File size: 4,356 Bytes
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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from datasets.download.download_manager import DownloadManager
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """
@article{galinato-etal-2023-context,
title="Context-Based Profanity Detection and Censorship Using Bidirectional Encoder Representations from Transformers",
author="Galinato, Valfrid and Amores, Lawrence and Magsino, Gino Ben and Sumawang, David Rafael",
month="jan",
year="2023"
url="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4341604"
}
"""
_LOCAL = False
_LANGUAGES = ["tgl"]
_DATASETNAME = "tgl_profanity"
_DESCRIPTION = """\
This dataset contains 13.8k Tagalog sentences containing profane words, together
with binary labels denoting whether or not the sentence conveys profanity /
abuse / hate speech. The data was scraped from Twitter using a Python library
called SNScrape and annotated manually by a panel of native Filipino speakers.
"""
_HOMEPAGE = "https://huggingface.co/datasets/mginoben/tagalog-profanity-dataset/"
_LICENSE = Licenses.UNKNOWN.value
_SUPPORTED_TASKS = [Tasks.ABUSIVE_LANGUAGE_PREDICTION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
_URLS = {
"train": "https://huggingface.co/datasets/mginoben/tagalog-profanity-dataset/resolve/main/train.csv",
"val": "https://huggingface.co/datasets/mginoben/tagalog-profanity-dataset/resolve/main/val.csv",
}
class TagalogProfanityDataset(datasets.GeneratorBasedBuilder):
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=_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"
CLASS_LABELS = ["1", "0"]
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.Value("int64"),
}
)
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
features = schemas.text_features(label_names=self.CLASS_LABELS)
else:
raise ValueError(f"Invalid config name: {self.config.schema}")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
data_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_files["val"]},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
"""Yield examples as (key, example) tuples"""
with open(filepath, encoding="utf-8") as f:
csv_reader = csv.reader(f, delimiter=",")
next(csv_reader, None) # skip the headers
for idx, row in enumerate(csv_reader):
text, label = row
if self.config.schema == "source":
example = {"text": text, "label": int(label)}
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
example = {"id": idx, "text": text, "label": int(label)}
yield idx, example
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