UZ-ByT5 is a fine-tuned byte-level sequence-to-sequence model for explainable Uzbek morpheme analysis. Given a surface word, it restores the lexical root and labels each morpheme as a root, derivational, lexical-forming or syntactic-forming unit.

The complete hybrid application, rule-based fallback and evaluation code are available at uzbtrust/uzbek-morpheme-analyzer.

Output format

The model generates space-separated form=TAG tokens:

Tag	Meaning
`A`	root / `asos`
`D`	derivational morpheme / `so‘z yasovchi`
`L`	lexical-forming morpheme / `lug‘aviy shakl yasovchi`
`S`	syntactic-forming morpheme / `sintaktik shakl yasovchi`

Examples:

text

o‘g‘limda       → o‘g‘il=A im=S da=S
yozdirilgan     → yoz=A dir=D il=L gan=L
kitoblarimizdan → kitob=A lar=L imiz=S dan=S

Usage

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

repo_id = "uzbtrust/uzbek-morpheme-byt5"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSeq2SeqLM.from_pretrained(repo_id).eval()

word = "kitoblarimizdan"
inputs = tokenizer([word], return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=96)
print(tokenizer.batch_decode(output, skip_special_tokens=True)[0])

For production use, validate that generated parts reconstruct the source word and combine the model with the rule-based fallback in the GitHub project.

Training

Base model: google/byt5-small
Task: surface word → labeled morpheme sequence
Training set: approximately 246K generated Uzbek forms
Validation: 3,000 held-out generated forms
Compute: Kaggle T4 GPU
Epochs: 8
Precision: fp32

The generator applies forward Uzbek morphophonological rules, including consonant alternation, vowel deletion and consonant insertion. This allows the model to learn inverse restoration such as:

text

o‘g‘lim → o‘g‘il
shahri  → shahar
eshigi  → eshik
tuprog‘i → tuproq

Evaluation

The best validation exact-match score recorded during training was 98.77% on the 3,000 generated held-out forms.

This is a synthetic-data validation score, not a universal benchmark for Uzbek. The accompanying project also includes 28 manually curated regression words and 31 unit tests for its hybrid inference pipeline.

Intended use

Suitable for:

Uzbek linguistic education and explainable word analysis;
morphology-aware NLP prototypes;
lexicographic or annotation assistance;
research on low-resource Turkic morphology.

The model should not be treated as an authoritative linguistic source without human review.

Limitations

trained primarily on generated isolated words rather than running text;
ambiguous suffixes may require sentence context;
proper names, new borrowings and dialectal forms may be out of distribution;
the raw output provides coarse morpheme classes; the application adds grammatical function, descriptions and examples deterministically.

License and attribution

The checkpoint is derived from google/byt5-small, distributed under Apache License 2.0. Training vocabulary and runtime resources include separately licensed third-party sources; see the project notices.