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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""MuTual dataset."""
import json
import os
from pathlib import Path
import datasets
_CITATION = """\
@inproceedings{mutual,
title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
author = "Cui, Leyang and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
}
"""
_DESCRIPTION = """\
MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is
modified from Chinese high school English listening comprehension test data.
"""
_HOMEPAGE = "https://github.com/Nealcly/MuTual"
_LICENSE = "No license found"
_URLS = "https://github.com/Nealcly/MuTual/archive/master.zip"
class Mutual(datasets.GeneratorBasedBuilder):
"""MuTual: A Dataset for Multi-Turn Dialogue Reasoning"""
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="mutual", version=VERSION, description="The MuTual dataset."
),
datasets.BuilderConfig(
name="mutual_plus",
version=VERSION,
description="MuTualPlus is a more difficult MuTual that replaces positive responses with a safe responses.",
),
]
def _info(self):
features = datasets.Features(
{
"answers": datasets.Value("string"),
"options": datasets.features.Sequence(datasets.Value("string")),
"article": datasets.Value("string"),
"id": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=f"{_DESCRIPTION}\n{self.config.description}",
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"basepath": os.path.join(
data_dir, "MuTual-master", "data", self.config.name, "train"
),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"basepath": os.path.join(
data_dir, "MuTual-master", "data", self.config.name, "test"
),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"basepath": os.path.join(
data_dir, "MuTual-master", "data", self.config.name, "dev"
),
"split": "dev",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, basepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
key = 0
for file in sorted(Path(basepath).iterdir()):
if file.suffix != ".txt":
continue
with open(file, "r", encoding="utf-8") as f:
data_str = f.read()
# Ignore the occasional empty file.
if not data_str:
continue
data = json.loads(data_str)
yield key, {
"answers": data["answers"],
"options": data["options"],
"article": data["article"],
"id": data["id"],
}
key += 1 |