Closes #309 | Create dataset loader for Vietnamese Hate Speech Detect…

…ion (UIT-ViHSD) #309Uit vihsd (#501)

* create dataloader for uit_vihsd

* Update uit_vihsd.py

* Add some info for the labels

* Update example for Seacrowd schema

seacrowd/sea_datasets/uit_vihsd/uit_vihsd.py

@@ -0,0 +1,145 @@
+# coding=utf-8
+# Copyright 2022 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.
+import os
+from typing import Dict, List, Tuple
+import datasets
+import pandas
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+_CITATION = """
+@InProceedings{10.1007/978-3-030-79457-6_35,
+author="Luu, Son T.
+and Nguyen, Kiet Van
+and Nguyen, Ngan Luu-Thuy",
+editor="Fujita, Hamido
+and Selamat, Ali
+and Lin, Jerry Chun-Wei
+and Ali, Moonis",
+title="A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts",
+booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
+year="2021",
+publisher="Springer International Publishing",
+address="Cham",
+pages="415--426",
+abstract="In recent years, Vietnam witnesses the mass development of social network users on different social
+platforms such as Facebook, Youtube, Instagram, and Tiktok. On social media, hate speech has become a critical
+problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for
+automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment
+in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process
+for annotating and evaluating the quality of the dataset. Finally, we evaluate the dataset by deep learning and transformer models.",
+isbn="978-3-030-79457-6"
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["vie"]
+_DATASETNAME = "uit_vihsd"
+_DESCRIPTION = """
+The ViHSD dataset consists of comments collected from Facebook pages and YouTube channels that have a
+high-interactive rate, and do not restrict comments. This dataset is used for hate speech detection on
+Vietnamese language. Data is anonymized, and labeled as either HATE, OFFENSIVE, or CLEAN.
+"""
+
+_HOMEPAGE = "https://github.com/sonlam1102/vihsd/"
+_LICENSE = Licenses.UNKNOWN.value
+_URL = "https://raw.githubusercontent.com/sonlam1102/vihsd/main/data/vihsd.zip"
+
+_Split_Path = {
+    "train": "vihsd/train.csv",
+    "validation": "vihsd/dev.csv",
+    "test": "vihsd/test.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "1.0.0"
+
+
+class UiTVihsdDataset(datasets.GeneratorBasedBuilder):
+    """
+    The SeaCrowd dataloader for the dataset Vietnamese Hate Speech Detection (UIT-ViHSD).
+    """
+
+    CLASS_LABELS = ["CLEAN", "OFFENSIVE", "HATE"]  # 0:CLEAN, 1:OFFENSIVE, 2:HATE
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema ",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(label_names=self.CLASS_LABELS)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        file_paths = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["train"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["validation"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(file_paths, _Split_Path["test"])},
+            ),
+        ]
+
+    def _generate_examples(self, filepath) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data_lines = pandas.read_csv(filepath)
+        for row in data_lines.itertuples():
+            if self.config.schema == "source":
+                example = {"id": str(row.Index), "text": row.free_text, "label": row.label_id}
+            if self.config.schema == "seacrowd_text":
+                example = {"id": str(row.Index), "text": row.free_text, "label": self.CLASS_LABELS[int(row.label_id)]}
+            yield row.Index, example
+