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:
+- expert-generated
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-label-classification
+---
+
+# Dataset Card for Fake News English
+
+## 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://dl.acm.org/doi/10.1145/3201064.3201100** 
+- **Repository: https://github.com/jgolbeck/fakenews/**
+- **Paper: https://doi.org/10.1145/3201064.3201100**
+- **Leaderboard:**
+- **Point of Contact: Jennifer Golbeck (http://www.jengolbeck.com)**
+
+### Dataset Summary
+This dataset contains URLs of news articles classified as either fake or satire. The articles classified as fake also have the URL of a rebutting article.
+
+### Supported Tasks and Leaderboards
+[More Information Needed]
+
+### Languages
+English
+
+## Dataset Structure
+
+### Data Instances
+```
+{
+"article_number": 102 ,
+"url_of_article": https://newslo.com/roger-stone-blames-obama-possibility-trump-alzheimers-attacks-president-caused-severe-stress/ ,
+"fake_or_satire": 1,  # Fake
+"url_of_rebutting_article": https://www.snopes.com/fact-check/donald-trumps-intelligence-quotient/
+}
+```
+
+### Data Fields
+- article_number: An integer used as an index for each row
+- url_of_article: A string which contains URL of an article to be assessed and classified as either Fake or Satire
+- fake_or_satire: A classlabel for the above variable which can take two values- Fake (1) and Satire (0)
+- url_of_rebutting_article: A string which contains a URL of the article used to refute the article in question (present - in url_of_article)
+
+### Data Splits
+This dataset is not split, only the train split is available.
+
+## 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
+Golbeck, Jennifer
+Everett, Jennine 
+Falak, Waleed
+Gieringer, Carl
+Graney, Jack 
+Hoffman, Kelly 
+Huth, Lindsay 
+Ma, Zhenya 
+Jha, Mayanka 
+Khan, Misbah 
+Kori, Varsha 
+Mauriello, Matthew 
+Lewis, Elo 
+Mirano, George 
+IV, William 
+Mussenden, Sean 
+Nelson, Tammie 
+Mcwillie, Sean 
+Pant, Akshat 
+Cheakalos, Paul
+
+### Licensing Information
+[More Information Needed]
+
+### Citation Information
+@inproceedings{inproceedings,
+author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
+year = {2018},
+month = {05},
+pages = {17-21},
+title = {Fake News vs Satire: A Dataset and Analysis},
+doi = {10.1145/3201064.3201100}
+}

dataset_infos.json

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+{"default": {"description": "\nFake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term \"fake news\" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear definitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, verified, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or condemnation of a gure, conspiracy theories, racist themes, and discrediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classification purposes. Overall, our contribution of a dataset and initial analysis are designed to support future work by fake news researchers.\n", "citation": "\n@inproceedings{inproceedings,\nauthor = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},\nyear = {2018},\nmonth = {05},\npages = {17-21},\ntitle = {Fake News vs Satire: A Dataset and Analysis},\ndoi = {10.1145/3201064.3201100}\n}\n", "homepage": "https://dl.acm.org/doi/10.1145/3201064.3201100", "license": "", "features": {"article_number": {"dtype": "int32", "id": null, "_type": "Value"}, "url_of_article": {"dtype": "string", "id": null, "_type": "Value"}, "fake_or_satire": {"num_classes": 2, "names": ["Satire", "Fake"], "names_file": null, "id": null, "_type": "ClassLabel"}, "url_of_rebutting_article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "fake_news_english", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 78078, "num_examples": 492, "dataset_name": "fake_news_english"}}, "download_checksums": {"https://github.com/jgolbeck/fakenews/raw/master/FakeNewsData.zip": {"num_bytes": 3002233, "checksum": "f423317ec13c5279bc5879240dca034ee9eae773eaa9c42e1fca8f8f88e37627"}}, "download_size": 3002233, "post_processing_size": null, "dataset_size": 78078, "size_in_bytes": 3080311}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bee5f01ec97eca7eb0cc8e50f387f6158c78aa5533414a1996c55b32c47528e4
+size 6682

fake_news_english.py

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+# 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.
+"""Fake News vs Satire: A Dataset and Analysis."""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import openpyxl  # noqa: requires this pandas optional dependency for reading xlsx files
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """
+@inproceedings{inproceedings,
+author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
+year = {2018},
+month = {05},
+pages = {17-21},
+title = {Fake News vs Satire: A Dataset and Analysis},
+doi = {10.1145/3201064.3201100}
+}
+"""
+
+_DESCRIPTION = """
+Fake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term "fake news" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear definitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, verified, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or condemnation of a gure, conspiracy theories, racist themes, and discrediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classification purposes. Overall, our contribution of a dataset and initial analysis are designed to support future work by fake news researchers.
+"""
+
+_HOMEPAGE = "https://dl.acm.org/doi/10.1145/3201064.3201100"
+
+# _LICENSE = ""
+
+_URLs = "https://github.com/jgolbeck/fakenews/raw/master/FakeNewsData.zip"
+
+
+class FakeNewsEnglish(datasets.GeneratorBasedBuilder):
+    """Fake News vs Satire: A Dataset and Analysis"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "article_number": datasets.Value("int32"),
+                "url_of_article": datasets.Value("string"),
+                "fake_or_satire": datasets.ClassLabel(names=["Satire", "Fake"]),
+                "url_of_rebutting_article": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "FakeNewsData", "Fake News Stories.xlsx")},
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        with open(filepath, "rb") as f:
+            f = pd.read_excel(f, engine="openpyxl")
+            for id_, row in f.iterrows():
+                yield id_, {
+                    "article_number": row["Article Number"],
+                    "url_of_article": str(row["URL of article"]),
+                    "fake_or_satire": str(row["Fake or Satire?"]),
+                    "url_of_rebutting_article": str(row["URL of rebutting article"]),
+                }