Create hatespeech_filipino.py

hatespeech_filipino.py

@@ -0,0 +1,111 @@
+# 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.
+"""Hate Speech Text Classification Dataset in Filipino."""
+
+import csv
+import os
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """\
+    Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections.
+"""
+
+_CITATION = """\
+@article{Cabasag-2019-hate-speech,
+  title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.},
+  author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng},
+  journal={Philippine Computing Journal},
+  volume={XIV},
+  number={1},
+  month={August},
+  year={2019}
+}
+"""
+
+_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = "https://huggingface.co/datasets/jcblaise/hatespeech_filipino/resolve/main/hatespeech_raw.zip"
+
+
+class HateSpeechFilipino(datasets.GeneratorBasedBuilder):
+    """Hate Speech Text Classification Dataset in Filipino."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        # Labels: 0="Non-hate Speech", 1="Hate Speech"
+        features = datasets.Features(
+            {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["0", "1"])}
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        train_path = os.path.join(data_dir, "hatespeech", "train.csv")
+        test_path = os.path.join(data_dir, "hatespeech", "train.csv")
+        validation_path = os.path.join(data_dir, "hatespeech", "valid.csv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_path,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": validation_path,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                try:
+                    text, label = row
+                    yield id_, {"text": text, "label": label}
+                except ValueError:
+                    pass