metadata
pretty_name: SEA Toxicity Detection
license:
- cc-by-nc-sa-4.0
- cc-by-nc-3.0
- cc-by-nc-4.0
task_categories:
- text-generation
- text-classification
language:
- id
- th
- vi
dataset_info:
- config_name: id
features:
- name: label
dtype: string
- name: prompts
list:
- name: text
dtype: string
- name: metadata
struct:
- name: Abusive
dtype: int64
- name: HS
dtype: int64
- name: HS_Gender
dtype: int64
- name: HS_Group
dtype: int64
- name: HS_Individual
dtype: int64
- name: HS_Moderate
dtype: int64
- name: HS_Other
dtype: int64
- name: HS_Physical
dtype: int64
- name: HS_Race
dtype: int64
- name: HS_Religion
dtype: int64
- name: HS_Strong
dtype: int64
- name: HS_Weak
dtype: int64
- name: language
dtype: string
- name: id
dtype: string
splits:
- name: eval
num_bytes: 242657
num_examples: 1000
- name: examples
num_bytes: 1215
num_examples: 5
download_size: 106737
dataset_size: 243872
- config_name: ms
features:
- name: id
dtype: string
- name: label
dtype: string
- name: prompts
list:
- name: text
dtype: string
- name: metadata
struct:
- name: language
dtype: string
splits:
- name: eval
num_bytes: 146757
num_examples: 1000
- name: examples
num_bytes: 827
num_examples: 5
download_size: 96716
dataset_size: 147584
- config_name: my
features:
- name: id
dtype: string
- name: label
dtype: string
- name: prompts
list:
- name: text
dtype: string
- name: metadata
struct:
- name: language
dtype: string
splits:
- name: eval
num_bytes: 110107
num_examples: 400
- name: examples
num_bytes: 1421
num_examples: 5
download_size: 41320
dataset_size: 111528
- config_name: th
features:
- name: label
dtype: string
- name: prompts
list:
- name: text
dtype: string
- name: metadata
struct:
- name: language
dtype: string
- name: nontoxic_votes
dtype: int64
- name: toxic_votes
dtype: int64
- name: tweet_id
dtype: int64
- name: id
dtype: string
splits:
- name: eval
num_bytes: 274301
num_examples: 1000
- name: examples
num_bytes: 1058
num_examples: 5
download_size: 126955
dataset_size: 275359
- config_name: tl
features:
- name: id
dtype: string
- name: label
dtype: string
- name: prompts
list:
- name: text
dtype: string
- name: metadata
struct:
- name: language
dtype: string
splits:
- name: eval
num_bytes: 51721
num_examples: 400
- name: examples
num_bytes: 660
num_examples: 5
download_size: 35427
dataset_size: 52381
- config_name: vi
features:
- name: label
dtype: string
- name: prompts
list:
- name: text
dtype: string
- name: metadata
struct:
- name: language
dtype: string
- name: id
dtype: string
splits:
- name: eval
num_bytes: 96345
num_examples: 1000
- name: examples
num_bytes: 539
num_examples: 5
download_size: 58271
dataset_size: 96884
configs:
- config_name: id
data_files:
- split: eval
path: id/eval-*
- split: examples
path: id/examples-*
- config_name: ms
data_files:
- split: eval
path: ms/eval-*
- split: examples
path: ms/examples-*
- config_name: my
data_files:
- split: eval
path: my/eval-*
- split: examples
path: my/examples-*
- config_name: th
data_files:
- split: eval
path: th/eval-*
- split: examples
path: th/examples-*
- config_name: tl
data_files:
- split: eval
path: tl/eval-*
- split: examples
path: tl/examples-*
- config_name: vi
data_files:
- split: eval
path: vi/eval-*
- split: examples
path: vi/examples-*
size_categories:
- 1K<n<10K
SEA Toxicity Detection
SEA Toxicity Detection evaluates a model's ability to identify toxic content such as hate speech and abusive language in text. It is sampled from MLHSD for Indonesian, TTD for Thai, and ViHSD for Vietnamese.
Supported Tasks and Leaderboards
SEA Toxicity Detection is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the SEA-HELM leaderboard from AI Singapore.
Languages
- Indonesian (id)
- Thai (th)
- Vietnamese (vi)
Dataset Details
SEA Toxicity Detection is split by language, with additional splits containing fewshot examples. Below are the statistics for this dataset. The number of tokens only refer to the strings of text found within the prompts column.
| Split | # of examples | # of GPT-4o tokens | # of Gemma 2 tokens | # of Llama 3 tokens |
|---|---|---|---|---|
| id | 1000 | 34416 | 34238 | 40537 |
| th | 1000 | 38189 | 35980 | 42901 |
| vi | 1000 | 17540 | 16904 | 18287 |
| id_fewshot | 5 | 183 | 174 | 216 |
| th_fewshot | 5 | 130 | 121 | 150 |
| vi_fewshot | 5 | 104 | 97 | 104 |
| total | 3015 | 90562 | 87514 | 102195 |
Data Sources
| Data Source | License | Language/s | Split/s |
|---|---|---|---|
| MLHSD | CC BY-NC-SA 4.0 | Indonesian | id, id_fewshot |
| TTD | CC BY-NC 3.0 | Thai | th, th_fewshot |
| ViHSD | CC BY-NC 4.0 | Vietnamese | vi, vi_fewshot |
License
For the license/s of the dataset/s, please refer to the data sources table above.
We endeavor to ensure data used is permissible and have chosen datasets from creators who have processes to exclude copyrighted or disputed data.
Acknowledgement
This project is supported by the National Research Foundation Singapore and Infocomm Media Development Authority (IMDA), Singapore under its National Large Language Model Funding Initiative.
References
@inproceedings{ibrohim-budi-2019-multi,
title = "Multi-label Hate Speech and Abusive Language Detection in {I}ndonesian {T}witter",
author = "Ibrohim, Muhammad Okky and
Budi, Indra",
editor = "Roberts, Sarah T. and
Tetreault, Joel and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3506",
doi = "10.18653/v1/W19-3506",
pages = "46--57",
}
@inproceedings{sirihattasak2018annotation,
title={Annotation and classification of toxicity for Thai Twitter},
author={Sirihattasak, Sugan and Komachi, Mamoru and Ishikawa, Hiroshi},
booktitle={TA-COS 2018: 2nd Workshop on Text Analytics for Cybersecurity and Online Safety},
pages={1},
year={2018}
}
@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",
isbn="978-3-030-79457-6"
}
@misc{leong2023bhasaholisticsoutheastasian,
title={BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models},
author={Wei Qi Leong and Jian Gang Ngui and Yosephine Susanto and Hamsawardhini Rengarajan and Kengatharaiyer Sarveswaran and William Chandra Tjhi},
year={2023},
eprint={2309.06085},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.06085},
}