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# coding=utf-8
# 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.
"""SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation"""
import json
import datasets
_CITATION = """\
"""
_DESCRIPTION = """\
"""
_HOMEPAGE = "https://github.com/Coldog2333/SuperDialseg"
_LICENSE = """\
"""
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
"train": "https://huggingface.co/datasets/Coldog2333/super_dialseg/resolve/main/train.json",
"validation": "https://huggingface.co/datasets/Coldog2333/super_dialseg/resolve/main/validation.json",
"test": "https://huggingface.co/datasets/Coldog2333/super_dialseg/resolve/main/test.json",
}
class SuperDialsegConfig(datasets.BuilderConfig):
"""BuilderConfig for SuperDialseg"""
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.dataset_name = "super_dialseg"
class SuperDialseg(datasets.GeneratorBasedBuilder):
"""SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation"""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"dial_id": datasets.Value("string"),
"utterance": datasets.features.Sequence(datasets.Value("string")),
"segmentation_label": datasets.features.Sequence(datasets.Value("int32")),
"da": datasets.features.Sequence(datasets.Value("string")),
"role": datasets.features.Sequence(datasets.Value("string")),
"turn_id": datasets.features.Sequence(datasets.Value("int32")),
"topic_id": datasets.features.Sequence(datasets.Value("int32"))
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
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["validation"]}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}
)
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
data = json.load(f)["dial_data"][self.dataset_name]
for id_, row in enumerate(data):
yield id_, {
"dial_id": row["dial_id"],
"utterance": [turn["utterance"] for turn in row["turns"]],
"segmentation_label": [turn["segmentation_label"] for turn in row["turns"]],
"da": [turn["da"] for turn in row["turns"]],
"role": [turn["role"] for turn in row["turns"]],
"turn_id": [turn["turn_id"] for turn in row["turns"]],
"topic_id": [turn["topic_id"] for turn in row["turns"]]
}
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