Upload fakebr.py

fakebr.py

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+# 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.br dataset"""
+
+
+import os
+from pprint import pprint
+
+import datasets
+
+
+_CITATION = """\
+@article{silva:20,
+  title = "Towards automatically filtering fake news in Portuguese",
+  journal = "Expert Systems with Applications",
+  volume = "146",
+  pages = "113199",
+  year = "2020",
+  issn = "0957-4174",
+  doi = "https://doi.org/10.1016/j.eswa.2020.113199",
+  url = "http://www.sciencedirect.com/science/article/pii/S0957417420300257",
+  author = "Renato M. Silva and Roney L.S. Santos and Tiago A. Almeida and Thiago A.S. Pardo",
+}
+"""
+
+
+_DESCRIPTION = """\
+Fake.Br Corpus is composed of aligned true and fake news written in Brazilian Portuguese.
+"""
+
+_HOMEPAGE = "https://github.com/roneysco/Fake.br-Corpus"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+
+_URL = "https://github.com/roneysco/Fake.br-Corpus/archive/refs/heads/master.zip"
+
+# column names in metadata texts
+_METADATA_COLS = [
+    "author",
+    "link",
+    "category",
+    "date of publication",
+    "number of tokens",
+    "number of words without punctuation",
+    "number of types",
+    "number of links inside the news",
+    "number of words in upper case",
+    "number of verbs",
+    "number of subjuntive and imperative verbs",
+    "number of nouns",
+    "number of adjectives",
+    "number of adverbs",
+    "number of modal verbs (mainly auxiliary verbs)",
+    "number of singular first and second personal pronouns",
+    "number of plural first personal pronouns",
+    "number of pronouns",
+    "pausality",
+    "number of characters",
+    "average sentence length",
+    "average word length",
+    "percentage of news with speeling errors",
+    "emotiveness",
+    "diversity",
+]
+
+
+class Fakebr(datasets.GeneratorBasedBuilder):
+    """Fake.Br Corpus is composed of aligned true and fake news written in Brazilian Portuguese."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="full_texts",
+            version=VERSION,
+            description="full texts, as collected from their websites",
+        ),
+        datasets.BuilderConfig(
+            name="size_normalized_texts",
+            version=VERSION,
+            description="in each fake-true pair, the longer text is truncated (in number of words) to the size of the shorter text",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "full_texts"
+
+    def _info(self):
+        if self.config.name == "full_texts":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(num_classes=2, names=["fake", "true"]),
+                    "author": datasets.Value("string"),
+                    "link": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                    "date of publication": datasets.Value("string"),
+                    "number of tokens": datasets.Value("int32"),
+                    "number of words without punctuation": datasets.Value("int32"),
+                    "number of types": datasets.Value("int32"),
+                    "number of links inside the news": datasets.Value("int32"),
+                    "number of words in upper case": datasets.Value("int32"),
+                    "number of verbs": datasets.Value("int32"),
+                    "number of subjuntive and imperative verbs": datasets.Value(
+                        "int32"
+                    ),
+                    "number of nouns": datasets.Value("int32"),
+                    "number of adjectives": datasets.Value("int32"),
+                    "number of adverbs": datasets.Value("int32"),
+                    "number of modal verbs (mainly auxiliary verbs)": datasets.Value(
+                        "int32"
+                    ),
+                    "number of singular first and second personal pronouns": datasets.Value(
+                        "int32"
+                    ),
+                    "number of plural first personal pronouns": datasets.Value("int32"),
+                    "number of pronouns": datasets.Value("int32"),
+                    "pausality": datasets.Value("float"),
+                    "number of characters": datasets.Value("int32"),
+                    "average sentence length": datasets.Value("float"),
+                    "average word length": datasets.Value("float"),
+                    "percentage of news with speeling errors": datasets.Value("float"),
+                    "emotiveness": datasets.Value("float"),
+                    "diversity": datasets.Value("float"),
+                }
+            )
+        elif self.config.name == "size_normalized_texts":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(num_classes=2, names=["fake", "true"]),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("text", "label"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URL
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": os.path.join(data_dir, "Fake.br-Corpus-master"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        config_dir = os.path.join(data_dir, self.config.name)
+
+        for label in ["fake", "true"]:
+            label_dir = os.path.join(config_dir, label)
+
+            for example in os.listdir(label_dir):
+                key = label + "_" + example.replace(".txt", "")
+                example_path = os.path.join(label_dir, example)
+
+                with open(example_path, "r") as f:
+                    text = f.read()
+
+                row = {"text": text, "label": label}
+
+                if self.config.name == "full_texts":
+                    metadata_path = os.path.join(
+                        config_dir,
+                        f"{label}-meta-information",
+                        example.replace(".txt", "-meta.txt"),
+                    )
+
+                    with open(metadata_path, "r") as f:
+                        metadata = f.read().split("\n")
+
+                    metadata = dict(zip(_METADATA_COLS, metadata))
+
+                    if metadata["author"] == "None":
+                        metadata["author"] = ""
+
+                    if metadata["number of links inside the news"] == "None":
+                        metadata["number of links inside the news"] = "0"
+
+                    row.update(metadata)
+
+                yield key, row