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 Thai morpheme (คำมูล) segmenter,
described in the technical report arXiv: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; 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_labelledgives 70M characters of training text with both the corrected labels (tokens) and the original LLM labels (tokens_llm). - Benchmark.
devandtestare human-labelled; keeptestfor final evaluation only, as the khammoon results do, so numbers stay comparable. - Study how native speakers disagree.
annotator_agreementgives three independent segmentations of 800 sentences, the raw material behind the agreement figures. - Compare with an LLM annotator.
llm_outputsreproduces the strongest baseline without a GPU, and the difference betweentokens_llmandtokensshows 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) |
llm_labelled/wangchan (27,193 rows) |
cc-by-nc-4.0 |
WangchanThaiInstruct by VISTEC/AIResearch (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}
}