kyrgyz_t5_tokenizer

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:

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:

@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: [email protected]

This model was made in Collaboration with UlutsoftLLC