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erdemKocaogluu / byt5-small-tr-normalizer
Model's Last Updated: September 21 2026
text-generation
Introduction of byt5-small-tr-normalizer
Model Details of byt5-small-tr-normalizer
byt5-small-tr-normalizer
A fine-tuned byT5-small model that rewrites informal, misspelled, or dialectal Turkish sentences into standard written Turkish.
Typical inputs are the kind of text people actually type in a hurry: dropped diacritics, missing letters, colloquial verb contractions, ASCII-only typing, and regional/dialectal spelling. The model rewrites these into a clean, standard-Turkish sentence while keeping the original meaning and word order.
sicil belgmi alcam -> Sicil belgimi alacağım
araç lsansı nasl alacaım -> araç lisansı nasıl alacağım
muhtara gitcem ne götüreyim -> Muhtara gideceğim ne götüreyim
Model description
-
Base model:
google/byt5-small - Tokenizer: byte-level (no vocabulary, no BPE merges) — every UTF-8 byte is a token, so the model reasons directly over characters. This makes it naturally robust to the kind of noisy, non-standard spelling this task targets.
-
Task:
sequence-to-sequence text normalization (
bozuk Türkçe -> standart Türkçe) -
Input format:
prefix the sentence with
"düzelt: "before feeding it to the model (see usage below).
How to use
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "erdemKocaogluu/byt5-small-tr-normalizer"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
text = "sicil belgmi alcam"
inputs = tokenizer("düzelt: " + text, return_tensors="pt")
output = model.generate(**inputs, max_length=384, num_beams=1)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Training data
Training pairs come from six public Turkish datasets that already provide noisy/clean sentence pairs (grammar correction, typo validation, text normalization — see Data sources below). Every source is permissively licensed and no data was synthetically generated for this training run.
Data sources
| Source | Author | Role | License |
|---|---|---|---|
| Turkish-OSCAR-GEC | asimokby | Main training corpus | MIT |
| Turkish-GPT-GEC | asimokby | LLM-generated correction pairs | MIT |
| turkish-text-normalization-1m | GoktugD | Large-scale text normalization pairs | CC0-1.0 |
| turkish_typo_validation | burakaytan | Typo-focused pairs, parsed separately | Apache-2.0 |
| trspell (NoisyWikiTr) | cgturhan | Wikipedia-derived noisy/clean pairs | MIT |
| noisy-sentences-dataset | radi-cho | Turkish subset of a multilingual noisy-sentence corpus | MIT |
Not every dataset that was evaluated made it into the final training mix — a diacritics-restoration dataset, for example, was tested but excluded after evaluation.
The MIT, Apache-2.0, and CC0-1.0 sources above are acknowledged here per their license terms; their original copyright notices and licenses remain with the linked repositories. This project does not redistribute the original dataset files — only sentence pairs derived from them were used to train the model.
Training procedure
During training, checkpoints were evaluated periodically on a held-out validation split and ranked with a combined selection score that balances two things: how well a checkpoint fixes noisy sentences, and how well it leaves already-correct sentences untouched (to discourage over-correction). The checkpoint used here is the highest-scoring one from that process, and its weights were verified by hash before being published.
Evaluation
Held-out gold sets (naturally occurring, human-written Turkish — never seen during training)
| Test set | n | WER (model) | WER (no correction) | F1 | Over-correction |
|---|---|---|---|---|---|
| tweets | 1,742 | 0.161 | 0.336 | 0.667 | 0.6% |
| boun | 507 | 0.093 | 0.129 | 0.590 | 4.3% |
The model roughly halves the word error rate compared to a "do nothing" baseline on both sets, while rarely damaging text that was already correct.
Sources:
- tweets — Köksal, A. T., Bozal, Ö., Yürekli, E., & Gezici, G. (2020). #Turki$hTweets: A Benchmark Dataset for Turkish Text Correction . Findings of ACL: EMNLP 2020 , pages 4190–4198.
- boun — Kara, A., Marouf Sofian, F., Bond, A., & Şahin, G. (2023). GECTurk: Grammatical Error Correction and Detection Dataset for Turkish . Findings of ACL: IJCNLP-AACL 2023 , pages 278–290.
Limitations
- The model generalizes very well to mechanical, deterministic corrections (like deasciification), where the rule mapping is unambiguous.
- It is comparatively weaker on organic, human-style noise that doesn't closely match the synthetic noise patterns it was trained on, and on long, multi-clause informal sentences.
- It corrects spelling, spacing, and word-internal errors; it does not reorder words or rewrite sentence structure.
- Inference is CPU-friendly but on Apple Silicon MPS the model can occasionally produce degenerate output; CPU or CUDA is recommended.
License
Apache 2.0 (same as the base model).
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Updated:September 21 2026