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:
+- tl
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+---
+
+# Dataset Card for Fake News Filipino
+
+## 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: [Fake News Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)**
+- **Repository: [Fake News Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)**
+- **Paper: [LREC 2020 paper](http://www.lrec-conf.org/proceedings/lrec2020/index.html)**
+- **Leaderboard:**
+- **Point of Contact:[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)**
+
+### Dataset Summary
+
+Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular.
+
+## Dataset Structure
+
+### Data Instances
+
+Sample data:
+```
+{
+  "label": "0",
+  "article": "Sa 8-pahinang desisyon, pinaboran ng Sandiganbayan First Division ang petition for Writ of Preliminary Attachment/Garnishment na inihain ng prosekusyon laban sa mambabatas."
+}
+```
+
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+Fake news articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real news articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
+
+### Curation Rationale
+
+We remedy the lack of a proper, curated benchmark dataset for fake news detection in Filipino by constructing and producing what we call “Fake News Filipino.” 
+
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+We construct the dataset by scraping our source websites, encoding all characters into UTF-8. Preprocessing was light to keep information intact: we retain capitalization and punctuation, and do not correct any misspelled words.
+
+#### Who are the source language producers?
+
+Jan Christian Blaise Cruz, Julianne Agatha Tan, and Charibeth Cheng
+
+### 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
+
+[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph), Julianne Agatha Tan, and Charibeth Cheng
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+    @inproceedings{cruz2020localization,
+      title={Localization of Fake News Detection via Multitask Transfer Learning},
+      author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+      booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+      pages={2596--2604},
+      year={2020}
+    }

dataset_infos.json

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+{"default": {"description": "    Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.\n", "citation": "    @inproceedings{cruz2020localization,\n      title={Localization of Fake News Detection via Multitask Transfer Learning},\n      author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},\n      booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},\n      pages={2596--2604},\n      year={2020}\n    }\n", "homepage": "https://github.com/jcblaisecruz02/Tagalog-fake-news", "license": "", "features": {"label": {"num_classes": 2, "names": ["0", "1"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "fake_news_filipino", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3623685, "num_examples": 3206, "dataset_name": "fake_news_filipino"}}, "download_checksums": {"https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/fakenews/fakenews.zip": {"num_bytes": 1313458, "checksum": "d3b823099f819e18594294487939c5534dc995677162fe744f103c01ff4c74f3"}}, "download_size": 1313458, "post_processing_size": null, "dataset_size": 3623685, "size_in_bytes": 4937143}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:348817db0c64b1dbb43becbcc963d7d8d7e67336b99d8f8bc5fbc0cddfee59d8
+size 2267

fake_news_filipino.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 Filipino Dataset"""
+
+import csv
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+    Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
+"""
+
+_CITATION = """\
+    @inproceedings{cruz2020localization,
+      title={Localization of Fake News Detection via Multitask Transfer Learning},
+      author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
+      booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
+      pages={2596--2604},
+      year={2020}
+    }
+"""
+
+_HOMEPAGE = "https://github.com/jcblaisecruz02/Tagalog-fake-news"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/fakenews/fakenews.zip"
+
+
+class FakeNewsFilipino(datasets.GeneratorBasedBuilder):
+    """Low-Resource Fake News Detection Corpora in Filipino"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {"label": datasets.features.ClassLabel(names=["0", "1"]), "article": 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_and_extract(_URL)
+        train_path = os.path.join(data_dir, "fakenews", "full.csv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                label, article = row
+                yield id_, {"label": label, "article": article}