---
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:
  features:
  - name: label
    dtype: string
  - name: prompts
    list:
    - name: text
      dtype: string
  - name: prompt_templates
    sequence: string
  - name: id
    dtype: string
  - name: metadata
    struct:
    - name: language
      dtype: string
  splits:
  - name: id
    num_bytes: 728212
    num_examples: 1000
    num_tokens_gpt_4o: 34416
    num_tokens_gemma_2: 34238
    num_tokens_llama_3: 40537
  - name: id_fewshot
    num_bytes: 780
    num_examples: 5
    num_tokens_gpt_4o: 183
    num_tokens_gemma_2: 174
    num_tokens_llama_3: 216
  - name: th
    num_bytes: 1356637
    num_examples: 1000
    num_tokens_gpt_4o: 38189
    num_tokens_gemma_2: 35980
    num_tokens_llama_3: 42901
  - name: th_fewshot
    num_bytes: 983
    num_examples: 5
    num_tokens_gpt_4o: 130
    num_tokens_gemma_2: 121
    num_tokens_llama_3: 150
  - name: vi
    num_bytes: 699612
    num_examples: 1000
    num_tokens_gpt_4o: 17540
    num_tokens_gemma_2: 16904
    num_tokens_llama_3: 18287
  - name: vi_fewshot
    num_bytes: 584
    num_examples: 5
    num_tokens_gpt_4o: 104
    num_tokens_gemma_2: 97
    num_tokens_llama_3: 104
  download_size: 283080
  dataset_size: 2786808
  total_tokens_gpt_4o: 90562
  total_tokens_gemma_2: 87514
  total_tokens_llama_3: 102195
configs:
- config_name: default
  data_files:
  - split: id
    path: data/id-*
  - split: id_fewshot
    path: data/id_fewshot-*
  - split: th
    path: data/th-*
  - split: th_fewshot
    path: data/th_fewshot-*
  - split: vi
    path: data/vi-*
  - split: vi_fewshot
    path: data/vi_fewshot-*
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](https://aclanthology.org/W19-3506/) for Indonesian, [TTD](http://lrec-conf.org/workshops/lrec2018/W32/pdf/1_W32.pdf) for Thai, and [ViHSD](https://link.springer.com/chapter/10.1007/978-3-030-79457-6_35) 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](https://leaderboard.sea-lion.ai/) leaderboard from [AI Singapore](https://aisingapore.org/).

### 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](https://github.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection) | [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) | Indonesian | id, id_fewshot
| [TTD](https://huggingface.co/datasets/tmu-nlp/thai_toxicity_tweet) | [CC BY-NC 3.0](https://creativecommons.org/licenses/by-nc/3.0/) | Thai |th, th_fewshot
| [ViHSD](https://github.com/sonlam1102/vihsd) | [CC BY-NC 4.0](https://creativecommons.org/licenses/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. 


### References

```bibtex
@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}, 
}
```