Datasets:

khalidalt
/

tydiqa-primary

	import json
	import textwrap

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive

	# 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).
	"""


	_LANG = ["arabic", "bengali", "english", "finnish", "indonesian", "japanese", "korean", "russian", "swahili", "telugu", "thai"]

	_URL = "https://huggingface.co/datasets/khalidalt/tydiqa-goldp/resolve/main/primary_tasks/{split}/{language}-{split}.jsonl"
	_VERSION = datasets.Version("1.1.0", "")


	class tydiqa_Primary(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=lang,
	description=f"tydiqa-primary language {lang}",
	version=_VERSION,
	)
	for lang in _LANG
	]


	def _info(self):
	# TODO(tydiqa): Specifies the datasets.DatasetInfo object

	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('int32'),
	"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('int32'),
	"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,
	)

	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
	language = self.config.name
	splits = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev"}

	data_urls = {
	split: _URL.format(language=language, split=splits[split]) for split in splits
	}

	dl_paths = dl_manager.download(data_urls)
	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={"filepath": dl_paths[split]},
	)
	for split in splits
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(tydiqa): Yields (key, example) tuples from the dataset

	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,
	}