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FacebookAI/xlm-roberta-base as its base encoder and is trained on UD_Korean-GSD (UD v2.17).| Metric | Tokens | Sentences | Words | UPOS | XPOS | UFeats | AllTags | Lemmas |
|---|---|---|---|---|---|---|---|---|
| Full-text (F1) | 99.86 | 98.28 | 99.88 | 96.91 | 91.29 | 99.62 | 89.24 | 93.24 |
| Aligned accuracy | 0.00 | 0.00 | 0.00 | 97.02 | 91.40 | 99.73 | 89.35 | 93.35 |
| Metric | UAS | LAS | CLAS | MLAS | BLEX |
|---|---|---|---|---|---|
| Full-text (F1) | 88.88 | 85.78 | 84.61 | 82.77 | 78.21 |
| Aligned accuracy | 88.98 | 85.88 | 84.66 | 82.81 | 78.25 |
pip install combo-nlpcombo-nlp, so
no extra install step is needed.1from combo import COMBO
2
3# Load a pre-trained model with the corresponding combo-seg segmenter
4nlp = COMBO("Korean")
5
6# Parse raw text (handles sentence splitting + tokenization)
7result = nlp("빠른 갈색 여우가 게으른 개를 뛰어넘는다.")
8
9# Inspect results
10for sentence in result:
11 for token in sentence:
12 print(f"{token.form:<15} {token.lemma:<15} {token.upos:<8} head={token.head} {token.deprel}")LICENSE.txt file in the treebank repository:1@software{combo_nlp_2026,
2 author = {Ulewicz, Michał and Jabłońska, Maja and Klimaszewski, Mateusz and Przybyła, Piotr and Pszenny, Łukasz and Rybak, Piotr and Wiącek, Martyna and Wróblewska, Alina},
3 title = {{COMBO-NLP} Models Trained on {UD} v2.17},
4 year = {2026},
5 publisher = {Zenodo},
6 doi = {10.5281/zenodo.19650523},
7 url = {https://doi.org/10.5281/zenodo.19650523}
8}