Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- expert-generated
+- found
+languages:
+  en:
+  - en
+  zh:
+  - zh
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n>1K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- closed-domain-qa
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/UCSD-AI4H/Medical-Dialogue-System
+- **Repository:** Hosted on [this link](https://drive.google.com/drive/folders/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2) for Chinese and [this link](https://drive.google.com/drive/folders/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD) for English.
+- **Paper:** Details about the dataset can be found in [this arxiv papaer](https://arxiv.org/abs/2004.03329) 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+The MedDialog dataset (Chinese) contains conversations (in Chinese) between doctors and patients. It has 1.1 million dialogues and 4 million utterances. The data is continuously growing and more dialogues will be added. The raw dialogues are from haodf.com. All copyrights of the data belong to haodf.com.
+
+The MedDialog dataset (English) contains conversations (in English) between doctors and patients. It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.
+
+Directions for using the pre-trained model using BERT using PyTorch is available in the Homepage.
+
+
+### Supported Tasks and Leaderboards
+
+Closed domain qa
+
+### Languages
+
+Monolingual. The datasets are in English (EN) and Chinese (ZH)
+
+## Dataset Structure
+
+### Data Instances
+#### For English:
+
+Each consultation consists of the below:
+- ID
+- URL
+- Description of patient’s medical condition
+- Dialogue
+
+The dataset is built from [icliniq.com](https://www.icliniq.com/), [healthcaremagic.com](https://www.healthcaremagic.com/), [healthtap.com](https://www.healthtap.com/) and all copyrights of the data belong to these websites.
+
+#### For Chinese:
+
+Each consultation consists of the below:
+- ID
+- URL
+- Description of patient’s medical condition
+- Dialogue
+- (Optional) Diagnosis and suggestions.
+
+The dataset is built from [Haodf.com](https://www.haodf.com/) and all copyrights of the data belong to [Haodf.com](https://www.haodf.com/).
+
+One example for chinese is
+
+```
+{
+{'dialogue_id': 2,
+  'dialogue_turns': [{'speaker': '病人',
+    'utterance': '孩子哭闹时，鸡鸡旁边会肿起，情绪平静时肿块会消失，去一个私人诊所看过，说是疝气.如果确定是疝气，是不是一定要手术治疗？我孩子只有1岁10月，自愈的可能性大吗？如果一定要手术，这么小的孩子风险大吗？术后的恢复困难吗？谢谢.'},
+   {'speaker': '医生', 'utterance': '南方医的B超说得不清楚，可能是鞘膜积液，可到我医院复查一个B超。'}],
+  'dialogue_url': 'https://www.haodf.com/doctorteam/flow_team_6477251152.htm',
+  'file_name': '2020.txt'},
+}
+```
+
+
+### Data Fields
+
+For generating the QA only the below fields have been considered:
+- ID : Consultatation Identifier (restarts for each file)
+- URL: The url link of the extracted conversation
+- Dialogue : The conversation between the doctor and the patient.
+
+These are arranged as below in the prepared dataset. Each item will be represented with these parameters.
+
+- "file_name": string - signifies the file from which the conversation was extracted
+- "dialogue_id": int32 - the dialogue id
+- "dialogue_url": string - url of the conversation
+- "dialogue_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english)
+
+
+### Data Splits
+
+There are no data splits on the original data
+
+## Dataset Creation
+
+### Curation Rationale
+
+Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+@article{chen2020meddiag,
+  title={MedDialog: a large-scale medical dialogue dataset},
+  author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
+  journal={arXiv preprint arXiv:2004.03329}, 
+  year={2020}
+}

dataset_infos.json

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+{"en": {"description": "The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com.\nAll copyrights of the data belong to healthcaremagic.com and icliniq.com.\n", "citation": "@article{chen2020meddiag,\n  title={MedDialog: a large-scale medical dialogue dataset},\n  author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},\n  journal={arXiv preprint arXiv:2004.03329},\n  year={2020}\n}\n", "homepage": "https://github.com/UCSD-AI4H/Medical-Dialogue-System", "license": "", "features": {"file_name": {"dtype": "string", "id": null, "_type": "Value"}, "dialogue_id": {"dtype": "int32", "id": null, "_type": "Value"}, "dialogue_url": {"dtype": "string", "id": null, "_type": "Value"}, "dialogue_turns": {"feature": {"speaker": {"num_classes": 2, "names": ["Patient", "Doctor"], "names_file": null, "id": null, "_type": "ClassLabel"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "medical_dialog", "config_name": "en", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 0, "num_examples": 0, "dataset_name": "medical_dialog"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 0, "size_in_bytes": 0}, "zh": {"description": "The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com.\nAll copyrights of the data belong to healthcaremagic.com and icliniq.com.\n", "citation": "@article{chen2020meddiag,\n  title={MedDialog: a large-scale medical dialogue dataset},\n  author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},\n  journal={arXiv preprint arXiv:2004.03329},\n  year={2020}\n}\n", "homepage": "https://github.com/UCSD-AI4H/Medical-Dialogue-System", "license": "", "features": {"file_name": {"dtype": "string", "id": null, "_type": "Value"}, "dialogue_id": {"dtype": "int32", "id": null, "_type": "Value"}, "dialogue_url": {"dtype": "string", "id": null, "_type": "Value"}, "dialogue_turns": {"feature": {"speaker": {"num_classes": 2, "names": ["\u75c5\u4eba", "\u533b\u751f"], "names_file": null, "id": null, "_type": "ClassLabel"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "medical_dialog", "config_name": "zh", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 0, "num_examples": 0, "dataset_name": "medical_dialog"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 0, "size_in_bytes": 0}}

dummy/en/1.0.0/dummy_data.zip

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dummy/zh/1.0.0/dummy_data.zip

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medical_dialog.py

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+# 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.
+"""Medical Dialog dataset in english and chinese"""
+
+from __future__ import absolute_import, division, print_function
+
+import copy
+import os
+import re
+
+import datasets
+
+
+_CITATION = """\
+@article{chen2020meddiag,
+  title={MedDialog: a large-scale medical dialogue dataset},
+  author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
+  journal={arXiv preprint arXiv:2004.03329},
+  year={2020}
+}
+"""
+
+
+_DESCRIPTION = """\
+The MedDialog dataset (English) contains conversations (in English) between doctors and patients.\
+It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \
+The raw dialogues are from healthcaremagic.com and icliniq.com.\
+
+All copyrights of the data belong to healthcaremagic.com and icliniq.com.
+"""
+
+_HOMEPAGE = "https://github.com/UCSD-AI4H/Medical-Dialogue-System"
+
+_LICENSE = ""
+
+
+class MedicalDialog(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="en", description="The dataset of medical dialogs in English.", version=VERSION),
+        datasets.BuilderConfig(name="zh", description="The dataset of medical dialogs in Chinese.", version=VERSION),
+    ]
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+    \n  For English:\nYou need to go to https://drive.google.com/drive/folders/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\
+    and manually download the dataset from Google Drive. Once it is completed,
+    a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder(
+    or whichever folder your browser chooses to save files to). Unzip the folder to obtain
+    a folder named "Medical-Dialogue-Dataset-English" several text files.
+
+    Now, you can specify the path to this folder for the data_dir argument in the
+    datasets.load_dataset(...) option.
+    The <path/to/folder> can e.g. be "/Downloads/Medical-Dialogue-Dataset-English".
+    The data can then be loaded using the below command:\
+         `datasets.load_dataset("medical_dialog", name="en", data_dir="/Downloads/Medical-Dialogue-Dataset-English")`.
+
+    \n  For Chinese:\nFollow the above process. Change the 'name' to 'zh'.The download link is https://drive.google.com/drive/folders/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2
+
+    **NOTE**
+    - A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times.
+    - After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path.
+    """
+
+    def _info(self):
+        if self.config.name == "zh":
+            features = datasets.Features(
+                {
+                    "file_name": datasets.Value("string"),
+                    "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":
+            features = datasets.Features(
+                {
+                    "file_name": datasets.Value("string"),
+                    "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(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        if not os.path.exists(path_to_manual_file):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {})".format(
+                    path_to_manual_file, self.manual_download_instructions
+                )
+            )
+
+        filepaths = [
+            os.path.join(path_to_manual_file, txt_file_name)
+            for txt_file_name in sorted(os.listdir(path_to_manual_file))
+            if txt_file_name.endswith("txt")
+        ]
+
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": filepaths})]
+
+    def _generate_examples(self, filepaths):
+        """Yields examples. Iterates over each file and give the creates the corresponding features.
+
+        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
+        for filepath in filepaths:
+            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 = f_in.readline().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["file_name"] = filepath
+                                        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