tydiqa-v1.0-dev.jsonl.gz", | |
| } | |
| _SECONDARY_URLS = { | |
| "train": _URL + "v1.1/tydiqa-goldp-v1.1-train.json", | |
| "dev": _URL + "v1.1/tydiqa-goldp-v1.1-dev.json", | |
| } | |
| class TydiqaConfig(datasets.BuilderConfig): | |
| """ BuilderConfig for Tydiqa""" | |
| def __init__(self, **kwargs): | |
| """ | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(TydiqaConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
| class Tydiqa(datasets.GeneratorBasedBuilder): | |
| """TODO(tydiqa): Short description of my dataset.""" | |
| # TODO(tydiqa): Set up version. | |
| VERSION = datasets.Version("0.1.0") | |
| BUILDER_CONFIGS = [ | |
| TydiqaConfig( | |
| name="primary_task", | |
| description=textwrap.dedent( | |
| """\ | |
| Passage selection task (SelectP): Given a list of the passages in the article, return either (a) the index of | |
| the passage that answers the question or (b) NULL if no such passage exists. | |
| Minimal answer span task (MinSpan): Given the full text of an article, return one of (a) the start and end | |
| byte indices of the minimal span that completely answers the question; (b) YES or NO if the question requires | |
| a yes/no answer and we can draw a conclusion from the passage; (c) NULL if it is not possible to produce a | |
| minimal answer for this question.""" | |
| ), | |
| ), | |
| TydiqaConfig( | |
| name="secondary_task", | |
| description=textwrap.dedent( | |
| """Gold passage task (GoldP): Given a passage that is guaranteed to contain the | |
| answer, predict the single contiguous span of characters that answers the question. This is more similar to | |
| existing reading comprehension datasets (as opposed to the information-seeking task outlined above). | |
| This task is constructed with two goals in mind: (1) more directly comparing with prior work and (2) providing | |
| a simplified way for researchers to use TyDi QA by providing compatibility with existing code for SQuAD 1.1, | |
| XQuAD, and MLQA. Toward these goals, the gold passage task differs from the primary task in several ways: | |
| only the gold answer passage is provided rather than the entire Wikipedia article; | |
| unanswerable questions have been discarded, similar to MLQA and XQuAD; | |
| we evaluate with the SQuAD 1.1 metrics like XQuAD; and | |
| Thai and Japanese are removed since the lack of whitespace breaks some tools. | |
| """ | |
| ), | |
| ), | |
| ] | |
| def _info(self): | |
| # TODO(tydiqa): Specifies the datasets.DatasetInfo object | |
| if self.config.name == "primary_task": | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # datasets.features.FeatureConnectors | |
| features=datasets.Features( | |
| { | |
| "passage_answer_candidates": datasets.features.Sequence( | |
| { | |
| "plaintext_start_byte": datasets.Value("int32"), | |
| "plaintext_end_byte": datasets.Value("int32"), | |
| } | |
| ), | |
| "question_text": datasets.Value("string"), | |
| "document_title": datasets.Value("string"), | |
| "language": datasets.Value("string"), | |
| "annotations": datasets.features.Sequence( | |
| { | |
| # 'annotation_id': datasets.Value('variant'), | |
| "passage_answer_candidate_index": datasets.Value("int32"), | |
| "minimal_answers_start_byte": datasets.Value("int32"), | |
| "minimal_answers_end_byte": datasets.Value("int32"), | |
| "yes_no_answer": datasets.Value("string"), | |
| } | |
| ), | |
| "document_plaintext": datasets.Value("string"), | |
| # 'example_id': datasets.Value('variant'), | |
| "document_url": datasets.Value("string") | |
| # These are the features of your dataset like images, labels ... | |
| } | |
| ), | |
| # If there's a common (input, target) tuple from the features, | |
| # specify them here. They'll be used if as_supervised=True in | |
| # builder.as_dataset. | |
| supervised_keys=None, | |
| # Homepage of the dataset for documentation | |
| homepage="https://github.com/google-research-datasets/tydiqa", | |
| citation=_CITATION, | |
| ) | |
| elif self.config.name == "secondary_task": | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "context": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "answers": datasets.features.Sequence( | |
| { | |
| "text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| } | |
| ), | |
| } | |
| ), | |
| # No default supervised_keys (as we have to pass both question | |
| # and context as input). | |
| supervised_keys=None, | |
| homepage="https://github.com/google-research-datasets/tydiqa", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| # TODO(tydiqa): Downloads the data and defines the splits | |
| # dl_manager is a datasets.download.DownloadManager that can be used to | |
| # download and extract URLs | |
| primary_downloaded = dl_manager.download_and_extract(_PRIMARY_URLS) | |
| secondary_downloaded = dl_manager.download_and_extract(_SECONDARY_URLS) | |
| if self.config.name == "primary_task": | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": primary_downloaded["train"]}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": primary_downloaded["dev"]}, | |
| ), | |
| ] | |
| elif self.config.name == "secondary_task": | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": secondary_downloaded["train"]}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": secondary_downloaded["dev"]}, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """Yields examples.""" | |
| # TODO(tydiqa): Yields (key, example) tuples from the dataset | |
| if self.config.name == "primary_task": | |
| with open(filepath, encoding="utf-8") as f: | |
| for id_, row in enumerate(f): | |
| data = json.loads(row) | |
| passages = data["passage_answer_candidates"] | |
| end_byte = [passage["plaintext_end_byte"] for passage in passages] | |
| start_byte = [passage["plaintext_start_byte"] for passage in passages] | |
| title = data["document_title"] | |
| lang = data["language"] | |
| question = data["question_text"] | |
| annotations = data["annotations"] | |
| # annot_ids = [annotation["annotation_id"] for annotation in annotations] | |
| yes_no_answers = [annotation["yes_no_answer"] for annotation in annotations] | |
| min_answers_end_byte = [ | |
| annotation["minimal_answer"]["plaintext_end_byte"] for annotation in annotations | |
| ] | |
| min_answers_start_byte = [ | |
| annotation["minimal_answer"]["plaintext_start_byte"] for annotation in annotations | |
| ] | |
| passage_cand_answers = [ | |
| annotation["passage_answer"]["candidate_index"] for annotation in annotations | |
| ] | |
| doc = data["document_plaintext"] | |
| # example_id = data["example_id"] | |
| url = data["document_url"] | |
| yield id_, { | |
| "passage_answer_candidates": { | |
| "plaintext_start_byte": start_byte, | |
| "plaintext_end_byte": end_byte, | |
| }, | |
| "question_text": question, | |
| "document_title": title, | |
| "language": lang, | |
| "annotations": { | |
| # 'annotation_id': annot_ids, | |
| "passage_answer_candidate_index": passage_cand_answers, | |
| "minimal_answers_start_byte": min_answers_start_byte, | |
| "minimal_answers_end_byte": min_answers_end_byte, | |
| "yes_no_answer": yes_no_answers, | |
| }, | |
| "document_plaintext": doc, | |
| # 'example_id': example_id, | |
| "document_url": url, | |
| } | |
| elif self.config.name == "secondary_task": | |
| with open(filepath, encoding="utf-8") as f: | |
| data = json.load(f) | |
| for article in data["data"]: | |
| title = article.get("title", "").strip() | |
| for paragraph in article["paragraphs"]: | |
| context = paragraph["context"].strip() | |
| for qa in paragraph["qas"]: | |
| question = qa["question"].strip() | |
| id_ = qa["id"] | |
| answer_starts = [answer["answer_start"] for answer in qa["answers"]] | |
| answers = [answer["text"].strip() for answer in qa["answers"]] | |
| # Features currently used are "context", "question", and "answers". | |
| # Others are extracted here for the ease of future expansions. | |
| yield id_, { | |
| "title": title, | |
| "context": context, | |
| "question": question, | |
| "id": id_, | |
| "answers": { | |
| "answer_start": answer_starts, | |
| "text": answers, | |
| }, | |
| } | |