Update bookcorpus.py

bookcorpus.py

@@ -1,6 +1,5 @@
-
 # coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+# Copyright 2023 Your Name or Your Organization
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,16 +14,21 @@
 # limitations under the License.
 
 # Lint as: python3
-"""Toxic/Abusive Tweets Multilabel Classification Dataset for TuPi."""
+"""TuPi: Hate Speech Detection Dataset in Portuguese."""
 
 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 = """\
-# Replace with the citation for TuPi dataset
+@article{YourReferenceHere,
+  author    = {Your Name or Your Organization},
+  title     = {TuPi: Largest Hate Speech Dataset in Portuguese},
+  year      = {2023},
+  url       = {URL to the official TuPi dataset publication or documentation},
+  eprinttype = {arXiv},
+  timestamp = {Current Timestamp},
+}
 """
 
 _DESCRIPTION = """\
@@ -45,23 +49,16 @@ The data includes content from Twitter and Instagram, collected between 2017 and
 - Other
 """
 
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "# Replace with the TuPi dataset homepage"
-
-# TODO: Add the license for the dataset here if you can find it
-_LICENSE = "# Replace with the TuPi dataset license"
-
-# 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)
+_HOMEPAGE = "# Add the TuPi dataset homepage URL"
+_LICENSE = "# Add the TuPi dataset license URL"
 
 _URLS = {
     "multilabel": "https://raw.githubusercontent.com/Silly-Machine/TuPi-Portuguese-Hate-Speech-Dataset/main/datasets/tupi_hierarchy.csv",
     "binary": "https://raw.githubusercontent.com/Silly-Machine/TuPi-Portuguese-Hate-Speech-Dataset/main/datasets/tupi_binary.csv",
-        }
+}
 
 class TuPi(datasets.GeneratorBasedBuilder):
-    """Toxic/Abusive Tweets Classification Dataset for TuPi."""
+    """TuPi Hate Speech Detection Dataset in Portuguese."""
 
     VERSION = datasets.Version("1.0.0")
 
@@ -69,12 +66,12 @@ class TuPi(datasets.GeneratorBasedBuilder):
         datasets.BuilderConfig(
             name="multilabel",
             version=VERSION,
-            description="Replace with the multilabel dataset description.",
+            description="Full multilabel dataset with annotations for each category.",
         ),
         datasets.BuilderConfig(
             name="binary",
             version=VERSION,
-            description="Replace with the binary dataset description.",
+            description="Binary classification dataset with combined hate speech labels.",
         ),
     ]
 
@@ -85,14 +82,25 @@ class TuPi(datasets.GeneratorBasedBuilder):
             features = datasets.Features(
                 {
                     "text": datasets.Value("string"),
-                    "hate": datasets.ClassLabel(names=["not-hate", "hate"]),
+                    "label": datasets.ClassLabel(names=["non-hate", "hate"]),
                 }
             )
         else:
             features = datasets.Features(
                 {
                     "text": datasets.Value("string"),
-                    # Replace with the appropriate class labels for TuPi multilabel dataset
+                    "aggressive": 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_shaming": 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"]),
+                    "politics": 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"]),
                 }
             )
 
@@ -132,12 +140,20 @@ class TuPi(datasets.GeneratorBasedBuilder):
         df = pd.read_csv(filepath, engine="python")
         for key, row in enumerate(df.itertuples()):
             if self.config.name == "multilabel":
-                # Replace with the appropriate field names for TuPi multilabel dataset
                 yield key, {
                     "text": row.text,
-                    "label_1": int(row.label_1),
-                    "label_2": int(row.label_2),
-                    # Add more labels as needed
+                    "aggressive": int(float(row.aggressive)),
+                    "ageism": int(float(row.ageism)),
+                    "aporophobia": int(float(row.aporophobia)),
+                    "body_shaming": int(float(row.body_shaming)),
+                    "capacitism": int(float(row.capacitism)),
+                    "lgbtphobia": int(float(row.lgbtphobia)),
+                    "politics": int(float(row.politics)),
+                    "racism": int(float(row.racism)),
+                    "religious_intolerance": int(float(row.religious_intolerance)),
+                    "misogyny": int(float(row.misogyny)),
+                    "xenophobia": int(float(row.xenophobia)),
+                    "other": int(float(row.other)),
                 }
             else:
-                yield key, {"text": row.text, "label": int(row.toxic)}
+                yield key, {"text": row.text, "label": int(row.hate)}