Update bookcorpus.py

bookcorpus.py

@@ -1,159 +1,41 @@
-# coding=utf-8
-# 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.
-# 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.
-
-# Lint as: python3
-"""TuPi: Hate Speech Detection Dataset in Portuguese."""
-
-import os
+from datasets import Dataset, DatasetBuilder
 import pandas as pd
-import datasets
-
-_CITATION = """\
-@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 = """\
-TuPi is the largest annotated dataset for hate speech in Portuguese, formed through the collaboration of Varags et al, Leite et al, and Fortuna et al. The dataset underwent a re-annotation process, including over 10 thousand previously unpublished documents.
-
-The data includes content from Twitter and Instagram, collected between 2017 and 2023. Each document was individually labeled by three annotators into twelve categories:
-- Aggressive
-- Ageism
-- Aporophobia
-- Body Shaming
-- Capacitism
-- LGBTphobia
-- Politics
-- Racism
-- Religious Intolerance
-- Misogyny
-- Xenophobia
-- Other
-"""
-
-_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):
-    """TuPi Hate Speech Detection Dataset in Portuguese."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="multilabel",
-            version=VERSION,
-            description="Full multilabel dataset with annotations for each category.",
-        ),
-        datasets.BuilderConfig(
-            name="binary",
-            version=VERSION,
-            description="Binary classification dataset with combined hate speech labels.",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "binary"
+class YourDataset(DatasetBuilder):
+    VERSION = "1.0.0"
 
     def _info(self):
-        if self.config.name == "binary":
-            features = datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.ClassLabel(names=["non-hate", "hate"]),
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "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"]),
-                }
-            )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
-        )
+        features = {
+            "source": "string",
+            "id": "string",
+            "text": "string",
+            "researcher": "string",
+            "year": "int",
+            "aggressive": "float",
+            "hate": "float",
+        }
+        return datasets.DatasetInfo(features=features)
 
     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, "train.csv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={"filepath": os.path.join(data_dir, "validation.csv")},
-                ),
-            ]
-        else:
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "tuipi_multilabel_dataset.csv"),
-                    },
-                )
-            ]
-
-    def _generate_examples(self, filepath):
-        df = pd.read_csv(filepath, engine="python")
-        for key, row in enumerate(df.itertuples()):
-            if self.config.name == "multilabel":
-                yield key, {
-                    "text": row.text,
-                    "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.hate)}
+        # You can download and extract data here if needed
+        return []
+
+    def _generate_examples(self, file_path):
+        df = pd.read_csv(file_path)
+        for i, row in df.iterrows():
+            yield i, {
+                "source": str(row["source"]),
+                "id": str(row["id"]),
+                "text": str(row["text"]),
+                "researcher": str(row["researcher"]),
+                "year": int(row["year"]),
+                "aggressive": float(row["aggressive"]),
+                "hate": float(row["hate"]),
+            }
+
+# Load your dataset
+file_path = "https://raw.githubusercontent.com/Silly-Machine/TuPi-Portuguese-Hate-Speech-Dataset/main/datasets/tupi_binary.csv"
+your_dataset = Dataset.load_from_disk(file_path)
+
+# Use the Hugging Face Dataset viewer
+your_dataset.view()