Upload tupy.py

tupy.py

@@ -0,0 +1,118 @@
+import os
+import pandas as pd
+import datasets
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+# Add your citation information here
+"""
+
+_DESCRIPTION = """\
+# Add your dataset description here
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/JAugusto97/ToLD-Br"
+
+# TODO: Add the license for the dataset here if you can find it
+_LICENSE = "https://github.com/JAugusto97/ToLD-Br/blob/main/LICENSE_ToLD-Br.txt "
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+
+_URLS = {
+    "multilabel": "https://raw.githubusercontent.com/JAugusto97/ToLD-Br/main/ToLD-BR.csv",
+    "binary": "https://github.com/JAugusto97/ToLD-Br/raw/main/experiments/data/1annotator.zip",
+}
+
+class ToldBr(datasets.GeneratorBasedBuilder):
+    """Toxic/Abusive Tweets Classification Dataset for Brazilian Portuguese."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="multilabel",
+            version=VERSION,
+            description="""
+            Full multilabel dataset with target values ranging
+            from 0 to 3 representing the votes from each annotator.
+            """,
+        ),
+        datasets.BuilderConfig(
+            name="binary",
+            version=VERSION,
+            description="""
+            Binary classification dataset version separated into train, dev, and test sets.
+            A text is considered toxic if at least one of the multilabel classes was labeled
+            by at least one annotator.
+            """,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "binary"
+
+    def _info(self):
+        if self.config.name == "binary":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["not-hate", "hate"]),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "aggressive": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "hate": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "ageism": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "aporophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "body_shame": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "capacitism": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "lgbtphobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "political": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "racism": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "religious_intolerance": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "misogyny": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "xenophobia": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                    "other": datasets.ClassLabel(names=["zero_votes", "one_vote", "two_votes", "three_votes"]),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        if self.config.name == "binary":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": os.path.join(data_dir, "path_to_binary_train.csv")},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": os.path.join(data_dir, "path_to_binary_test.csv")},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={"filepath": os.path.join(data_dir, "path_to_binary_validation.csv")},
+                ),
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "path_to_multilabel.csv"),
+                    },
+                )
+            ]
+
+   
+