uzpostagger-cyrillic-3

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uzpostagger-cyrillic-3

This model is a fine-tuned version of coppercitylabs/uzbert-base-uncased on uzbekpos dataset. It achieves the following results on the evaluation set:

• Loss: 0.2715
• Precision: 0.8763
• Recall: 0.8699
• F1: 0.8731
• Accuracy: 0.9219

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.32.1
• Pytorch 2.2.0
• Datasets 2.17.1
• Tokenizers 0.13.3

Citation Information

@inproceedings{bobojonova-etal-2025-bbpos,
    title = "{BBPOS}: {BERT}-based Part-of-Speech Tagging for {U}zbek",
    author = "Bobojonova, Latofat  and
      Akhundjanova, Arofat  and
      Ostheimer, Phil Sidney  and
      Fellenz, Sophie",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the First Workshop on Language Models for Low-Resource Languages",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.loreslm-1.23/",
    pages = "287--293",
    abstract = "This paper advances NLP research for the low-resource Uzbek language by evaluating two previously untested monolingual Uzbek BERT models on the part-of-speech (POS) tagging task and introducing the first publicly available UPOS-tagged benchmark dataset for Uzbek. Our fine-tuned models achieve 91{\%} average accuracy, outperforming the baseline multi-lingual BERT as well as the rule-based tagger. Notably, these models capture intermediate POS changes through affixes and demonstrate context sensitivity, unlike existing rule-based taggers."
}