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# coding=utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# 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
import json
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
DureaderRobust is a chinese reading comprehension \
dataset, designed to evaluate the MRC models from \
three aspects: over-sensitivity, over-stability \
and generalization.
"""
_URL = "https://bj.bcebos.com/paddlenlp/datasets/dureader_robust-data.tar.gz"
class DureaderRobustConfig(datasets.BuilderConfig):
"""BuilderConfig for DureaderRobust."""
def __init__(self, **kwargs):
"""BuilderConfig for DureaderRobust.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(DureaderRobustConfig, self).__init__(**kwargs)
class DureaderRobust(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
DureaderRobustConfig(
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://arxiv.org/abs/2004.11142",
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir,'dureader_robust-data', 'train.json')}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir,'dureader_robust-data', 'dev.json')}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir,'dureader_robust-data', 'test.json')}),
]
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:
durobust = json.load(f)
for article in durobust["data"]:
title = article.get("title", "")
for paragraph in article["paragraphs"]:
context = paragraph["context"] # do not strip leading blank spaces GH-2585
for qa in paragraph["qas"]:
answer_starts = [answer["answer_start"] for answer in qa.get("answers",'')]
answers = [answer["text"] for answer in qa.get("answers",'')]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield key, {
"title": title,
"context": context,
"question": qa["question"],
"id": qa["id"],
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
key += 1
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