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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"])}
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