| --- |
| 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](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. |
|
|
|
|
| ## 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 |
|
|
| ```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}, |
| } |
| ``` |
