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clarin-pl/
combo-nlp-xlm-roberta-base-uyghur-udt-ud2.17

Name: combo-nlp-xlm-roberta-base-uyghur-udt-ud2.17
Availability: InStock

pytorch

dependency-parsing

combo

universal-dependencies

token-classification

universal_dependencies

cc-by-sa-4.0

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COMBO-NLP Model for Uyghur

Model Description

This is a Uyghur-language model based on combo-nlp, an open-source natural language preprocessing system. It performs:

sentence segmentation (via combo-seg)
tokenisation (via combo-seg)
part-of-speech tagging
morphological analysis
lemmatisation
dependency parsing

The Uyghur model uses FacebookAI/xlm-roberta-base as its base encoder and is trained on UD_Uyghur-UDT (UD v2.17).

Evaluation

Evaluation was performed on the UD_Uyghur-UDT test split using the standard CoNLL 2018 eval script.

Two evaluation rows are reported:

Full-text (F1): raw text is segmented by combo-seg, then parsed and compared against gold — measures end-to-end pipeline performance including segmentation quality.
Aligned accuracy: accuracy on correctly segmented (aligned) tokens — measures parsing quality on tokens that were correctly identified by the segmenter.

Morphosyntactic Tagging

Metric	Tokens	Sentences	Words	UPOS	XPOS	UFeats	AllTags	Lemmas
Full-text (F1)	99.45	88.19	99.30	89.78	92.20	82.20	75.25	96.31
Aligned accuracy	0.00	0.00	0.00	90.42	92.86	82.79	75.79	97.00

Dependency Parsing

Metric	UAS	LAS	CLAS	MLAS	BLEX
Full-text (F1)	78.90	68.69	65.57	47.47	62.94
Aligned accuracy	79.46	69.18	66.26	47.96	63.59

Usage

Install the library from PyPI (assuming you have a virtual environment created):

pip install combo-nlp

The combo-seg segmenter (used to split and tokenise raw text) is installed automatically as a dependency of combo-nlp, so no extra install step is needed.

python

from combo import COMBO

# Load a pre-trained model with the corresponding combo-seg segmenter
nlp = COMBO("Uyghur")

# Parse raw text (handles sentence splitting + tokenization)
result = nlp("چاققان قوڭۇر تۈلكە ھۈرسەن ئىتنىڭ ئۈستىدىن سەكرەيدۇ.")

# Inspect results
for sentence in result:
    for token in sentence:
        print(f"{token.form:<15} {token.lemma:<15} {token.upos:<8} head={token.head}  {token.deprel}")

Refer to the combo-nlp documentation for installation and usage instructions:

License

The training data license: cc-by-sa-4.0 is derived from the Universal Dependencies treebank. For the full license terms of each treebank, please refer to the corresponding LICENSE.txt file in the treebank repository:

UD_Uyghur-UDT LICENSE.txt

Citation

If you use this model, please cite:

Ulewicz, M., Jabłońska, M., Klimaszewski, M., Przybyła, P., Pszenny, Ł., Rybak, P., Wiącek, M., & Wróblewska, A. (2026). COMBO-NLP Models Trained on UD v2.17. Zenodo. https://doi.org/10.5281/zenodo.19650523

bibtex

@software{combo_nlp_2026,
  author    = {Ulewicz, Michał and Jabłońska, Maja and Klimaszewski, Mateusz and Przybyła, Piotr and Pszenny, Łukasz and Rybak, Piotr and Wiącek, Martyna and Wróblewska, Alina},
  title     = {{COMBO-NLP} Models Trained on {UD} v2.17},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19650523},
  url       = {https://doi.org/10.5281/zenodo.19650523}
}

Resources

combo-nlp: https://pypi.org/project/combo-nlp/
combo-seg: https://pypi.org/project/combo-seg/
UD_Uyghur-UDT: https://github.com/UniversalDependencies/UD_Uyghur-UDT

clarin-pl/combo-nlp-xlm-roberta-base-uyghur-udt-ud2.17