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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import json
import os
import datasets
from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
Duconv is a chinese convolution \
dataset, designed to evaluate the dialogue models.
"""
_URL = "https://bj.bcebos.com/paddlenlp/datasets/DuConv.zip"
class DuconvConfig(datasets.BuilderConfig):
"""BuilderConfig for Duconv."""
def __init__(self, **kwargs):
"""BuilderConfig for Duconv.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(DuconvConfig, self).__init__(**kwargs)
class Duconv(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
DuconvConfig(
name="DuConv",
version=datasets.Version("1.0.0", ""),
description=_DESCRIPTION,
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"id":
datasets.Value("string"),
"goal":
datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
"knowledge":
datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
"conversation":
datasets.Sequence(datasets.Value("string")),
"history":
datasets.Sequence(datasets.Value("string")),
"response":
datasets.Value("string"),
}),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://arxiv.org/pdf/1906.05572.pdf",
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(name="train",
gen_kwargs={
"filepath":
os.path.join(dl_dir, 'DuConv',
'train.txt'),
}),
datasets.SplitGenerator(name="dev",
gen_kwargs={
"filepath":
os.path.join(dl_dir, 'DuConv',
'dev.txt'),
}),
datasets.SplitGenerator(name="test_1",
gen_kwargs={
"filepath":
os.path.join(dl_dir, 'DuConv',
'test_1.txt'),
}),
datasets.SplitGenerator(name="test_2",
gen_kwargs={
"filepath":
os.path.join(dl_dir, 'DuConv',
'test_2.txt'),
}),
]
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, 'r', encoding="utf-8") as fin:
for line in fin:
duconv = json.loads(line)
goal = duconv["goal"] if "goal" in duconv.keys() else [[]]
knowledge = duconv["knowledge"] if "knowledge" in duconv.keys(
) else [[]]
conversation = duconv[
"conversation"] if "conversation" in duconv.keys() else []
history = duconv["history"] if "history" in duconv.keys(
) else []
response = duconv["response"] if "response" in duconv.keys(
) else ""
yield key, {
"id": str(key),
"goal": goal,
"knowledge": knowledge,
"conversation": conversation,
"history": history,
"response": response,
}
key += 1
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