CMB.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""The Chinese Medical Benchmark (CMB)"""

import csv
import os
import sys
import json
import io
import textwrap

import numpy as np

import datasets

_CMB_CITATION = """\
coming soon~
"""

_CMB_DESCRIPTION = """\

Chinese Medical Benchmark

"""

_DATASETS_FILE = "https://huggingface.co/datasets/FreedomIntelligence/CMB/resolve/main/CMB-datasets.zip"


class CMBConfig(datasets.BuilderConfig):
    """BuilderConfig for CMB"""

    def __init__(
            self,
            features,
            data_url,
            data_dir,
            citation,
            url,
            **kwargs,
    ):


        super(CMBConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
        self.features = features
        self.data_url = data_url
        self.data_dir = data_dir
        self.citation = citation
        self.url = url


class CMB(datasets.GeneratorBasedBuilder):
    """The Chinese Medical Benchmark (CMB)"""

    BUILDER_CONFIGS = [
        CMBConfig(
            name="exam",
            description=textwrap.dedent(
                """\
            全方位多层次注入和测评模型医疗知识，包含 train val test 三个组成部分."""
            ),
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "exam_type": datasets.Value("string"),
                    "exam_class": datasets.Value("string"),
                    "exam_subject": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "question_type": datasets.Value("string"),
                    "option": datasets.Value("string"),
                    "answer": datasets.Value("string"),
                    "explanation": datasets.Value("string")

                }
            ),
            data_url=_DATASETS_FILE,
            data_dir="CMB-Exam",
            citation=textwrap.dedent(
                """\

            }"""
            ),
            url="https://github.com/FreedomIntelligence/CMB",
        ),
        CMBConfig(
            name="clin",
            description=textwrap.dedent(
                """\
            测评复杂临床问诊能力
            """
            ),
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "description": datasets.Value("string"),
                    "QA_pairs": datasets.Value("string")

                }
            ),

            data_url=_DATASETS_FILE,
            data_dir="CMB-Clin",
            citation=textwrap.dedent(
                """\

            }"""
            ),
            url="https://github.com/FreedomIntelligence/CMB",
        ),

    ]

    def _info(self):

        return datasets.DatasetInfo(
            description=_CMB_DESCRIPTION,
            features=self.config.features,
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _CMB_CITATION,
        )

    def _split_generators(self, dl_manager):
        if self.config.name == "exam":
            data_file = dl_manager.extract(self.config.data_url)
            main_data_dir = os.path.join(data_file, self.config.data_dir)

            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "data_file": os.path.join(main_data_dir, 'CMB-train', 'CMB-train-merge.json'),
                        "split": "train",
                    },
                )
                ,
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "data_file": os.path.join(main_data_dir, 'CMB-val', 'CMB-val-merge.json'),
                        "split": "val",
                    },
                )
                ,
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "data_file": os.path.join(main_data_dir, 'CMB-test', 'CMB-test-choice-question-merge.json'),
                        "split": "test",
                    },
                )
            ]

        if self.config.name == "clin":
            data_file = dl_manager.extract(self.config.data_url)
            main_data_dir = os.path.join(data_file, self.config.data_dir)
            return [


                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "data_file": os.path.join(main_data_dir, 'CMB-Clin-qa.json'),
                        "split": "test",
                    },
                )
            ]


    def _generate_examples(self, data_file, split, mrpc_files=None):

        if self.config.name == 'exam':

            examples = json.loads(io.open(data_file, 'r', encoding='utf-8').read())

            for idx in range(len(examples)):
                vals = examples[idx]
                vals['explanation'] = vals.get('explanation','')
                vals['answer'] = vals.get('answer','')
                vals['id'] = vals.get('id',idx)
                yield idx, vals

        if self.config.name == 'clin':
            examples = json.loads(io.open(data_file, 'r', encoding='utf-8').read())
            for idx in range(len(examples)):
                vals = examples[idx]
                vals['id'] = vals.get('id',idx)
                yield idx, vals



if __name__ == '__main__':
    from datasets import load_dataset

    dataset = load_dataset('CMB.py', 'exam')
    # dataset = load_dataset('CMB.py', 'clin')

    print()