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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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- pt
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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://github.com/jghm-f/FACTCK.BR
+- **Repository:** https://github.com/jghm-f/FACTCK.BR
+- **Paper:** https://dl.acm.org/doi/10.1145/3323503.3361698
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification.
+The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.
+The FACTCK.BR dataset contains 1309 claims with its corresponding label.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification.\nThe data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.\nThe FACTCK.BR dataset contains 1309 claims with its corresponding label.\n", "citation": "\n    @inproceedings{10.1145/3323503.3361698,\n    author = {Moreno, Jo\\~{a}o and Bressan, Gra\\c{c}a},\n    title = {FACTCK.BR: A New Dataset to Study Fake News},\n    year = {2019},\n    isbn = {9781450367639},\n    publisher = {Association for Computing Machinery},\n    address = {New York, NY, USA},\n    url = {https://doi.org/10.1145/3323503.3361698},\n    doi = {10.1145/3323503.3361698},\n    abstract = {Machine learning algorithms can be used to combat fake news propagation. For the news classification, labeled datasets are required, however, among the existing datasets, few separate verified false from skewed ones with a good variety of sources. This work presents FACTCK.BR, a new dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification. The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.},\n    booktitle = {Proceedings of the 25th Brazillian Symposium on Multimedia and the Web},\n    pages = {525\u2013527},\n    numpages = {3},\n    keywords = {fake news, fact check, information extraction, dataset},\n    location = {Rio de Janeiro, Brazil},\n    series = {WebMedia '19}\n}\n", "homepage": "https://github.com/jghm-f/FACTCK.BR", "license": "MIT", "features": {"url": {"dtype": "string", "id": null, "_type": "Value"}, "author": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "review": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "rating": {"dtype": "float32", "id": null, "_type": "Value"}, "best_rating": {"dtype": "float32", "id": null, "_type": "Value"}, "label": {"num_classes": 14, "names": ["falso", "distorcido", "impreciso", "exagerado", "insustent\u00e1vel", "verdadeiro", "outros", "subestimado", "imposs\u00edvel provar", "discut\u00edvel", "sem contexto", "de olho", "verdadeiro, mas", "ainda \u00e9 cedo para dizer"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "claim", "output": "label"}, "builder_name": "factckbr", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 750646, "num_examples": 1313, "dataset_name": "factckbr"}}, "download_checksums": {"https://github.com/jghm-f/FACTCK.BR/raw/master/FACTCKBR.tsv": {"num_bytes": 721314, "checksum": "1e90fe8b67af22d0f756a7f831b6a2f66072b5d0d66fb1172f3bda1e748201db"}}, "download_size": 721314, "post_processing_size": null, "dataset_size": 750646, "size_in_bytes": 1471960}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e0df48af919eafc4b2ef167bef7c8022952ec13b405d8fd1871b4b1a5164233
+size 1678

factckbr.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.
+"""FACTCK.BR: A dataset to study Fake News in Portuguese."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_CITATION = """
+    @inproceedings{10.1145/3323503.3361698,
+    author = {Moreno, Jo\\~{a}o and Bressan, Gra\\c{c}a},
+    title = {FACTCK.BR: A New Dataset to Study Fake News},
+    year = {2019},
+    isbn = {9781450367639},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3323503.3361698},
+    doi = {10.1145/3323503.3361698},
+    abstract = {Machine learning algorithms can be used to combat fake news propagation. For the news classification, labeled datasets are required, however, among the existing datasets, few separate verified false from skewed ones with a good variety of sources. This work presents FACTCK.BR, a new dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification. The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.},
+    booktitle = {Proceedings of the 25th Brazillian Symposium on Multimedia and the Web},
+    pages = {525–527},
+    numpages = {3},
+    keywords = {fake news, fact check, information extraction, dataset},
+    location = {Rio de Janeiro, Brazil},
+    series = {WebMedia '19}
+}
+"""
+
+_DESCRIPTION = """\
+A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification.
+The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.
+The FACTCK.BR dataset contains 1309 claims with its corresponding label.
+"""
+
+_HOMEPAGE = "https://github.com/jghm-f/FACTCK.BR"
+
+_LICENSE = "MIT"
+
+_URL = "https://github.com/jghm-f/FACTCK.BR/raw/master/FACTCKBR.tsv"
+
+
+class Factckbr(datasets.GeneratorBasedBuilder):
+    """FACTCK.BR: A dataset to study Fake News in Portuguese."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "url": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "claim": datasets.Value("string"),
+                    "review": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "rating": datasets.Value("float"),
+                    "best_rating": datasets.Value("float"),
+                    "label": datasets.ClassLabel(
+                        names=[
+                            "falso",
+                            "distorcido",
+                            "impreciso",
+                            "exagerado",
+                            "insustentável",
+                            "verdadeiro",
+                            "outros",
+                            "subestimado",
+                            "impossível provar",
+                            "discutível",
+                            "sem contexto",
+                            "de olho",
+                            "verdadeiro, mas",
+                            "ainda é cedo para dizer",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=("claim", "label"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        data_file = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_file,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+
+        with open(filepath, encoding="utf-8") as tsv_file:
+            reader = csv.reader(tsv_file, delimiter="\t")
+            for id_, row in enumerate(reader):
+                if id_ == 0:
+                    continue
+
+                label = row[8].lower()
+
+                yield id_, {
+                    "url": row[0],
+                    "author": row[1],
+                    "date": row[2],
+                    "claim": row[3],
+                    "review": row[4],
+                    "title": row[5],
+                    "rating": row[6] or 0,
+                    "best_rating": row[7],
+                    "label": -1 if label == "" else label,
+                }