Datasets:
Tasks:
Question Answering
Languages:
Icelandic
Multilinguality:
monolingual
Language Creators:
curated
Annotations Creators:
curated
Source Datasets:
original
Tags:
License:
File size: 4,483 Bytes
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# Adapted from the SQuAD dataset file
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
_CITATION = """\
"""
_DESCRIPTION = """\
"""
_URL = "https://repository.clarin.is/repository/xmlui/bitstream/handle/20.500.12537/143/"
_URLS = {
"train": _URL + "nqii_train_squad_format_tok.json",
"dev": _URL + "nqii_dev_squad_format_tok.json",
"test": _URL + "nqii_test_squad_format_tok.json"
}
class NQiIConfig(datasets.BuilderConfig):
"""BuilderConfig."""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(NQiIConfig, self).__init__(**kwargs)
class NQiI(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NQiIConfig(name="icelandic-qa-NQiI", version=datasets.Version("1.0.0"), description=""),
]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
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"),
}
),
# 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://vesteinn.is/qa/",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(squad_v2): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(squad_v2): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
squad = json.load(f)
for example in squad["data"]:
title = example.get("title", "")
for paragraph in example["paragraphs"]:
context = paragraph["context"] # do not strip leading blank spaces GH-2585
for qa in paragraph["qas"]:
question = qa["question"]
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"] 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,
},
}
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