offenseval2020_tr / offenseval2020_tr.py
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# coding=utf-8
# Lint as: python3
"""OffensEval-TR 2020: A Corpus of Turkish Offensive Language on Social Media"""
import csv
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@InProceedings{coltekin2020lrec,
author = {Cagri Coltekin},
year = {2020},
title = {A Corpus of Turkish Offensive Language on Social Media},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
pages = {6174--6184},
address = {Marseille, France},
url = {https://www.aclweb.org/anthology/2020.lrec-1.758},
}
"""
_DESCRIPTION = """\
OffensEval-TR 2020 is a Turkish offensive language corpus. The corpus consist of randomly sampled tweets and annotated in a similar way to OffensEval and GermEval.
"""
_HOMEPAGE = "https://coltekin.github.io/offensive-turkish/"
_DOWNLOAD_URL = "https://coltekin.github.io/offensive-turkish/offenseval2020-turkish.zip"
_FOLDER_NAME = "offenseval-tr-{split}-v1"
class OffensEval2020TRConfig(datasets.BuilderConfig):
"""BuilderConfig for OffensEval2020TR."""
def __init__(self, **kwargs):
"""BuilderConfig for OffensEval2020TR.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(OffensEval2020TRConfig, self).__init__(**kwargs)
class Offenseval2020TR(datasets.GeneratorBasedBuilder):
"""OffensEval-TR 2020: A Corpus of Turkish Offensive Language on Social Media"""
BUILDER_CONFIGS = [
OffensEval2020TRConfig(
name="offenseval2020-turkish",
version=datasets.Version("1.0.0"),
description="OffensEval-TR 2020: A Corpus of Turkish Offensive Language on Social Media",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"tweet": datasets.Value("string"),
"subtask_a": datasets.features.ClassLabel(names=["NOT", "OFF"]),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
data_dir = os.path.join(dl_dir, self.config.name)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(
data_dir, _FOLDER_NAME.format(split="training"), "offenseval-tr-training-v1.tsv"
),
"labelpath": None,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
data_dir, _FOLDER_NAME.format(split="testset"), "offenseval-tr-testset-v1.tsv"
),
"labelpath": os.path.join(
data_dir, _FOLDER_NAME.format(split="testset"), "offenseval-tr-labela-v1.tsv"
),
},
),
]
def _generate_examples(self, filepath, labelpath):
"""Generate OffensEval2020TR examples."""
logger.info("⏳ Generating examples from = %s", filepath)
if labelpath:
with open(filepath, encoding="utf-8") as f:
with open(labelpath, encoding="utf-8") as f2:
reader_testset = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
reader_label = csv.DictReader(
f2, delimiter=",", quoting=csv.QUOTE_NONE, fieldnames=["id", "subtask_a"]
)
list_label = list(reader_label)
for idx, row in enumerate(reader_testset):
row_label = list_label[idx]
yield idx, {"id": row["id"], "tweet": row["tweet"], "subtask_a": row_label["subtask_a"]}
else:
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for idx, row in enumerate(reader):
yield idx, {"id": row["id"], "tweet": row["tweet"], "subtask_a": row["subtask_a"]}