Upload uit_viocd.py with huggingface_hub

uit_viocd.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@incollection{nguyen2021vietnamese,
+  title={Vietnamese Complaint Detection on E-Commerce Websites},
+  author={Nguyen, Nhung Thi-Hong and Ha, Phuong Phan-Dieu and Nguyen, Luan Thanh and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy},
+  booktitle={New Trends in Intelligent Software Methodologies, Tools and Techniques},
+  pages={618--629},
+  year={2021},
+  publisher={IOS Press}
+}
+"""
+
+_DATASETNAME = "uit_viocd"
+
+_DESCRIPTION = """\
+The UIT-ViOCD dataset includes 5,485 reviews e-commerce sites across four categories: fashion, cosmetics, applications,
+and phones. Each review is annotated by humans, assigning a label of 1 for complaints and 0 for non-complaints.
+The dataset is divided into training, validation, and test sets, distributed approximately in an 80:10:10 ratio.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/tarudesu/ViOCD"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/tarudesu/ViOCD/resolve/main/train.csv?download=true",
+    "val": "https://huggingface.co/datasets/tarudesu/ViOCD/resolve/main/val.csv?download=true",
+    "test": "https://huggingface.co/datasets/tarudesu/ViOCD/resolve/main/test.csv?download=true",
+}
+
+_SUPPORTED_TASKS = [Tasks.COMPLAINT_DETECTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class UITVIOCDDataset(datasets.GeneratorBasedBuilder):
+    """The UIT-ViOCD dataset includes 5,485 reviews e-commerce sites across four categories: fashion, cosmetics, applications, and phones."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    LABEL_CLASSES = [1, 0]
+
+    SEACROWD_SCHEMA_NAME = "text"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_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=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "review": datasets.Value("string"),
+                    "review_tokenize": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=self.LABEL_CLASSES),
+                    "domain": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(self.LABEL_CLASSES)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        data_dir = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["val"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        df = pd.read_csv(filepath)
+
+        if self.config.schema == "source":
+            for key, example in df.iterrows():
+                yield key, {
+                    "review": example["review"],
+                    "review_tokenize": example["review_tokenize"],
+                    "label": example["label"],
+                    "domain": example["domain"],
+                }
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for key, example in df.iterrows():
+                yield key, {"id": str(key), "text": str(example["review"]), "label": int(example["label"])}