Update files from the datasets library (from 1.2.0)

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

.gitattributes

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- bg
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+---
+
+# Dataset Card for Clickbait/Fake News in Bulgarian
+
+## 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:** [Data Science Society / Case Fake News](https://gitlab.com/datasciencesociety/case_fake_news)
+- **Repository:** [Data Science Society / Case Fake News / Data](https://gitlab.com/datasciencesociety/case_fake_news/-/tree/master/data)
+- **Paper:** [This paper uses the dataset.](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+This is a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned. 
+The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks.
+
+The dataset was prepared for the Hack the
+Fake News hackathon. It was provided by the
+[Bulgarian Association of PR Agencies](http://www.bapra.bg/) and is
+available in [Gitlab](https://gitlab.com/datasciencesociety/). 
+
+The corpus was automatically collected, and then annotated by students of journalism. 
+
+The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
+and 1,968 (i.e., 70%) are click-baits; There are 761 testing examples. 
+
+There is 98% correlation between fake news and clickbaits.
+
+One important aspect about the training dataset is that it contains many repetitions.
+This should not be surprising as it attempts to represent a natural distribution of factual
+vs. fake news on-line over a period of time. As publishers of fake news often have a group of
+websites that feature the same deceiving content, we should expect some repetition.
+In particular, the training dataset contains
+434 unique articles with duplicates. These articles have three reposts each on average, with
+the most reposted article appearing 45 times.
+If we take into account the labels of the reposted articles, we can see that if an article
+is reposted, it is more likely to be fake news.
+The number of fake news that have a duplicate in the training dataset are 1018 whereas,
+the number of articles with genuine content
+that have a duplicate article in the training set is 322.
+
+(The dataset description is from the following [paper](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf).)
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Bulgarian
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+Each entry in the dataset consists of the following elements: 
+
+* `fake_news_score` - a label indicating whether the article is fake or not
+
+* `click_bait_score` - another label indicating whether it is a click-bait
+
+* `content_title` - article heading
+
+* `content_url` - URL of the original article
+
+* `content_published_time` - date of publication
+
+* `content` - article content 
+
+
+### Data Splits
+
+The **training dataset** contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
+and 1,968 (i.e., 70%) are click-baits; 
+
+The **validation dataset** contains 761 testing examples. 
+
+## 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

clickbait_news_bg.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.
+
+""" Dataset with clickbait and fake news in Bulgarian. """
+
+from __future__ import absolute_import, division, print_function
+
+import openpyxl  # noqa: requires this pandas optional dependency for reading xlsx files
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{clickbait_news_bg,
+title = {Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.},
+authors={Data Science Society},
+year={2017},
+url={https://gitlab.com/datasciencesociety/case_fake_news/}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://gitlab.com/datasciencesociety/case_fake_news/"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {
+    "default_train": "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Training_Set.xlsx",
+    "default_validation": "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Validation_Set.xlsx",
+}
+
+
+class ClickbaitNewsBG(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        if self.config.name == "default":
+            features = datasets.Features(
+                {
+                    "fake_news_score": datasets.features.ClassLabel(names=["legitimate", "fake"]),
+                    "click_bait_score": datasets.features.ClassLabel(names=["normal", "clickbait"]),
+                    "content_title": datasets.Value("string"),
+                    "content_url": datasets.Value("string"),
+                    "content_published_time": datasets.Value("string"),
+                    "content": datasets.Value("string"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download(_URLs)
+
+        return [
+            datasets.SplitGenerator(
+                name=spl_enum,
+                gen_kwargs={
+                    "filepath": data_dir[f"{self.config.name}_{spl}"],
+                    "split": spl,
+                },
+            )
+            for spl, spl_enum in [
+                ("train", datasets.Split.TRAIN),
+                ("validation", datasets.Split.VALIDATION),
+            ]
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        keys = [
+            "fake_news_score",
+            "click_bait_score",
+            "content_title",
+            "content_url",
+            "content_published_time",
+            "content",
+        ]
+        with open(filepath, "rb") as f:
+            data = pd.read_excel(f, engine="openpyxl")
+            for id_, row in enumerate(data.itertuples()):
+                row_dict = dict()
+                for key, value in zip(keys, row[1:]):
+                    if key == "fake_news_score":
+                        row_dict[key] = "legitimate" if value == 1 else "fake"
+                    elif key == "click_bait_score":
+                        row_dict[key] = "normal" if value == 1 else "clickbait"
+                    else:
+                        row_dict[key] = str(value)
+                yield id_, row_dict

dataset_infos.json

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+{"default": {"description": "Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.\n", "citation": "@InProceedings{clickbait_news_bg,\ntitle = {Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.},\nauthors={Data Science Society},\nyear={2017},\nurl={https://gitlab.com/datasciencesociety/case_fake_news/}\n}\n", "homepage": "https://gitlab.com/datasciencesociety/case_fake_news/", "license": "", "features": {"fake_news_score": {"num_classes": 2, "names": ["legitimate", "fake"], "names_file": null, "id": null, "_type": "ClassLabel"}, "click_bait_score": {"num_classes": 2, "names": ["normal", "clickbait"], "names_file": null, "id": null, "_type": "ClassLabel"}, "content_title": {"dtype": "string", "id": null, "_type": "Value"}, "content_url": {"dtype": "string", "id": null, "_type": "Value"}, "content_published_time": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "clickbait_news_bg", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 24480402, "num_examples": 2815, "dataset_name": "clickbait_news_bg"}, "validation": {"name": "validation", "num_bytes": 6752242, "num_examples": 761, "dataset_name": "clickbait_news_bg"}}, "download_checksums": {"https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Training_Set.xlsx": {"num_bytes": 6543801, "checksum": "ffb10237c03f06f73b65a63e3eb507ab8683d5bb71b35233b3f7703ff3a60c7e"}, "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Validation_Set.xlsx": {"num_bytes": 2025774, "checksum": "56207dfa58f9b3eb20444a919e739321768cd37ad7e53612658a80f483eb003a"}}, "download_size": 8569575, "post_processing_size": null, "dataset_size": 31232644, "size_in_bytes": 39802219}}

dummy/1.1.0/dummy_data.zip

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