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gklmip_newsclass.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.
+
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import numpy as np
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@article{,
+author="Jiang, Shengyi
+and Fu, Sihui
+and Lin, Nankai
+and Fu, Yingwen",
+title="Pre-trained Models and Evaluation Data for the Khmer Language",
+year="2021",
+publisher="Tsinghua Science and Technology",
+}
+"""
+
+_DATASETNAME = "gklmip_newsclass"
+
+_DESCRIPTION = """\
+The GKLMIP Khmer News Dataset is scraped from the Voice of America Khmer website. \
+The news articles in the dataset are categorized into 8 categories: culture, economics, education, \
+environment, health, politics, rights and science.
+"""
+
+_HOMEPAGE = "https://github.com/GKLMIP/Pretrained-Models-For-Khmer"
+_LANGUAGES = ["khm"]
+
+_LICENSE = Licenses.UNKNOWN.value
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/GKLMIP/Pretrained-Models-For-Khmer/raw/main/NewsDataset.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.TOPIC_MODELING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_TAGS = ["culture", "economic", "education", "environment", "health", "politics", "right", "science"]
+
+
+class GklmipNewsclass(datasets.GeneratorBasedBuilder):
+    """\
+    The GKLMIP Khmer News Dataset is scraped from the Voice of America Khmer website. \
+    The news articles in the dataset are categorized into 8 categories: culture, economics, education, \
+    environment, health, politics, rights and science.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "text"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "culture": datasets.Value("bool"),
+                    "economic": datasets.Value("bool"),
+                    "education": datasets.Value("bool"),
+                    "environment": datasets.Value("bool"),
+                    "health": datasets.Value("bool"),
+                    "politics": datasets.Value("bool"),
+                    "right": datasets.Value("bool"),
+                    "science": datasets.Value("bool"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(_TAGS)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.csv"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.csv"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        dataset = pd.read_csv(filepath)
+        reverse_encoding = dict(zip(range(len(_TAGS)), _TAGS))
+        if self.config.schema == "source":
+            for i, row in dataset.iterrows():
+                yield i, {
+                    "text": row["text"],
+                    "culture": row["culture"],
+                    "economic": row["economic"],
+                    "education": row["education"],
+                    "environment": row["environment"],
+                    "health": row["health"],
+                    "politics": row["politics"],
+                    "right": row["right"],
+                    "science": row["science"],
+                }
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in dataset.iterrows():
+                yield i, {"id": i, "text": row["text"], "label": reverse_encoding[np.argmax(row[_TAGS])]}