Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Thai
Size:
1K<n<10K
License:
# coding=utf-8 | |
# Copyright 2020 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. | |
# Lint as: python3 | |
import json | |
import os | |
import datasets | |
_CITATION = """\ | |
@article{sirihattasak2019annotation, | |
title={Annotation and Classification of Toxicity for Thai Twitter}, | |
author={Sirihattasak, Sugan and Komachi, Mamoru and Ishikawa, Hiroshi}, | |
year={2019} | |
} | |
""" | |
_DESCRIPTION = """\ | |
Thai Toxicity Tweet Corpus contains 3,300 tweets annotated by humans with guidelines including a 44-word dictionary. | |
The author obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus | |
analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains | |
toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing | |
target, and word sense ambiguity. | |
Notes from data cleaner: The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. | |
By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`. | |
Processing can be found at [this PR](https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/pull/1). | |
""" | |
class ThaiToxicityTweetConfig(datasets.BuilderConfig): | |
"""BuilderConfig""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(ThaiToxicityTweetConfig, self).__init__(**kwargs) | |
class ThaiToxicityTweet(datasets.GeneratorBasedBuilder): | |
_DOWNLOAD_URL = "https://archive.org/download/ThaiToxicityTweetCorpus/data.zip" | |
_TRAIN_FILE = "train.jsonl" | |
BUILDER_CONFIGS = [ | |
ThaiToxicityTweetConfig( | |
name="thai_toxicity_tweet", | |
version=datasets.Version("1.0.0"), | |
description=_DESCRIPTION, | |
), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"tweet_id": datasets.Value("string"), | |
"tweet_text": datasets.Value("string"), | |
"toxic_votes": datasets.Value("int32"), | |
"nontoxic_votes": datasets.Value("int32"), | |
"is_toxic": datasets.features.ClassLabel(names=["neg", "pos"]), | |
} | |
), | |
supervised_keys=None, | |
homepage="https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL) | |
data_dir = os.path.join(arch_path, "data") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FILE)}, | |
), | |
] | |
def _generate_examples(self, filepath): | |
"""Generate examples.""" | |
with open(filepath, encoding="utf-8") as f: | |
for id_, row in enumerate(f): | |
data = json.loads(row) | |
yield id_, { | |
"tweet_id": str(data["tweet_id"]), | |
"tweet_text": data["tweet_text"], | |
"toxic_votes": data["toxic_votes"], | |
"nontoxic_votes": data["nontoxic_votes"], | |
"is_toxic": data["is_toxic"], | |
} | |