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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
"""(SC)^2QA: Self-Contained Summary-Centric QA Dataset."""
import csv
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{zhou2021generating,
author = {Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri},
title = "{Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning}",
conference = {The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)},
year = 2021,
}
"""
_DESCRIPTION = """\
"""
_URLS = {
"train":"https://huggingface.co/datasets/sc2qa/sc2qa_commoncrawl/resolve/main/train.csv",
"val":"https://huggingface.co/datasets/sc2qa/sc2qa_commoncrawl/resolve/main/val.csv",
"test":"https://huggingface.co/datasets/sc2qa/sc2qa_commoncrawl/resolve/main/test.csv",
}
class SC2QAConfig(datasets.BuilderConfig):
"""BuilderConfig for (SC)^2QA."""
def __init__(self, **kwargs):
"""BuilderConfig for (SC)^2QA.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SC2QAConfig, self).__init__(**kwargs)
class SC2QA(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
SC2QAConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text",
),
]
def _info(self):
# Should return a datasets.DatasetInfo object
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"question": datasets.Value("string"),
"article": datasets.Value("string"),
"summary": datasets.Value("string"),
"model source": datasets.Value("string"),
"length bucket": datasets.Value("int8"),
"url": datasets.Value("string"),
"qa classifier score": datasets.Value("float"),
}
),
citation=_CITATION,
)
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["val"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"], "split": "test"}),
]
def _generate_examples(self, filepath, split=None):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
key = 0
with open(filepath, encoding="ascii", errors='ignore') as f:
csv_reader = csv.DictReader(f)
for i, row in enumerate(csv_reader):
if split == "test": # This avoids error when doing data type converation from empty string
row["length bucket"] = "1"
row["qa classifier score"] = "0.0"
yield i, row
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