SEACrowd/uit_vihsd

ViHSD数据集包含从Facebook页面和YouTube频道收集的评论数据，这些页面和频道的互动率较高且不限制评论。该数据集用于越南语的仇恨言论检测。数据经过匿名化处理，并被标记为HATE（仇恨）、OFFENSIVE（冒犯）或CLEAN（干净）。数据集支持情感分析任务，并提供了使用`datasets`和`seacrowd`库加载数据的方法。

The ViHSD dataset consists of comments collected from Facebook pages and YouTube channels that have a high-interactive rate, and do not restrict comments. This dataset is used for hate speech detection on Vietnamese language. Data is anonymized, and labeled as either HATE, OFFENSIVE, or CLEAN. The dataset supports sentiment analysis tasks and provides methods to load the data using the `datasets` and `seacrowd` libraries.

原始信息汇总

Uit Vihsd 数据集概述

数据集简介

Uit Vihsd 数据集包含从高互动率的 Facebook 页面和 YouTube 频道收集的评论，这些评论不限制评论内容。该数据集用于越南语的仇恨言论检测，数据已匿名化，并标记为 HATE、OFFENSIVE 或 CLEAN。

语言

• 越南语 (vie)

支持的任务

• 情感分析 (Sentiment Analysis)

数据集使用

使用 datasets 库

python from datasets import load_dataset dset = datasets.load_dataset("SEACrowd/uit_vihsd", trust_remote_code=True)

使用 seacrowd 库

python import seacrowd as sc

使用默认配置加载数据集

dset = sc.load_dataset("uit_vihsd", schema="seacrowd")

检查数据集的所有可用子集（配置名称）

print(sc.available_config_names("uit_vihsd"))

使用特定配置加载数据集

dset = sc.load_dataset_by_config_name(config_name="<config_name>")

数据集主页

https://github.com/sonlam1102/vihsd/

数据集版本

• 源版本: 1.0.0
• SEACrowd 版本: 2024.06.20

数据集许可证

未知 (Unknown)

引用

如果使用 Uit Vihsd 数据集，请引用以下内容：

@InProceedings{10.1007/978-3-030-79457-6_35, author="Luu, Son T. and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy", editor="Fujita, Hamido and Selamat, Ali and Lin, Jerry Chun-Wei and Ali, Moonis", title="A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts", booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices", year="2021", publisher="Springer International Publishing", address="Cham", pages="415--426", abstract="In recent years, Vietnam witnesses the mass development of social network users on different social platforms such as Facebook, Youtube, Instagram, and Tiktok. On social media, hate speech has become a critical problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process for annotating and evaluating the quality of the dataset. Finally, we evaluate the dataset by deep learning and transformer models.", isbn="978-3-030-79457-6" }

@article{lovenia2024seacrowd, title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, year={2024}, eprint={2406.10118}, journal={arXiv preprint arXiv: 2406.10118} }