Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+languages:
+- de
+licenses:
+- cc-by-nc-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for One Million Posts Corpus
+
+## 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://ofai.github.io/million-post-corpus/
+- **Repository:** https://github.com/OFAI/million-post-corpus
+- **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).
+
+DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, there is a discussion section below each news article where readers engage in online discussions. The data set contains a selection of user posts from the 12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and 1,000,000 unlabeled posts in the data set. The labeled posts were annotated by professional forum moderators employed by the newspaper.
+
+The data set contains the following data for each post:
+
+* Post ID
+* Article ID
+* Headline (max. 250 characters)
+* Main Body (max. 750 characters)
+* User ID (the user names used by the website have been re-mapped to new numeric IDs)
+* Time stamp
+* Parent post (replies give rise to tree-like discussion thread structures)
+* Status (online or deleted by a moderator)
+* Number of positive votes by other community members
+* Number of negative votes by other community members
+
+For each article, the data set contains the following data:
+
+* Article ID
+* Publishing date
+* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
+* Title
+* Body
+
+Detailed descriptions of the post selection and annotation procedures are given in the paper.
+
+#### Annotated Categories
+
+Potentially undesirable content:
+
+* Sentiment (negative/neutral/positive)
+    An important goal is to detect changes in the prevalent sentiment in a discussion, e.g., the location within the fora and the point in time where a turn from positive/neutral sentiment to negative sentiment takes place.
+* Off-Topic (yes/no)
+    Posts which digress too far from the topic of the corresponding article.
+* Inappropriate (yes/no)
+    Swearwords, suggestive and obscene language, insults, threats etc.
+* Discriminating (yes/no)
+    Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.
+
+Neutral content that requires a reaction:
+
+* Feedback (yes/no)
+    Sometimes users ask questions or give feedback to the author of the article or the newspaper in general, which may require a reply/reaction.
+
+Potentially desirable content:
+
+* Personal Stories (yes/no)
+    In certain fora, users are encouraged to share their personal stories, experiences, anecdotes etc. regarding the respective topic.
+* Arguments Used (yes/no)
+    It is desirable for users to back their statements with rational argumentation, reasoning and sources.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Austrian German
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+The data set contains the following data for each post:
+
+* **ID_Post**: Post ID
+* **ID_Parent_Post**: Parent post (replies give rise to tree-like discussion thread structures)
+* **ID_Article**: Article ID
+* **ID_User**: User ID (the user names used by the website have been re-mapped to new numeric IDs)
+* **Headline**: Headline (max. 250 characters)
+* **Body**: Main Body (max. 750 characters)
+* **CreatedAt**: Time stamp
+* **Status**: Status (online or deleted by a moderator)
+* **PositiveVotes**: Number of positive votes by other community members
+* **NegativeVotes**: Number of negative votes by other community members
+
+Labeled posts also contain:
+
+* **Category**: The category of the annotation, one of: ArgumentsUsed, Discriminating, Inappropriate, OffTopic, PersonalStories, PossiblyFeedback, SentimentNegative, SentimentNeutral, SentimentPositive
+* **Value**: either 0 or 1, explicitly indicating whether or not the post has the specified category as a label (i.e. a category of `ArgumentsUsed` with value of `0` means that an annotator explicitly labeled that this post doesn't use arguments, as opposed to the mere absence of a positive label).
+* **Fold**: a number between [0-9] from a 10-fold split by the authors
+
+For each article, the data set contains the following data:
+
+* **ID_Article**: Article ID
+* **publishingDate**: Publishing date
+* **Path**: Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
+* **Title**: Title
+* **Body**: Body
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### 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
+
+This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
+
+### Citation Information
+
+```
+@InProceedings{Schabus2018,
+  author    = {Dietmar Schabus and Marcin Skowron},
+  title     = {Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website},
+  booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)},
+  year      = {2018},
+  address   = {Miyazaki, Japan},
+  month     = may,
+  pages     = {1602-1605},
+  abstract  = {This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.},
+  url       = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html},
+}
+```

dataset_infos.json

@@ -0,0 +1 @@
+{"posts_labeled": {"description": "The \u201cOne Million Posts\u201d corpus is an annotated data set consisting of\nuser comments posted to an Austrian newspaper website (in German language).\n\nDER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper\u2019s website,\nthere is a discussion section below each news article where readers engage in\nonline discussions. The data set contains a selection of user posts from the\n12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and\n1,000,000 unlabeled posts in the data set. The labeled posts were annotated by\nprofessional forum moderators employed by the newspaper.\n\nThe data set contains the following data for each post:\n\n* Post ID\n* Article ID\n* Headline (max. 250 characters)\n* Main Body (max. 750 characters)\n* User ID (the user names used by the website have been re-mapped to new numeric IDs)\n* Time stamp\n* Parent post (replies give rise to tree-like discussion thread structures)\n* Status (online or deleted by a moderator)\n* Number of positive votes by other community members\n* Number of negative votes by other community members\n\nFor each article, the data set contains the following data:\n\n* Article ID\n* Publishing date\n* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)\n* Title\n* Body\n\nDetailed descriptions of the post selection and annotation procedures are given in the paper.\n\n## Annotated Categories\n\nPotentially undesirable content:\n\n* Sentiment (negative/neutral/positive)\n    An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,\n    the location within the fora and the point in time where a turn from positive/neutral\n    sentiment to negative sentiment takes place.\n* Off-Topic (yes/no)\n    Posts which digress too far from the topic of the corresponding article.\n* Inappropriate (yes/no)\n    Swearwords, suggestive and obscene language, insults, threats etc.\n* Discriminating (yes/no)\n    Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.\n\nNeutral content that requires a reaction:\n\n* Feedback (yes/no)\n    Sometimes users ask questions or give feedback to the author of the article or the\n    newspaper in general, which may require a reply/reaction.\n\nPotentially desirable content:\n\n* Personal Stories (yes/no)\n    In certain fora, users are encouraged to share their personal stories, experiences,\n    anecdotes etc. regarding the respective topic.\n* Arguments Used (yes/no)\n    It is desirable for users to back their statements with rational argumentation,\n    reasoning and sources.\n", "citation": "@InProceedings{Schabus2017,\n  Author    = {Dietmar Schabus and Marcin Skowron and Martin Trapp},\n  Title     = {One Million Posts: A Data Set of German Online Discussions},\n  Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},\n  Pages     = {1241--1244},\n  Year      = {2017},\n  Address   = {Tokyo, Japan},\n  Doi       = {10.1145/3077136.3080711},\n  Month     = aug\n}\n", "homepage": "https://ofai.github.io/million-post-corpus/", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License", "features": {"ID_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Parent_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Article": {"dtype": "string", "id": null, "_type": "Value"}, "ID_User": {"dtype": "string", "id": null, "_type": "Value"}, "CreatedAt": {"dtype": "string", "id": null, "_type": "Value"}, "Status": {"dtype": "string", "id": null, "_type": "Value"}, "Headline": {"dtype": "string", "id": null, "_type": "Value"}, "Body": {"dtype": "string", "id": null, "_type": "Value"}, "PositiveVotes": {"dtype": "int32", "id": null, "_type": "Value"}, "NegativeVotes": {"dtype": "int32", "id": null, "_type": "Value"}, "Category": {"num_classes": 9, "names": ["ArgumentsUsed", "Discriminating", "Inappropriate", "OffTopic", "PersonalStories", "PossiblyFeedback", "SentimentNegative", "SentimentNeutral", "SentimentPositive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "Value": {"dtype": "int32", "id": null, "_type": "Value"}, "Fold": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "omp", "config_name": "posts_labeled", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 13955964, "num_examples": 40567, "dataset_name": "omp"}}, "download_checksums": {"https://github.com/aseifert/million-post-corpus/raw/master/data/posts_labeled.csv.xz": {"num_bytes": 1329892, "checksum": "2d1cb6cd8fec07c5d378f9be889b355c1eb23b30e8fde2dcbb073cdad6f472ad"}}, "download_size": 1329892, "post_processing_size": null, "dataset_size": 13955964, "size_in_bytes": 15285856}, "posts_unlabeled": {"description": "The \u201cOne Million Posts\u201d corpus is an annotated data set consisting of\nuser comments posted to an Austrian newspaper website (in German language).\n\nDER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper\u2019s website,\nthere is a discussion section below each news article where readers engage in\nonline discussions. The data set contains a selection of user posts from the\n12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and\n1,000,000 unlabeled posts in the data set. The labeled posts were annotated by\nprofessional forum moderators employed by the newspaper.\n\nThe data set contains the following data for each post:\n\n* Post ID\n* Article ID\n* Headline (max. 250 characters)\n* Main Body (max. 750 characters)\n* User ID (the user names used by the website have been re-mapped to new numeric IDs)\n* Time stamp\n* Parent post (replies give rise to tree-like discussion thread structures)\n* Status (online or deleted by a moderator)\n* Number of positive votes by other community members\n* Number of negative votes by other community members\n\nFor each article, the data set contains the following data:\n\n* Article ID\n* Publishing date\n* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)\n* Title\n* Body\n\nDetailed descriptions of the post selection and annotation procedures are given in the paper.\n\n## Annotated Categories\n\nPotentially undesirable content:\n\n* Sentiment (negative/neutral/positive)\n    An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,\n    the location within the fora and the point in time where a turn from positive/neutral\n    sentiment to negative sentiment takes place.\n* Off-Topic (yes/no)\n    Posts which digress too far from the topic of the corresponding article.\n* Inappropriate (yes/no)\n    Swearwords, suggestive and obscene language, insults, threats etc.\n* Discriminating (yes/no)\n    Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.\n\nNeutral content that requires a reaction:\n\n* Feedback (yes/no)\n    Sometimes users ask questions or give feedback to the author of the article or the\n    newspaper in general, which may require a reply/reaction.\n\nPotentially desirable content:\n\n* Personal Stories (yes/no)\n    In certain fora, users are encouraged to share their personal stories, experiences,\n    anecdotes etc. regarding the respective topic.\n* Arguments Used (yes/no)\n    It is desirable for users to back their statements with rational argumentation,\n    reasoning and sources.\n", "citation": "@InProceedings{Schabus2017,\n  Author    = {Dietmar Schabus and Marcin Skowron and Martin Trapp},\n  Title     = {One Million Posts: A Data Set of German Online Discussions},\n  Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},\n  Pages     = {1241--1244},\n  Year      = {2017},\n  Address   = {Tokyo, Japan},\n  Doi       = {10.1145/3077136.3080711},\n  Month     = aug\n}\n", "homepage": "https://ofai.github.io/million-post-corpus/", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License", "features": {"ID_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Parent_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Article": {"dtype": "string", "id": null, "_type": "Value"}, "ID_User": {"dtype": "string", "id": null, "_type": "Value"}, "CreatedAt": {"dtype": "string", "id": null, "_type": "Value"}, "Status": {"dtype": "string", "id": null, "_type": "Value"}, "Headline": {"dtype": "string", "id": null, "_type": "Value"}, "Body": {"dtype": "string", "id": null, "_type": "Value"}, "PositiveVotes": {"dtype": "int32", "id": null, "_type": "Value"}, "NegativeVotes": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "omp", "config_name": "posts_unlabeled", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 305770324, "num_examples": 1000000, "dataset_name": "omp"}}, "download_checksums": {"https://github.com/aseifert/million-post-corpus/raw/master/data/posts_unlabeled.csv.xz": {"num_bytes": 79296188, "checksum": "433e80787abf587ecbd54756b3b200b57b7ef31041f19ba0bb8d2c5cc39cad65"}}, "download_size": 79296188, "post_processing_size": null, "dataset_size": 305770324, "size_in_bytes": 385066512}, "articles": {"description": "The \u201cOne Million Posts\u201d corpus is an annotated data set consisting of\nuser comments posted to an Austrian newspaper website (in German language).\n\nDER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper\u2019s website,\nthere is a discussion section below each news article where readers engage in\nonline discussions. The data set contains a selection of user posts from the\n12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and\n1,000,000 unlabeled posts in the data set. The labeled posts were annotated by\nprofessional forum moderators employed by the newspaper.\n\nThe data set contains the following data for each post:\n\n* Post ID\n* Article ID\n* Headline (max. 250 characters)\n* Main Body (max. 750 characters)\n* User ID (the user names used by the website have been re-mapped to new numeric IDs)\n* Time stamp\n* Parent post (replies give rise to tree-like discussion thread structures)\n* Status (online or deleted by a moderator)\n* Number of positive votes by other community members\n* Number of negative votes by other community members\n\nFor each article, the data set contains the following data:\n\n* Article ID\n* Publishing date\n* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)\n* Title\n* Body\n\nDetailed descriptions of the post selection and annotation procedures are given in the paper.\n\n## Annotated Categories\n\nPotentially undesirable content:\n\n* Sentiment (negative/neutral/positive)\n    An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,\n    the location within the fora and the point in time where a turn from positive/neutral\n    sentiment to negative sentiment takes place.\n* Off-Topic (yes/no)\n    Posts which digress too far from the topic of the corresponding article.\n* Inappropriate (yes/no)\n    Swearwords, suggestive and obscene language, insults, threats etc.\n* Discriminating (yes/no)\n    Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.\n\nNeutral content that requires a reaction:\n\n* Feedback (yes/no)\n    Sometimes users ask questions or give feedback to the author of the article or the\n    newspaper in general, which may require a reply/reaction.\n\nPotentially desirable content:\n\n* Personal Stories (yes/no)\n    In certain fora, users are encouraged to share their personal stories, experiences,\n    anecdotes etc. regarding the respective topic.\n* Arguments Used (yes/no)\n    It is desirable for users to back their statements with rational argumentation,\n    reasoning and sources.\n", "citation": "@InProceedings{Schabus2017,\n  Author    = {Dietmar Schabus and Marcin Skowron and Martin Trapp},\n  Title     = {One Million Posts: A Data Set of German Online Discussions},\n  Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},\n  Pages     = {1241--1244},\n  Year      = {2017},\n  Address   = {Tokyo, Japan},\n  Doi       = {10.1145/3077136.3080711},\n  Month     = aug\n}\n", "homepage": "https://ofai.github.io/million-post-corpus/", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License", "features": {"ID_Article": {"dtype": "string", "id": null, "_type": "Value"}, "Path": {"dtype": "string", "id": null, "_type": "Value"}, "publishingDate": {"dtype": "string", "id": null, "_type": "Value"}, "Title": {"dtype": "string", "id": null, "_type": "Value"}, "Body": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "omp", "config_name": "articles", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43529400, "num_examples": 12087, "dataset_name": "omp"}}, "download_checksums": {"https://github.com/aseifert/million-post-corpus/raw/master/data/articles.csv.xz": {"num_bytes": 10681288, "checksum": "ff707a8adddd0f8785c7668b051d01b69cfac696db89afbd46054656f909a479"}}, "download_size": 10681288, "post_processing_size": null, "dataset_size": 43529400, "size_in_bytes": 54210688}}

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omp.py

@@ -0,0 +1,269 @@
+# 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.
+"""The “One Million Posts” corpus is an annotated data set consisting of
+user comments posted to an Austrian newspaper website (in German language)."""
+
+from __future__ import absolute_import, division, print_function
+
+from pathlib import Path
+
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{Schabus2017,
+  Author    = {Dietmar Schabus and Marcin Skowron and Martin Trapp},
+  Title     = {One Million Posts: A Data Set of German Online Discussions},
+  Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},
+  Pages     = {1241--1244},
+  Year      = {2017},
+  Address   = {Tokyo, Japan},
+  Doi       = {10.1145/3077136.3080711},
+  Month     = aug
+}
+"""
+
+_DESCRIPTION = """\
+The “One Million Posts” corpus is an annotated data set consisting of
+user comments posted to an Austrian newspaper website (in German language).
+
+DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website,
+there is a discussion section below each news article where readers engage in
+online discussions. The data set contains a selection of user posts from the
+12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and
+1,000,000 unlabeled posts in the data set. The labeled posts were annotated by
+professional forum moderators employed by the newspaper.
+
+The data set contains the following data for each post:
+
+* Post ID
+* Article ID
+* Headline (max. 250 characters)
+* Main Body (max. 750 characters)
+* User ID (the user names used by the website have been re-mapped to new numeric IDs)
+* Time stamp
+* Parent post (replies give rise to tree-like discussion thread structures)
+* Status (online or deleted by a moderator)
+* Number of positive votes by other community members
+* Number of negative votes by other community members
+
+For each article, the data set contains the following data:
+
+* Article ID
+* Publishing date
+* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
+* Title
+* Body
+
+Detailed descriptions of the post selection and annotation procedures are given in the paper.
+
+## Annotated Categories
+
+Potentially undesirable content:
+
+* Sentiment (negative/neutral/positive)
+    An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,
+    the location within the fora and the point in time where a turn from positive/neutral
+    sentiment to negative sentiment takes place.
+* Off-Topic (yes/no)
+    Posts which digress too far from the topic of the corresponding article.
+* Inappropriate (yes/no)
+    Swearwords, suggestive and obscene language, insults, threats etc.
+* Discriminating (yes/no)
+    Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.
+
+Neutral content that requires a reaction:
+
+* Feedback (yes/no)
+    Sometimes users ask questions or give feedback to the author of the article or the
+    newspaper in general, which may require a reply/reaction.
+
+Potentially desirable content:
+
+* Personal Stories (yes/no)
+    In certain fora, users are encouraged to share their personal stories, experiences,
+    anecdotes etc. regarding the respective topic.
+* Arguments Used (yes/no)
+    It is desirable for users to back their statements with rational argumentation,
+    reasoning and sources.
+"""
+
+_HOMEPAGE = "https://ofai.github.io/million-post-corpus/"
+
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
+
+_URLs = {
+    "posts_labeled": "https://github.com/aseifert/million-post-corpus/raw/master/data/posts_labeled.csv.xz",
+    "posts_unlabeled": "https://github.com/aseifert/million-post-corpus/raw/master/data/posts_unlabeled.csv.xz",
+    "articles": "https://github.com/aseifert/million-post-corpus/raw/master/data/articles.csv.xz",
+}
+
+
+class Omp(datasets.GeneratorBasedBuilder):
+    """The “One Million Posts” corpus is an annotated data set consisting of user comments
+    posted to an Austrian newspaper website (in German language). Annotated categories include:
+    sentiment (negative/neutral/positive), off-topic (yes/no), inappropriate (yes/no),
+    discriminating (yes/no), feedback (yes/no), personal story (yes/no), arguments used (yes/no)."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="posts_labeled",
+            version=VERSION,
+            description="This part of the dataset includes labeled posts (11,773 annotated posts)",
+        ),
+        datasets.BuilderConfig(
+            name="posts_unlabeled",
+            version=VERSION,
+            description="This part of the dataset includes unlabeled posts (1,000,000)",
+        ),
+        datasets.BuilderConfig(
+            name="articles",
+            version=VERSION,
+            description="This part of the dataset includes the articles that the comments were posted to (~12k)",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = (
+        "posts_labeled"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
+
+    def _info(self):
+        if self.config.name == "posts_labeled":
+            features = datasets.Features(
+                {
+                    "ID_Post": datasets.Value("string"),
+                    "ID_Parent_Post": datasets.Value("string"),
+                    "ID_Article": datasets.Value("string"),
+                    "ID_User": datasets.Value("string"),
+                    "CreatedAt": datasets.Value("string"),
+                    "Status": datasets.Value("string"),
+                    "Headline": datasets.Value("string"),
+                    "Body": datasets.Value("string"),
+                    "PositiveVotes": datasets.Value("int32"),
+                    "NegativeVotes": datasets.Value("int32"),
+                    "Category": datasets.features.ClassLabel(
+                        names=[
+                            "ArgumentsUsed",
+                            "Discriminating",
+                            "Inappropriate",
+                            "OffTopic",
+                            "PersonalStories",
+                            "PossiblyFeedback",
+                            "SentimentNegative",
+                            "SentimentNeutral",
+                            "SentimentPositive",
+                        ]
+                    ),
+                    "Value": datasets.Value("int32"),
+                    "Fold": datasets.Value("int32"),
+                }
+            )
+        elif self.config.name == "posts_unlabeled":
+            features = datasets.Features(
+                {
+                    "ID_Post": datasets.Value("string"),
+                    "ID_Parent_Post": datasets.Value("string"),
+                    "ID_Article": datasets.Value("string"),
+                    "ID_User": datasets.Value("string"),
+                    "CreatedAt": datasets.Value("string"),
+                    "Status": datasets.Value("string"),
+                    "Headline": datasets.Value("string"),
+                    "Body": datasets.Value("string"),
+                    "PositiveVotes": datasets.Value("int32"),
+                    "NegativeVotes": datasets.Value("int32"),
+                }
+            )
+        elif self.config.name == "articles":
+            features = datasets.Features(
+                {
+                    "ID_Article": datasets.Value("string"),
+                    "Path": datasets.Value("string"),
+                    "publishingDate": datasets.Value("string"),
+                    "Title": datasets.Value("string"),
+                    "Body": datasets.Value("string"),
+                }
+            )
+        else:
+            assert False
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            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."""
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        my_urls = _URLs[self.config.name]
+        data_path = Path(dl_manager.download_and_extract(my_urls))
+        if data_path.is_dir():
+            if self.config.name == "posts_labeled":
+                fname = "posts_labeled.csv.gz"
+            elif self.config.name == "posts_unlabeled":
+                fname = "posts_unlabeled.csv.gz"
+            elif self.config.name == "articles":
+                fname = "articles.csv.gz"
+            else:
+                assert False
+            data_path = data_path / fname
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": str(data_path), "split": "train"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        if self.config.name in ["posts_labeled", "posts_unlabeled"]:
+            posts_labeled = pd.read_csv(
+                filepath,
+                compression=None,
+                dtype={"ID_Post": str, "ID_Parent_Post": str, "ID_Article": str, "ID_User": str},
+            )
+            posts_labeled.fillna("", inplace=True)
+            for i, row in posts_labeled.iterrows():
+                yield row["ID_Post"], row.to_dict()
+        elif self.config.name == "articles":
+            posts_labeled = pd.read_csv(
+                filepath,
+                compression=None,
+                dtype={"ID_Article": str, "Path": str, "publishingDate": str, "ID_User": str},
+            )
+            posts_labeled.fillna("", inplace=True)
+            for i, row in posts_labeled.iterrows():
+                yield row["ID_Article"], row.to_dict()