| --- |
| language: th |
| license: other |
| task_categories: |
| - token-classification |
| pretty_name: khammoon data (LLM-labelled training text, human-labelled dev and test sets) |
| configs: |
| - config_name: llm_labelled |
| data_files: |
| - split: train |
| path: llm_labelled/*.jsonl |
| - config_name: dev |
| data_files: |
| - split: validation |
| path: dev/best2009.jsonl |
| - config_name: test |
| data_files: |
| - split: test |
| path: test/wangchan.jsonl |
| - config_name: llm_outputs |
| data_files: |
| - split: dev |
| path: llm_outputs/best2009.jsonl |
| - split: test |
| path: llm_outputs/wangchan.jsonl |
| - config_name: annotator_agreement |
| data_files: |
| - split: best2009 |
| path: annotator_agreement/best2009.jsonl |
| - split: wangchan |
| path: annotator_agreement/wangchan.jsonl |
| --- |
| |
| # khammoon data |
|
|
| Data used to build the [`typhoon-ai/khammoon`](https://huggingface.co/typhoon-ai/khammoon) Thai morpheme (คำมูล) segmenter, |
| described in the technical report [arXiv:2611.13012](https://arxiv.org/abs/2611.13012). |
|
|
| | config | rows | labels | role | |
| |---|---|---|---| |
| | `llm_labelled` | 147,999 BEST2009-train sentences + 32,207 WangchanThaiInstruct-train documents | LLM segmentation (Qwen3-30B-A3B) corrected in two rounds: (1) a convention table learnt from dev-train of the development set plus guideline Step-2 span merges; (2) Thai–Thai boundaries re-predicted by a model fine-tuned on dev-train (other boundaries kept from round 1) | training | |
| | `dev` | 2,252 BEST2009 test sentences | human (3 annotators, adjudicated) | development: `dev_split` = `devtrain` (1,484; label correction + fine-tuning) / `devval` (768; model & threshold selection) | |
| | `test` | 2,000 WangchanThaiInstruct test documents | human (3 annotators, adjudicated) | evaluation only — never used during development | |
| | `llm_outputs` | the `dev` and `test` texts | segmentation by the LLM segmenter that produced the LLM labels (Qwen3-30B-A3B-Instruct-2507, two-pass prompt v6-3, pass-1 T=0.2) | baseline: P 0.975 / R 0.948 / F1 0.962 on `test` | |
| | `annotator_agreement` | 400 `dev` sentences + 400 `test` documents | the independent segmentation of each of the three annotators (`tokens_a`, `tokens_b`, `tokens_c`), before adjudication | inter-annotator agreement: Fleiss' κ 0.986 (BEST2009) / 0.991 (Wangchan) | |
|
|
| The annotation guideline that defines a morpheme (คำมูล) for all labels in this dataset (human and |
| LLM) is included as [`guideline/linguistic_guideline.md`](guideline/linguistic_guideline.md); its |
| maintained copy lives in the code repository under `linguistics/`. |
|
|
| Every row satisfies `"".join(tokens) == text`; whitespace characters are kept as tokens. |
| Every row has a `license` field (see below). `llm_labelled` rows also keep the original LLM |
| segmentation in `tokens_llm` (before correction), so the whole pipeline can be rerun from this |
| dataset (`scripts/download_data.py` in the code repository). `test` rows also have `source_ids`, the |
| WangchanThaiInstruct `ID`(s) of the document (14 documents occur under more than one ID). |
| Training rows whose text also appears in either human set were removed. |
|
|
| `annotator_agreement` rows hold the three annotators' independent segmentations of the same |
| sentence, labelled A, B and C as in the technical report, plus `reference_id`, the id of the same |
| sentence in `dev` or `test` whose `tokens` are the adjudicated segmentation. The annotation tool |
| dropped newline characters, so `text` here can differ from the reference text in whitespace only; |
| all three token lists join back to `text` exactly. `scripts/annotator_agreement.py` in the code |
| repository recomputes the agreement figures of the report from this configuration. |
|
|
| ## Intended uses |
|
|
| This dataset is released in full so that developers and researchers can build on it: |
|
|
| - **Train new segmenters.** `llm_labelled` gives 70M characters of training text with both the corrected |
| labels (`tokens`) and the original LLM labels (`tokens_llm`). |
| - **Benchmark.** `dev` and `test` are human-labelled; keep `test` for final |
| evaluation only, as the khammoon results do, so numbers stay comparable. |
| - **Study how native speakers disagree.** `annotator_agreement` gives three independent |
| segmentations of 800 sentences, the raw material behind the agreement figures. |
| - **Compare with an LLM annotator.** `llm_outputs` reproduces the strongest baseline without a GPU, |
| and the difference between `tokens_llm` and `tokens` shows where the LLM goes wrong. |
| - **Research Thai morphology and tokenization**, e.g. morpheme-aware tokenizers for language models. |
|
|
| ## Evaluation |
|
|
| Recommended metric: boundary precision/recall/F1 ignoring boundaries adjacent to whitespace |
| (see `khammoon/metrics.py`). khammoon 0.1.0 scores P 0.992 / R 0.993 / F1 0.992 on `test`. |
|
|
| ## License |
|
|
| The texts come from two sources and keep their licenses **per row** (`license` field). Because |
| the sources mix NonCommercial and ShareAlike terms, there is no single license for the whole |
| dataset; filter on `license` if you need a subset with specific terms. |
|
|
| | rows | `license` | source and attribution | |
| |---|---|---| |
| | `llm_labelled/best2009`, `dev` | `cc-by-nc-sa-3.0` | BEST2009 corpus by NECTEC ([nectec/best2009](https://huggingface.co/datasets/nectec/best2009)) | |
| | `llm_labelled/wangchan` (27,193 rows) | `cc-by-nc-4.0` | WangchanThaiInstruct by VISTEC/AIResearch ([airesearch/WangchanThaiInstruct](https://huggingface.co/datasets/airesearch/WangchanThaiInstruct)), per-row `License` of the source | |
| | `llm_labelled/wangchan` (5,014 rows) | `cc-by-sa-4.0` | same | |
| | `test` | `cc-by-nc-4.0` | WangchanThaiInstruct test split, same | |
| | `annotator_agreement/best2009` | `cc-by-nc-sa-3.0` | BEST2009, as `dev` | |
| | `annotator_agreement/wangchan` | `cc-by-nc-4.0` | WangchanThaiInstruct test split, as `test` | |
|
|
| The segmentation labels added here (human labels in `dev`/`test`, corrected LLM |
| labels in `llm_labelled`) are released under the same terms as the text of each row. None of the |
| data may be used commercially except the `cc-by-sa-4.0` rows. The LLM labels were produced with |
| Qwen3-30B-A3B-Instruct-2507 (Apache-2.0). |
|
|
| Please cite BEST2009 / WangchanThaiInstruct when using the corresponding rows: |
|
|
| ``` |
| @inproceedings{limkonchotiwat2025thaiinstruct, |
| title = {WangchanThaiInstruct: An Instruction-Following Dataset for Culture-Aware, Multitask, and Multi-domain Evaluation in Thai}, |
| author = {Limkonchotiwat, Peerat and Tuchinda, Pume and Lowphansirikul, Lalita and Nonesung, Surapon and Tasawong, Panuthep and Aji, Alham Fikri and Udomcharoenchaikit, Can and Nutanong, Sarana}, |
| booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing}, |
| year = {2025}, |
| publisher = {Association for Computational Linguistics} |
| } |
| ``` |
|
|
