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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."""
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """\
"""
_URL = "https://raw.githubusercontent.com/meetyildiz/toqad/main/"
_URLS = {
"train": _URL + "train.json",
"dev": _URL + "dev.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."""
BUILDER_CONFIGS = [
SquadConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text",
),
]
def _info(self):
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://rajpurkar.github.io/SQuAD-explorer/",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
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 document in squad["data"]:
for par in document['paragraphs']:
for qas in par['qas']:
if len(qas['answers']) == 0: #no answer
ans_start = -1
ans_end = -1
ans_text = ""
else:
ans_start = int(qas['answers'][0]['answer_start'])
ans_end = ans_start + len(qas['answers'][0]['text'])
ans_text = qas['answers'][0]['text']
ex = {
"id": qas["id"],
"title": document["title"],
"context": par['context'],
"question": qas['question'],
"answers": {
"text": [ans_text],
"answer_start": [ans_start],
},
}
yield key, ex
key += 1
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