covid_dialogue.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.
"""Covid Dialog dataset in English and Chinese"""


import copy
import os
import re
import textwrap

import datasets


# BibTeX citation
_CITATION = """\
@article{ju2020CovidDialog,
  title={CovidDialog: Medical Dialogue Datasets about COVID-19},
  author={Ju, Zeqian and Chakravorty, Subrato and He, Xuehai and Chen, Shu and Yang, Xingyi and Xie, Pengtao},
  journal={ https://github.com/UCSD-AI4H/COVID-Dialogue},
  year={2020}
}
"""

# Official description of the dataset
_DESCRIPTION = textwrap.dedent(
    """
    COVID-Dialogue-Dataset is amedical dialogue dataset about COVID-19 and other types of pneumonia.
    Patients who are concerned that they may be infected by COVID-19 or other pneumonia consult doctors and doctors provide advice.
    There are 603 consultations in English and 1393 consultations in Chinese.
    """
)

# Link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/UCSD-AI4H/COVID-Dialogue"

_LICENSE = ""


import datasets
import os
import json


class CovidDialogueHelm(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [datasets.BuilderConfig(name="default", version=datasets.Version("1.0.0"), description=_DESCRIPTION)]

    def _info(self):
        features = datasets.Features(
            {
                "query": datasets.Value("string"),
                "answer": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=f"Covid Dialogue dataset, as preprocessed and shuffled in HELM",
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        test_target = dl_manager.download("test.source")
        test_source = dl_manager.download("test.source")
        train_source = dl_manager.download("train.source")
        train_target = dl_manager.download("train.target")
        val_source = dl_manager.download("val.source")
        val_target = dl_manager.download("val.target")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"target": train_target, "source": train_source},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"target": val_target, "source": val_source},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"target": test_target, "source": test_source},
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, source, target):
        with open(source, encoding="utf-8") as f_source:
            with open(target, encoding="utf-8") as f_target:
                for idx, (s, t) in enumerate(zip(f_source, f_target)):
                    yield idx, {"query": s, "answer": t}