---
library_name: transformers
license: mit
language:
- ky
---

# Model Card for Kyrgyz BERT Tokenizer


This is a WordPiece-based BERT tokenizer trained specifically for the Kyrgyz language. It was developed to support Kyrgyz NLP applications, including text classification, translation, and morphological analysis. The tokenizer was trained on a large corpus from various Kyrgyz text sources.


## Model Details

### Model Description

  - **Developed by:** Metinov Adilet
- **Funded by :** Self-funded(MetinLab)
- **Shared by :** metinovadilet
- **Model type:** WordPiece Tokenizer (BERT-style)
- **Language(s) (NLP):** Kyrgyz (ky)
- **License:** MIT
- **Finetuned from model [optional]:** N/A (trained from scratch)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** metinovadilet/bert-kyrgyz-tokenizer
- **Paper [optional]:** N/A
- **Demo [optional]:** N/A

## Uses

### Direct Use
This tokenizer can be used directly for NLP tasks such as:

- Tokenizing Kyrgyz texts for training language models

- Preparing data for Kyrgyz BERT training or fine-tuning

- Kyrgyz text segmentation and wordpiece-based analysis

### Downstream Use [optional]

- Can be used as the tokenizer for BERT-based models trained on Kyrgyz text

- Supports various NLP applications like sentiment analysis, morphological modeling, and machine translation

### Out-of-Scope Use

- This tokenizer is not optimized for multilingual text. It is designed for Kyrgyz-only corpora.

- It may not work well for transliterated or mixed-script text (e.g., combining Latin and Cyrillic scripts).

## Bias, Risks, and Limitations

- The tokenizer is limited by the training corpus, meaning rare words, dialectal forms, and domain-specific terms may not be well-represented.

- As with most tokenizers, it may exhibit biases from the source text, particularly in areas of gender, ethnicity, or socio-political context.

### Recommendations

Users should be aware of potential biases and evaluate performance for their specific application. If biases or inefficiencies are found, fine-tuning or training with a more diverse corpus is recommended.

## How to Get Started with the Model

Use the code below to get started with the model.
```
from transformers import BertTokenizerFast

tokenizer = BertTokenizerFast.from_pretrained("metinovadilet/bert-kyrgyz-tokenizer")

text = "Бул кыргыз тилинде жазылган текст."

tokens = tokenizer(text, return_offsets_mapping=True)

print("Input Text:", text)
print("Tokens:", tokenizer.convert_ids_to_tokens(tokens['input_ids']))
print("Token IDs:", tokens['input_ids'])
print("Offsets:", tokens['offset_mapping'])
```
## Training Details and Training Data

Non disclosable

## Environmental Impact


Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** NVIDIA RTX 3090
- **Hours used:** ~1 hour
- **Compute Region:** Central Asia
- **Carbon Emitted:** ~0.1 kg CO2
## Technical Specifications

### Model Architecture and Objective

- Architecture: WordPiece-based BERT tokenizer

- Objective: Efficient tokenization for Kyrgyz NLP applications

### Compute Infrastructure

[More Information Needed]

#### Hardware

- GPU: NVIDIA RTX 3090 (24GB VRAM)
- CPU: intel core i5-13400f

#### Software

- Python 3.10

- Transformers (Hugging Face)

- Tokenizers (Hugging Face)

## Citation [optional]

If you use this tokenizer, please cite:
```
@misc{bert-kyrgyz-tokenizer,
  author = {Metinov Adilet},
  title = {BERT Kyrgyz Tokenizer},
  year = {2025},
  url = {https://huggingface.co/metinovadilet/bert-kyrgyz-tokenizer},
  note = {Trained at MetinLab}
}
```
## Model Card Contact

For questions or issues, reach out to MetinLab via:

Email: metinovadilet@gmail.com

## This model was made in Collaboration with UlutsoftLLC