README.md

metadata

license: apache-2.0
language:
  - kk
  - ru
  - en
base_model:
  - google-bert/bert-base-uncased
pipeline_tag: fill-mask
tags:
  - pytorch
  - safetensors
library_name: transformers
paper: https://doi.org/10.5281/zenodo.15565394
datasets:
  - amandyk/kazakh_wiki_articles
  - Eraly-ml/kk-cc-data
direct_use: true
widget:
  - text: KazBERT қазақ тілін [MASK] түсінеді.

KazBERT: A Custom BERT Model for the Kazakh Language 🇰🇿

License & Metadata

• License: apache-2.0
• Languages: Kazakh (kk), Russian (ru), English (en)
• Base Model: google-bert/bert-base-uncased
• Pipeline Tag: fill-mask
• Tags: pytorch, safetensors
• Library: transformers
• Datasets:
  • amandyk/kazakh_wiki_articles
  • Eraly-ml/kk-cc-data
• Direct Use: ✅
• Widget Example:
  "KazBERT қазақ тілін [MASK] түсінеді."

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Model Overview

KazBERT is a BERT-based model fine-tuned specifically for Kazakh using Masked Language Modeling (MLM). It is based on bert-base-uncased and uses a custom tokenizer trained on Kazakh text.

Model Details

• Architecture: BERT
• Tokenizer: WordPiece trained on Kazakh
• Training Data: Kazakh Wikipedia & Common Crawl
• Method: Masked Language Modeling (MLM)

Erlanulu, Y. G. (2025). KazBERT: A Custom BERT Model for the Kazakh Language. Zenodo.

📄 Read the paper

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Files in Repository

• config.json – Model config
• model.safetensors – Model weights
• tokenizer.json – Tokenizer data
• tokenizer_config.json – Tokenizer config
• special_tokens_map.json – Special tokens
• vocab.txt – Vocabulary

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Training Configuration

• Epochs: 20
• Batch size: 16
• Learning rate: Default
• Weight decay: 0.01
• FP16 Training: Enabled

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Usage

Install 🤗 Transformers and load the model:

from transformers import BertForMaskedLM, BertTokenizerFast

model_name = "Eraly-ml/KazBERT"
tokenizer = BertTokenizerFast.from_pretrained(model_name)
model = BertForMaskedLM.from_pretrained(model_name)

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Example: Masked Token Prediction

from transformers import pipeline

pipe = pipeline("fill-mask", model="Eraly-ml/KazBERT")
output = pipe('KazBERT қазақ тілін [MASK] түсінеді.')

Output:

[
  {"score": 0.198, "token_str": "жетік", "sequence": "KazBERT қазақ тілін жетік түсінеді."},
  {"score": 0.038, "token_str": "де", "sequence": "KazBERT қазақ тілін де түсінеді."},
  {"score": 0.032, "token_str": "терең", "sequence": "KazBERT қазақ тілін терең түсінеді."},
  {"score": 0.029, "token_str": "ерте", "sequence": "KazBERT қазақ тілін ерте түсінеді."},
  {"score": 0.026, "token_str": "жете", "sequence": "KazBERT қазақ тілін жете түсінеді."}
]

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Bias and Limitations

- Trained only on public Kazakh Wikipedia & Common Crawl
- Might miss informal speech or dialects
- Could underperform on deep-context or rare words
- May reflect cultural or social biases in data

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License

Apache 2.0 License

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Citation

@misc{erlanulu_2025_15565394,
  author       = {Erlanulu, Yeraly Gainulla},
  title        = {KazBERT: A Custom BERT Model for the Kazakh
                   Language
                  },
  month        = may,
  year         = 2025,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.15565394},
  url          = {https://doi.org/10.5281/zenodo.15565394},
}