---
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}
}
```