Revert data file hosting (#6)

- Revert data file hosting (0c554b7629808c0c20312926b00190ea7e98f128)
- Revert loading script update (122aea4a14bbcd913a6968b4b90a35ef675c95ca)
- Add point of contact (c86903e9b085733f9f7865da7ee000fe63473624)
- Add licensing information (eee39e2fe3b6a88950c51b9700285d0047351e4c)

README.md

@@ -137,11 +137,9 @@ config_names:
 
 ## Dataset Description
 
-[//]: # (- **Homepage:** )
 - **Repository:** https://github.com/UCSD-AI4H/Medical-Dialogue-System
 - **Paper:** [MedDialog: Two Large-scale Medical Dialogue Datasets](https://arxiv.org/abs/2004.03329)
-[//]: # (- **Leaderboard:** )
-[//]: # (- **Point of Contact:** )
+- **Point of Contact:** [Pengtao Xie](mailto:[email protected])
 
 ### Dataset Summary
 
@@ -313,7 +311,18 @@ Medical dialogue systems are promising in assisting in telemedicine to increase
 
 ### Licensing Information
 
-Unknow.
+The authors claim that:
+- They scraped the data from the following websites:
+  - MedDialog-EN: data was crawled from https://www.icliniq.com/ and https://www.healthcaremagic.com/
+  - MedDialog-CN: data was crawled from https://www.haodf.com/
+- All copyrights of the data belong to the corresponding websites
+
+The [terms and conditions](https://www.icliniq.com/p/terms) (last updated on: 11th April 2022) of www.icliniq.com website state:
+> No person (including a User, Doctor, Alternative Medicine Practitioner, or Wellness Professional) shall copy, transfer, download, republish, sell, duplicate, or "scrape", for commercial or any other purpose whatsoever, the contents or information made available on the Platform including Directory Listing Services, academic articles, and queries, in whole or in part, in any medium whatsoever.
+
+The [terms and conditions](https://www.healthcaremagic.com/tc) (last updated: August 17, 2012) of www.healthcaremagic.com website stipulate:
+> You are prohibited from republishing, selling, duplicating, or "scraping" for commercial or any other purpose whatsoever any of the data or other information contained therein, in whole or in part, in any medium whatsoever.
+
 
 ### Citation Information
 ```

data/Medical-Dialogue-Dataset-Chinese.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2d7e5b8ab5c09ba2fd015b4363461d6a026ba994ef799666c5bc6e367438bb4d
-size 2406679418

data/Medical-Dialogue-Dataset-English.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:899635d6be9489602f432ea70d24be6a3c1ef1d6ccd22564f33946ee60f20f8c
-size 93916317

data/processed-chinese.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2d368320555045b773d24b4b3bf295c3c6b62a3b46d537d25484f3948c00cffe
-size 809796157

data/processed-english.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:12cd3161693c7e5e15d9875ea902ca7d7471db4f0eb4ffd77a662c3dc51517be
-size 139172

medical_dialog.py

@@ -46,21 +46,15 @@ _LICENSE = "Unknown"
 
 # URLS of processed data
 _URLS = {
-    "en": "data/Medical-Dialogue-Dataset-English.zip",
-    "zh": "data/Medical-Dialogue-Dataset-Chinese.zip",
-    "processed.en": "data/processed-english.zip",
-    "processed.zh": "data/processed-chinese.zip",
-}
-_FILENAMES = {
-    "processed.en": {
-        "train": "english-train.json",
-        "validation": "english-dev.json",
-        "test": "english-test.json",
+    "en": {
+        "train": "https://drive.google.com/uc?export=download&id=1ria4E6IdTIPsikL4Glm3uy1tFKJKw0W8",
+        "validation": "https://drive.google.com/uc?export=download&id=1KAZneuwdfEVQQM6euCX4pMDP-9DQpiB5",
+        "test": "https://drive.google.com/uc?export=download&id=10izqL71kcgnteYsf87Vh6j_mZ8sZM2Rc",
     },
-    "processed.zh": {
-        "train": "train_data.json",
-        "validation": "validate_data.json",
-        "test": "test_data.json",
+    "zh": {
+        "train": "https://drive.google.com/uc?export=download&id=1AaDJoHaiHAwEZwtskRH8oL1UP4FRgmgx",
+        "validation": "https://drive.google.com/uc?export=download&id=1TvfZCmQqP1kURIfEinOcj5VOPelTuGwI",
+        "test": "https://drive.google.com/uc?export=download&id=1pmmG95Yl6mMXRXDDSRb9-bYTxOE7ank5",
     },
 }
 
@@ -83,6 +77,33 @@ class MedicalDialog(datasets.GeneratorBasedBuilder):
         ),
     ]
 
+    @property
+    def manual_download_instructions(self):
+        *processed, _ = self.config.name.split(".")
+        return (
+            None
+            if processed
+            else """\
+    \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(
@@ -137,13 +158,23 @@ class MedicalDialog(datasets.GeneratorBasedBuilder):
         """Returns SplitGenerators."""
         *processed, lang = self.config.name.split(".")
         if processed:
-            # data_dir = dl_manager.download(_URLS[lang])
-            data_dir = dl_manager.download_and_extract(_URLS[self.config.name])
+            data_dir = dl_manager.download(_URLS[lang])
             splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
-            return [datasets.SplitGenerator(name=split, gen_kwargs={"filepaths": os.path.join(data_dir, _FILENAMES[self.config.name][split])}) for split in splits]
+            return [datasets.SplitGenerator(name=split, gen_kwargs={"filepaths": data_dir[split]}) for split in splits]
         else:
-            archive = dl_manager.download(_URLS[self.config.name])
-            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": dl_manager.iter_archive(archive)})]
+            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(
+                    f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {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.
@@ -174,130 +205,130 @@ class MedicalDialog(datasets.GeneratorBasedBuilder):
                                 array = ""
         else:
             id_ = -1
-            for filepath, f_in 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().decode("utf-8")
-                    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().decode("utf-8")
-                            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().decode("utf-8").rstrip()
-                                        des_flag = True
+            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 = []
 
-                                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"
+                    # 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
 
-                            if data_lang == "en":
-                                last_user = "Patient"
+                        # 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().decode("utf-8")
+                                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
+                                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":
-                                    if line[:3] == "病人：" or line[:3] == "医生：":  # Hardcode alert!
-                                        user = line[:2]  # Hardcode alert!
-                                        line = f_in.readline().decode("utf-8")
-                                        conv_flag = True
+                                    last_user = "病人"
 
-                                # 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().decode("utf-8")
-                                        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_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))
-                                        last_conv["utterance"] = sen
-                                        last_conv["speaker"] = user
+                                        # 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