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metinovadilet/
kyrgyz_t5_tokenizer

Name: kyrgyz_t5_tokenizer
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Kyrgyz T5 Tokenizer

Model Details

Model Description

This is a Unigram SentencePiece tokenizer . It follows the T5 tokenizer format and is designed for use with mT5, ByT5, or other T5 models fine-tuned for the Kyrgyz language.

Developed by: MetinLab
Funded by: Self-funded by MetinLab
Shared by: Metinov Adilet
Model type: Tokenizer (Unigram SentencePiece)
Language(s): Kyrgyz (ky)
License: Apache-2.0
Finetuned from: Custom SentencePiece model

Model Sources

Repository: Kyrgyz T5 Tokenizer on Hugging Face
Demo: Not available yet
Paper: N/A

Uses

Direct Use

This tokenizer is useful for Kyrgyz NLP tasks, including:

Text generation
Translation
Summarization
Question answering
Any task involving a T5-based model trained for Kyrgyz

Downstream Use

It is expected to be used for:

Training new T5/mT5 models specifically for Kyrgyz.
Preprocessing Kyrgyz datasets for model fine-tuning.

Out-of-Scope Use

It may not work well for informal, dialectal, or mixed-language texts that are not covered in the corpus.
It is not suitable for tokenizing non-Kyrgyz languages.

Bias, Risks, and Limitations

Bias

The tokenizer is trained on cleaned, formal Kyrgyz text. It may not represent the full diversity of Kyrgyz as spoken in different regions.

Risks

Over-segmentation of rare words due to the Unigram model.
Encoding errors in informal or non-standard text.
No direct handling of multilingual text, though mT5 can handle it.

Recommendations

Users should evaluate the tokenizer on their own datasets to ensure it works well for their use cases.

How to Get Started with the Tokenizer

You can load and use this tokenizer with the following code:

python

from transformers import AutoTokenizer

# Load the tokenizer from Hugging Face
tokenizer = AutoTokenizer.from_pretrained("metinovadilet/kyrgyz_t5_tokenizer")

# Example Kyrgyz sentence
text = "Кыргызстандагы илимий долбоорлор өнүгүп жатат."

# Tokenize the text
tokens = tokenizer(text, return_tensors="pt")

# Print token IDs
print("Token IDs:", tokens["input_ids"][0].tolist())

# Print individual tokenized words
decoded_tokens = [tokenizer.decode([tid]) for tid in tokens["input_ids"][0].tolist()]
print("Tokenized Words:", decoded_tokens)

# Print segmentation
print("\nWord Segmentation Breakdown:")
for token_id in tokens["input_ids"][0].tolist():
    token = tokenizer.decode([token_id])
    print(f"'{token}' -> ID: {token_id}")

Citation

If you use this tokenizer, please cite:

bibtex

@misc{metinovadilet2025kyrgyzT5tokenizer,
  title={Kyrgyz T5 Tokenizer},
  author={Metinov Adilet},
  year={2025},
  publisher={Hugging Face},
  url=https://huggingface.co/metinovadilet/kyrgyz_t5_tokenizer
}

More Information

Contact: metinovadilet@gmail.com

metinovadilet/kyrgyz_t5_tokenizer