mT5-small fine-tuned for Kyrgyz text normalization

Fine-tuned google/mt5-small for normalizing noisy Kyrgyz social-media text (YouTube comments, Instagram posts, Telegram messages) into a standardized form — punctuation, capitalization, dialectal spelling, digit–word compounds.

This is the fine-tuned only variant from the camera-ready paper "Kyrgyz Text Normalization: A Comparative Study of Neural and Rule-Based Approaches" (MeLLM Workshop @ ACL 2026). For the continual pre-training + fine-tuning variant see Zarinaaa/mt5-small-kyrgyz-normalization-ptft.

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "Zarinaaa/mt5-small-kyrgyz-normalization"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

noisy = "барды жакшы болсун коркунучту жерлерди тазалаш керек"
inputs = tokenizer("correct: " + noisy, return_tensors="pt", truncation=True, max_length=256)
out = model.generate(**inputs, max_new_tokens=256, num_beams=4)
print(tokenizer.decode(out[0], skip_special_tokens=True))
# Барды жакшы болсун. Коркунучтуу жерлерди тазалаш керек.

The prefix "correct: " is required — the model was fine-tuned with this exact prompt.

Training data

1.67M noisy–clean Kyrgyz text pairs from YouTube (45%), Instagram (25%), and Telegram (30%), automatically annotated with Gemini 3 Pro and spot-checked on a 400-example sample (84% acceptance rate, 95% Wilson CI [80%, 87%]). The 1,000-example test set was fully verified by two native Kyrgyz speakers with adjudication.

A 20,000-pair subset of the training data and the full test set are released at Zarinaaa/kyrgyz-text-normalization.

Training procedure

• Base model: google/mt5-small (300M parameters)
• Effective batch size: 64 (physical batch 4 × gradient accumulation 16)
• Learning rate: 3e-4, cosine schedule, 500 warmup steps
• Epochs: 5
• Max sequence length: 256
• Train/validation split: 95 / 5, seed 42; best checkpoint by validation loss
• Hardware: 1× NVIDIA RTX 5080 (16 GB VRAM)

The 1,000 test inputs are disjoint from the 1.67M training set (verified 0/1,000 exact-match overlap and 0/1,000 case-insensitive overlap).

Evaluation

Automatic metrics on the held-out 1,000-example test set:

For comparison: rule-based baseline 0.2029 CER, zero-shot Gemma 4 (9.6B, 32× larger) 0.1620 CER.

Human evaluation by two native Kyrgyz speakers on 200 examples: 99.8% rated correct (Wilson 95% CI [0.986, 0.9996]). Reliability under prevalence skew: PABAK = 0.990, Gwet's AC1 = 0.995. Of the 199 outputs both annotators rated correct, 162 (81.4%) differ from the Gemini reference at the character level — surface-form variability that EM penalizes but native speakers accept.

Per-category CER

Limitations

• Domain: trained and evaluated on social-media text. Performance on news, speech transcripts, or formal government text is not guaranteed.
• Reference bias: training references were produced by Gemini 3 Pro; a probe with an independent annotator shows the model has learned a general normalization function (CER changes by only 0.012 against an independent reference), but residual stylistic bias is possible.
• Label noise: ~16% of training pairs may contain minor issues per the 400-example spot-check.
• Model size: larger variants (mT5-base/large, ByT5) and fine-tuned LLMs were not evaluated due to compute constraints.
• Rule-based comparison: the baseline in the paper is intentionally minimal; a stronger Kyrgyz FST-based pipeline would likely close part of the gap.

Citation

@inproceedings{uvalieva2026kyrgyz,
  title={Kyrgyz Text Normalization: A Comparative Study of Neural and Rule-Based Approaches},
  author={Uvalieva, Zarina and Kumarbai uulu, Bektemir and Metinov, Adilet and Tashbaltaev, Tynchtykbek and Alibekov, Nurtilek},
  booktitle={Proceedings of the MeLLM Workshop at ACL 2026},
  year={2026}
}

License

MIT. Code: github.com/Zarina33/Kyrgyz-Text-Normalization-Conference.