Datasets:

FreedomIntelligence
/

CMB

	# 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()