# 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 = "" _CHINESE_QA = "COVID-Dialogue-Dataset-Chinese.txt" _ENGLISH_QA = "COVID-Dialogue-Dataset-English.txt" class CovidQaUcsd(datasets.GeneratorBasedBuilder): """Dataset has one file having consulatations purely based on COVID queries""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="en", version=VERSION, description="The dataset of medical dialogs related to Covid in English." ), datasets.BuilderConfig( name="zh", version=VERSION, description="The dataset of medical dialogs related to Covid in Chinese." ), ] @property def manual_download_instructions(self): return """\ \nBoth the English and Chinese text files are present in https://github.com/UCSD-AI4H/COVID-Dialogue. It is present as COVID-Dialogue-Dataset-English.txt (for the english dialogues) and COVID-Dialogue-Dataset-Chinese.txt (for the Chinese Dialog). To load the dataset, simple pass the folder where the file is saved to the 'data_dir' param in the datasets.load_dataset(...) option. The data directory can e.g. be "/Downloads/". The data can then be loaded using the below command:\n `datasets.load_dataset("covid_qa_ucsd", name="en", data_dir="/Downloads/")`. Just change the 'name' parameter to 'zh' for Chinese. TAKE CARE NOT TO CHANGE THE NAME OF THE INPUT FILE """ def _info(self): # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset if self.config.name == "zh": # For english dialouge data features = datasets.Features( { "dialogue_id": datasets.Value("int32"), "dialogue_url": datasets.Value("string"), "dialogue_turns": datasets.Sequence( { "speaker": datasets.ClassLabel(names=["病人", "医生"]), "utterance": datasets.Value("string"), } ), } ) if self.config.name == "en": # For english dialouge data features = datasets.Features( { "dialogue_id": datasets.Value("int32"), "dialogue_url": datasets.Value("string"), "dialogue_turns": datasets.Sequence( { "speaker": datasets.ClassLabel(names=["Patient", "Doctor"]), "utterance": datasets.Value("string"), } ), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" if self.config.name == "zh": path_to_manual_file = os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), _CHINESE_QA) if self.config.name == "en": path_to_manual_file = os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), _ENGLISH_QA) if not os.path.exists(path_to_manual_file): raise FileNotFoundError( f"{path_to_manual_file} does not exist. Make sure the file is present in the directory specified in the data_dir specified in the input {dl_manager.manual_dir} `datasets.load_dataset('covid_qa_ucsd', 'en', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})" ) return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path_to_manual_file})] def _generate_examples(self, filepath): """Yields examples. Iterates over the file and creates appropriate dialogue data NOTE: - The code makes some assumption on the structure of the raw .txt file. - There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added. """ data_lang = self.config.name id_ = -1 with open(filepath, encoding="utf-8") as f_in: # Parameters to just "sectionize" the raw data last_part = "" last_dialog = {} last_list = [] last_user = "" check_list = [] # These flags are present to have a single function address both chinese and english data # English data is a little hahazard (i.e. the sentences spans multiple different lines), # Chinese is compact with one line for doctor and patient. conv_flag = False des_flag = False while True: line = f_in.readline() if not line: break # Extracting the dialog id if line[:2] == "id": # Hardcode alert! # Handling ID references that may come in the description # These were observed in the Chinese dataset and were not # followed by numbers try: dialogue_id = int(re.findall(r"\d+", line)[0]) except IndexError: continue # Extracting the url if line[:4] == "http": # Hardcode alert! dialogue_url = line.rstrip() # Extracting the patient info from description. if line[:11] == "Description": # Hardcode alert! last_part = "description" last_dialog = {} last_list = [] last_user = "" last_conv = {"speaker": "", "utterance": ""} while True: line = f_in.readline() if (not line) or (line in ["\n", "\n\r"]): break else: if data_lang == "zh": # Condition in chinese if line[:5] == "病情描述：": # Hardcode alert! last_user = "病人" sen = line[6:].rstrip() des_flag = True if data_lang == "en": last_user = "Patient" sen = line.rstrip() des_flag = True if des_flag: if sen == "": continue if sen in check_list: last_conv["speaker"] = "" last_conv["utterance"] = "" else: last_conv["speaker"] = last_user last_conv["utterance"] = sen check_list.append(sen) des_flag = False break # Extracting the conversation info from dialogue. elif line[:8] == "Dialogue": # Hardcode alert! if last_part == "description" and len(last_conv["utterance"]) > 0: last_part = "dialogue" if data_lang == "zh": last_user = "病人" if data_lang == "en": last_user = "Patient" while True: line = f_in.readline() if (not line) or (line in ["\n", "\n\r"]): conv_flag = False last_user = "" last_list.append(copy.deepcopy(last_conv)) # To ensure close of conversation, only even number of sentences # are extracted last_turn = len(last_list) if int(last_turn / 2) > 0: temp = int(last_turn / 2) id_ += 1 last_dialog["dialogue_id"] = dialogue_id last_dialog["dialogue_url"] = dialogue_url last_dialog["dialogue_turns"] = last_list[: temp * 2] yield id_, last_dialog break if data_lang == "zh": if line[:3] == "病人：" or line[:3] == "医生：": # Hardcode alert! user = line[:2] # Hardcode alert! line = f_in.readline() conv_flag = True # The elif block is to ensure that multi-line sentences are captured. # This has been observed only in english. if data_lang == "en": if line.strip() == "Patient:" or line.strip() == "Doctor:": # Hardcode alert! user = line.replace(":", "").rstrip() line = f_in.readline() conv_flag = True elif line[:2] != "id": # Hardcode alert! conv_flag = True # Continues till the next ID is parsed if conv_flag: sen = line.rstrip() if sen == "": continue if user == last_user: last_conv["utterance"] = last_conv["utterance"] + sen else: last_user = user last_list.append(copy.deepcopy(last_conv)) last_conv["utterance"] = sen last_conv["speaker"] = user