---
license: mit
base_model: coppercitylabs/uzbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: uzpostagger-cyrillic-3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# uzpostagger-cyrillic-3

This model is a fine-tuned version of [coppercitylabs/uzbert-base-uncased](https://huggingface.co/coppercitylabs/uzbert-base-uncased) on [uzbekpos](https://huggingface.co/datasets/latofat/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

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 25   | 0.8765          | 0.6558    | 0.5477 | 0.5969 | 0.7485   |
| No log        | 2.0   | 50   | 0.4086          | 0.8496    | 0.8237 | 0.8364 | 0.9004   |
| No log        | 3.0   | 75   | 0.3133          | 0.8615    | 0.8552 | 0.8583 | 0.9142   |
| No log        | 4.0   | 100  | 0.2806          | 0.8730    | 0.8657 | 0.8693 | 0.9193   |
| No log        | 5.0   | 125  | 0.2715          | 0.8763    | 0.8699 | 0.8731 | 0.9219   |


### 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."
}
```