Add configs with processed data to medical_dialog dataset (#4127)

* Add configs with processed data in medical_dialog dataset

* Update metadata JSON

* Update dataset card

* Rename dummy data dirs

* Fix script

Commit from https://github.com/huggingface/datasets/commit/eab78694e17f10c200bceb60c6f21a2f70eadf68

README.md

@@ -14,7 +14,7 @@ licenses:
 multilinguality:
 - monolingual
 size_categories:
-- n<1K
+- 1M<n<10M
 source_datasets:
 - original
 task_categories:
@@ -25,7 +25,7 @@ paperswithcode_id: null
 pretty_name: MedDialog
 ---
 
-# Dataset Card for [Dataset Name]
+# Dataset Card for MedDialog
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -53,11 +53,11 @@ pretty_name: MedDialog
 
 ## 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:** 
+[//]: # (- **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:** )
 
 ### Dataset Summary
 
@@ -79,7 +79,16 @@ Monolingual. The datasets are in English (EN) and Chinese (ZH)
 ## Dataset Structure
 
 ### Data Instances
-#### For English:
+
+There are 4 configurations:
+- Raw data:
+  - en
+  - zh
+- Processed data:
+  - processed.en
+  - processed.zh
+
+#### en
 
 Each consultation consists of the below:
 - ID
@@ -89,7 +98,7 @@ Each consultation consists of the below:
 
 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:
+#### zh
 
 Each consultation consists of the below:
 - ID
@@ -113,6 +122,26 @@ One example for chinese is
 }
 ```
 
+#### processed.en
+```
+{
+  'description': 'throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.',
+  'utterances': [
+    'patient: throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.',
+    "doctor: during this pandemic. throat pain can be from a strep throat infection (antibiotics needed), a cold or influenza or other virus, or from some other cause such as allergies or irritants. usually, a person sees the doctor (call first) if the sore throat is bothersome, recurrent, or doesn't go away quickly. covid-19 infections tend to have cough, whereas strep throat usually lacks cough but has more throat pain. (3/21/20)"
+  ]
+}
+```
+
+#### processed.zh
+```
+{
+  'utterances': [
+    '病人：强制性脊柱炎，晚上睡觉翻身时腰骶骨区域疼痛，其他身体任何部位均不疼痛。', 
+    '医生：应该没有问题，但最好把图像上传看看。'
+  ]
+}
+```
 
 ### Data Fields
 
@@ -128,13 +157,26 @@ These are arranged as below in the prepared dataset. Each item will be represent
 - "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)
 
+#### processed.en
+- `description` (str): Description of the dialog.
+- `utterances` (list of str): Dialog utterances between patient and doctor.
+
+#### processed.zh
+- `utterances` (list of str): Dialog utterances between patient and doctor.
 
 ### Data Splits
 
-There are no data splits on the original data. The "train" split for each language contains:
+There are no data splits on the original raw data. The "train" split for each language contains:
 - en: 229674 examples
 - zh: 1921127 examples
 
+For processed configurations, data is split into train, validation and test, with the following number of examples:
+
+|              |   train | validation |   test |
+|--------------|--------:|-----------:|-------:|
+| processed.en |     482 |         60 |     61 |
+| processed.zh | 2725989 |     340748 | 340754 |
+
 ## Dataset Creation
 
 ### Curation Rationale
@@ -187,15 +229,17 @@ Medical dialogue systems are promising in assisting in telemedicine to increase
 
 ### Licensing Information
 
-[More Information Needed]
+Unknow.
 
 ### 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}
 }
+```
 
 ### Contributions
 

dataset_infos.json

@@ -1 +1 @@
-{"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, "task_templates": 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": 290274759, "num_examples": 229674, "dataset_name": "medical_dialog"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 290274759, "size_in_bytes": 290274759}, "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, "task_templates": 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": 1092063621, "num_examples": 1921127, "dataset_name": "medical_dialog"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 1092063621, "size_in_bytes": 1092063621}}
+{"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"], "id": null, "_type": "ClassLabel"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": 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": 290274759, "num_examples": 229674, "dataset_name": "medical_dialog"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 290274759, "size_in_bytes": 290274759}, "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"], "id": null, "_type": "ClassLabel"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": 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": 1092063621, "num_examples": 1921127, "dataset_name": "medical_dialog"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 1092063621, "size_in_bytes": 1092063621}, "processed.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": "Copyright", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "utterances": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "medical_dialog", "config_name": "processed.en", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 370745, "num_examples": 482, "dataset_name": "medical_dialog"}, "validation": {"name": "validation", "num_bytes": 52145, "num_examples": 60, "dataset_name": "medical_dialog"}, "test": {"name": "test", "num_bytes": 46514, "num_examples": 61, "dataset_name": "medical_dialog"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1ria4E6IdTIPsikL4Glm3uy1tFKJKw0W8": {"num_bytes": 414490, "checksum": "568a9c6c670502eec3319c78e9d12c0aebb883c0d1e45095b5dd5f99d8b6b874"}, "https://drive.google.com/uc?export=download&id=1KAZneuwdfEVQQM6euCX4pMDP-9DQpiB5": {"num_bytes": 57706, "checksum": "a5cd29f17fcfedf01af41410e12e47474ba1176f376136e18fc0446b7e2f52b2"}, "https://drive.google.com/uc?export=download&id=10izqL71kcgnteYsf87Vh6j_mZ8sZM2Rc": {"num_bytes": 52018, "checksum": "316e5b3eb03ec7210b0d84414df0e84a42b396205d72a2b5fdba533fd19a5ebd"}}, "download_size": 524214, "post_processing_size": null, "dataset_size": 469404, "size_in_bytes": 993618}, "processed.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": "Copyright", "features": {"utterances": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "medical_dialog", "config_name": "processed.zh", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1571262099, "num_examples": 2725989, "dataset_name": "medical_dialog"}, "validation": {"name": "validation", "num_bytes": 197117565, "num_examples": 340748, "dataset_name": "medical_dialog"}, "test": {"name": "test", "num_bytes": 196526738, "num_examples": 340754, "dataset_name": "medical_dialog"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1AaDJoHaiHAwEZwtskRH8oL1UP4FRgmgx": {"num_bytes": 1665206303, "checksum": "fd34385487755d95783cf834921bff14ceb74d9a244962577140c9e291dce4e9"}, "https://drive.google.com/uc?export=download&id=1TvfZCmQqP1kURIfEinOcj5VOPelTuGwI": {"num_bytes": 208871784, "checksum": "ed6b04ff4d62a4fa5b5b85327d692302b3369c0d28e9da887c12ec78ea778ce4"}, "https://drive.google.com/uc?export=download&id=1pmmG95Yl6mMXRXDDSRb9-bYTxOE7ank5": {"num_bytes": 208276068, "checksum": "b1118b614f866089a1daf18107a72dd5ba77c50a1e9ca145491ddcef89d797b7"}}, "download_size": 2082354155, "post_processing_size": null, "dataset_size": 1964906402, "size_in_bytes": 4047260557}}

medical_dialog.py

@@ -15,6 +15,7 @@
 
 
 import copy
+import json
 import os
 import re
 
@@ -41,20 +42,48 @@ All copyrights of the data belong to healthcaremagic.com and icliniq.com.
 
 _HOMEPAGE = "https://github.com/UCSD-AI4H/Medical-Dialogue-System"
 
-_LICENSE = ""
+_LICENSE = "Unknown"
+
+# URLS of processed data
+_URLS = {
+    "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",
+    },
+    "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",
+    },
+}
 
 
 class MedicalDialog(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
+    VERSION = datasets.Version("2.0.0")
 
     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),
+        datasets.BuilderConfig(
+            name="en", description="The raw dataset of medical dialogs in English.", version=VERSION
+        ),
+        datasets.BuilderConfig(
+            name="zh", description="The raw dataset of medical dialogs in Chinese.", version=VERSION
+        ),
+        datasets.BuilderConfig(
+            name="processed.en", description="The processed dataset of medical dialogs in English.", version=VERSION
+        ),
+        datasets.BuilderConfig(
+            name="processed.zh", description="The processed dataset of medical dialogs in Chinese.", version=VERSION
+        ),
     ]
 
     @property
     def manual_download_instructions(self):
-        return """\
+        *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(
@@ -73,6 +102,7 @@ class MedicalDialog(datasets.GeneratorBasedBuilder):
     - 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":
@@ -89,8 +119,7 @@ class MedicalDialog(datasets.GeneratorBasedBuilder):
                     ),
                 }
             )
-
-        if self.config.name == "en":
+        elif self.config.name == "en":
             features = datasets.Features(
                 {
                     "file_name": datasets.Value("string"),
@@ -104,35 +133,48 @@ class MedicalDialog(datasets.GeneratorBasedBuilder):
                     ),
                 }
             )
-
+        elif self.config.name == "processed.en":
+            features = datasets.Features(
+                {
+                    "description": datasets.Value("string"),
+                    "utterances": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        elif self.config.name == "processed.zh":
+            features = datasets.Features(
+                {
+                    "utterances": datasets.Sequence(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(
-                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})]
+        *processed, lang = self.config.name.split(".")
+        if processed:
+            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": data_dir[split]}) for split in splits]
+        else:
+            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.
@@ -141,132 +183,152 @@ class MedicalDialog(datasets.GeneratorBasedBuilder):
         - 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
+        *processed, data_lang = self.config.name.split(".")
+        if processed:
+            with open(filepaths, encoding="utf-8") as f:
+                if self.config.name == "processed.en":
+                    data = json.load(f)
+                    for idx, item in enumerate(data):
+                        yield idx, item
+                elif self.config.name == "processed.zh":
+                    idx = 0
+                    array = ""
+                    for line in f:
+                        if line[0] not in ["[", "]"]:
+                            if line != "    ],\n":
+                                array += line
                             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"
-
+                                array += "]"
+                                item = json.loads(array)
+                                yield idx, {"utterances": item}
+                                idx += 1
+                                array = ""
+        else:
+            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"]):
-                                    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()
-                                        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()
-                                        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