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uit_victsd.py

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+# 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.
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@inproceedings{,
+  author    = {Nguyen, Luan Thanh and Van Nguyen, Kiet and Nguyen,  Ngan Luu-Thuy},
+  title     = {Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese},
+  booktitle = {Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices},
+  year      = {2021},
+  publisher = {Springer International Publishing},
+  address   = {Kuala Lumpur, Malaysia},
+  pages     = {572--583},
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["vie"]
+_DATASETNAME = "uit_victsd"
+_DESCRIPTION = """
+The UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) is a compilation of 10,000 human-annotated
+comments intended for constructive and toxic comments detection. The dataset spans 10 domains, reflecting the diverse topics
+and expressions found in social media interactions among Vietnamese users.
+"""
+
+_HOMEPAGE = "https://github.com/tarudesu/ViCTSD"
+_LICENSE = Licenses.UNKNOWN.value
+_URL = "https://huggingface.co/datasets/tarudesu/ViCTSD"
+
+
+_SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION, Tasks.ABUSIVE_LANGUAGE_PREDICTION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class UiTViCTSDDataset(datasets.GeneratorBasedBuilder):
+    """
+    Dataset of Vietnamese social media comments annotated
+    for constructiveness and toxicity.
+    """
+
+    SUBSETS = ["constructiveness", "toxicity"]
+    CLASS_LABELS = [0, 1]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema for {subset} subset",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_constructiveness_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "Unnamed: 0": datasets.Value("int64"),  # Column name missing in original dataset
+                    "Comment": datasets.Value("string"),
+                    "Constructiveness": datasets.ClassLabel(names=self.CLASS_LABELS),
+                    "Toxicity": datasets.ClassLabel(names=self.CLASS_LABELS),
+                    "Title": datasets.Value("string"),
+                    "Topic": 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]:
+        # dl_manager not used since dataloader uses HF 'load_dataset'
+        return [datasets.SplitGenerator(name=split, gen_kwargs={"split": split._name}) for split in (datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST)]
+
+    def _load_hf_data_from_remote(self, split: str) -> datasets.DatasetDict:
+        """Load dataset from HuggingFace."""
+        HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
+        _hf_dataset_source = datasets.load_dataset(HF_REMOTE_REF, split=split)
+        return _hf_dataset_source
+
+    def _generate_examples(self, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data = self._load_hf_data_from_remote(split=split)
+        for index, row in enumerate(data):
+            if self.config.schema == "source":
+                example = row
+
+            elif self.config.schema == "seacrowd_text":
+                if "constructiveness" in self.config.name:
+                    label = row["Constructiveness"]
+                elif "toxicity" in self.config.name:
+                    label = row["Toxicity"]
+                example = {"id": str(index), "text": row["Comment"], "label": label}
+            yield index, example