---
license: odc-by
task_categories:
- text-generation
- fill-mask
language:
- th
tags:
- finance
- legal
- medical
pretty_name: Magostreen
size_categories:
- 10M<n<100M
---

## Citation
```
@misc{phatthiyaphaibun2025mangosteenopenthaicorpus,
      title={Mangosteen: An Open Thai Corpus for Language Model Pretraining}, 
      author={Wannaphong Phatthiyaphaibun and Can Udomcharoenchaikit and Pakpoom Singkorapoom and Kunat Pipatanakul and Ekapol Chuangsuwanich and Peerat Limkonchotiwat and Sarana Nutanong},
      year={2025},
      eprint={2507.14664},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.14664}, 
}
```

We have collected additional Thai text that is unlikely to be included in the common crawl from various sources. The total number of documents collected is as follows:425,304 documents, we deduplication these noncc documents to later divide them into train sets for further web data and validation set.

We include Common Crawl and Fineweb2 as follows:

| Source             | Documents | Tokens (B) |
|--------------------|:---------:|:----------:|
| CC-Derived Dataset |   29.7 M  |    45.9    |

We also propose a new data cleaning pipeline to improve and filter out the low-quality data. We adopt the data collection of Dolma by applying five major components:
- Language identity: Instead of relying on FastTex as a language identifier, as in Dolma, we use a rule-based approach for Thai script, which is more efficient in terms of performance and speed.
- Deduplication by URL: We use the Bloom filter to remove duplicate data.
- Quality Filters: For this step, we still use the same practice as in the original dolma by using C4 and Gopher rules. However, we made changes to make it more compatible with the Thai language by investigating and then changing the rules.
- Content Filters: We also update the content filter by upgrading the filter to remove not-safe-for-work (NSFW), phone number, and gambling content for Thai more efficiently than the existing filters. 
- Deduplication on text overlap: We also use the Bloom filter that is used in the Dolma pipeline to remove the text that overlaps in our corpus.

Resources
- Pre-training data (web): https://huggingface.co/datasets/aisingapore/WangchanLION-Web 
- Pre-training data (curated): https://huggingface.co/datasets/aisingapore/WangchanLION-Curated 
- Pre-training model: https://huggingface.co/aisingapore/WangchanLION-v3 
- SFT model: https://huggingface.co/aisingapore/WangchanLION-v3-IT
- Paper: https://arxiv.org/abs/2507.14664
- Blog: https://sea-lion.ai/sea-lion-wangchanlionv3/
- Github: https://github.com/vistec-AI/Mangosteen