Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
extended|squad
ArXiv:
License:
File size: 4,850 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""SQUAD: The Stanford Question Answering Dataset."""
"""Modified version for fine tuning T5 on Question Generation """
import json
import datasets
# from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
"""
_DESCRIPTION = """\
Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
articles, where the answer to every question is a segment of text, or span, \
from the corresponding reading passage, or the question might be unanswerable.
"""
_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
"train": _URL + "train-v1.1.json",
"dev": _URL + "dev-v1.1.json",
}
class SquadConfig(datasets.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for SQUAD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquadConfig, self).__init__(**kwargs)
class Squad(datasets.GeneratorBasedBuilder):
"""SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
CONTEXT_PREFIX = 'gq: '
QUESTIONS_SEP = ' Question: '
BUILDER_CONFIGS = [
SquadConfig(
name="plain_text",
version=datasets.Version("2.9.0", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"context": datasets.Value("string"),
"questions": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://rajpurkar.github.io/SQuAD-explorer/",
citation=_CITATION,
task_templates=[
],
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
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"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
key = 0
with open(filepath, encoding="utf-8") as f:
squad = json.load(f)
for article in squad["data"]:
for paragraph in article["paragraphs"]:
source_text = self.CONTEXT_PREFIX + paragraph['context'].strip()
# Get questions in order
qas = []
for qa in paragraph['qas']:
earliest_answer_start = min([answer['answer_start'] for answer in qa['answers']])
question = qa['question'].strip()
qas.append((earliest_answer_start, question))
sorted_qas = sorted(qas, key=lambda x: x[0])
only_qs = [qa[1] for qa in sorted_qas]
target_text = self.QUESTIONS_SEP + self.QUESTIONS_SEP.join(only_qs)
target_text = target_text.strip()
yield key, {
"context": source_text,
"questions": target_text}
key += 1
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