super_dialseg.py

# 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"]]
                }