README.md

---
language:
- ky
license: mit
library_name: transformers
pipeline_tag: text-generation
base_model: google/mt5-small
tags:
- mt5
- text-normalization
- kyrgyz
- low-resource
- turkic
- continual-pretraining
datasets:
- Zarinaaa/kyrgyz-text-normalization
metrics:
- cer
- wer
- exact_match
---

# mT5-small with continual pre-training + fine-tuning for Kyrgyz text normalization

`google/mt5-small` continually pre-trained on a 538 MB Kyrgyz corpus (news portals + books) with T5-style span corruption, then fine-tuned on 1.67M noisy–clean text pairs for Kyrgyz text normalization.

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

**Note on choice between the two variants:** in our experiments the additional continual pre-training step did **not** improve over direct fine-tuning (CER 0.0825 vs. 0.0796, p = 0.06). The main observable difference is a higher rate of hallucination (input repetition) in failure cases. For most users we recommend the fine-tune-only variant unless you specifically want the slightly better Digit–Word category performance (see Evaluation below).

## Usage

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "Zarinaaa/mt5-small-kyrgyz-normalization-ptft"
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))
```

The prefix `"correct: "` is required.

## Training procedure

### Stage 1 — Continual pre-training

- **Corpus:** 538 MB clean Kyrgyz text from news portals and books
- **Objective:** T5-style span corruption (mask rate 0.15, mean span length 3)
- **Epochs:** 3
- **Train/validation split:** 98 / 2, seed 42; best checkpoint by validation loss

### Stage 2 — Fine-tuning

Identical to the fine-tune-only variant:

- **Effective batch size:** 64 (4 × 16 gradient accumulation)
- **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)

## Evaluation

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

| Metric | Value |
|---|---|
| CER | 0.0825 ± 0.004 |
| WER | 0.2017 |
| Exact Match | 0.184 |

Vs. fine-tune-only (CER 0.0796): paired bootstrap two-sided p = 0.06. We treat this as **insufficient evidence to reject the null** of no difference, **not** as equivalence — n = 1,000 is underpowered for detecting small effects in either direction.

Human evaluation (200 examples, 2 native annotators): **99.8%** rated correct (Wilson 95% CI [0.986, 0.9996]); PABAK = 0.990, Gwet's AC1 = 0.995 — identical to the fine-tune-only variant at the ceiling.

### Per-category CER

| Category | N | FT-only | **PT+FT** |
|---|---|---|---|
| Punctuation | 849 | **0.078** | 0.081 |
| Capitalization | 62 | **0.084** | 0.085 |
| All-caps | 39 | 0.084 | **0.083** |
| Digit–Word | 41 | 0.076 | **0.067** |

PT+FT is numerically slightly better on Digit–Word compounds; with N = 41 we do not treat this as a robust advantage.

### Failure analysis

In 40 examples where FT outperforms PT+FT by more than 0.05 CER, **hallucination (input repetition) is the dominant error mode (35/40 = 87.5%, 95% Wilson CI [74%, 95%])**. Two non-exclusive hypotheses (see paper §6.1):

1. **Copy bias from span corruption** — T5-style span corruption trains the decoder to reconstruct spans of the input verbatim, which may reinforce copying behavior harmful for normalization (where the target is usually not a superset of the input).
2. **Register mismatch** — continual pre-training used clean, formal text (news/books), while fine-tuning targets normalize noisy informal social-media text. The register gap may push the model toward fluent formal continuations that read as hallucinations.

## Limitations

Same as the fine-tune-only variant, plus:

- **Higher hallucination rate** in failure cases — if you need maximum robustness, use the FT-only variant.
- **No measurable benefit from the additional pre-training** at this scale and corpus composition; results suggest a more targeted continual objective (in-domain noisy text, denoising closer to the normalization target) would be needed.

## Citation

```bibtex
@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](https://github.com/Zarina33/Kyrgyz-Text-Normalization-Conference).