Slovorez
English · Русский
Slovorez is a neural library for morpheme segmentation of the Russian language: it splits a word into prefix, root, suffix and ending. Inference runs on CPU via ONNX Runtime — no GPU and no heavy dependencies — while the lexer core is written in C++. A lightweight model (~0.69M parameters) makes it suitable for production and edge use.
Features
- Morpheme segmentation into classes: prefix, root, suffix, ending, postfix
- CPU inference via ONNX, no GPU required
- C++ tokenizer with pybind11 bindings
- Batch file processing with caching of already-segmented words
Quick start
from slovorez import Slovorez
model = Slovorez.from_pretrained("models/slovorez-test")
model.predict("Приставки и суффиксы выделяются автоматически.")
[{'word': 'приставки', 'morphemes': [Morpheme('при', PREF, 0.61), Morpheme('став', ROOT, 0.78), Morpheme('к', SUFF, 0.71), Morpheme('и', END, 0.96)]},
{'word': 'и', 'morphemes': [Morpheme('и', END, 0.54)]},
{'word': 'суффиксы', 'morphemes': [Morpheme('суффикс', ROOT, 0.82), Morpheme('ы', END, 0.95)]},
{'word': 'выделяются', 'morphemes': [Morpheme('вы', PREF, 0.93), Morpheme('дел', ROOT, 0.93), Morpheme('я', SUFF, 0.84), Morpheme('ют', SUFF, 0.79), Morpheme('ся', POSTFIX, 0.99)]},
{'word': 'автоматически', 'morphemes': [Morpheme('автомат', ROOT, 0.73), Morpheme('ическ', SUFF, 0.80), Morpheme('и', SUFF, 0.76)]}]
Each morpheme is a Morpheme(text, type, score).
Morpheme classes:
| class | meaning |
|---|---|
| PREF | prefix |
| ROOT | root |
| SUFF | suffix |
| END | ending (inflection) |
| POSTFIX | postfix |
| LINK | linking morpheme (interfix) |
| HYPH | hyphen |
You can pass text in any language — Slovorez extracts and segments only Russian words, preserving their order.
Decoding
By default, predictions are decoded with greedy argmax. Pass use_viterbi=True for constrained BIES decoding, which enforces valid label transitions and improves boundary consistency:
model.predict("...", use_viterbi=True)
Quality
Slovorez (Sq NoRoPE, ~0.69M parameters) on the Revised RuMorphsLemmas test set, under two splits:
- Random split — word forms split randomly; test roots may also occur in training.
- Root split — split by roots, so test roots are unseen in training (OOV) — the harder, generalization setting.
| Split | Boundary F1 | Root F1 | Accuracy | Word Acc |
|---|---|---|---|---|
| Random | 95.95 | 91.77 | 95.22 | 80.51 |
| Root (OOV) | 91.83 | 83.59 | 90.12 | 64.32 |
Boundary F1 — F1 over all morpheme boundaries; Root F1 — F1 over root boundaries; Accuracy — character-level; Word Acc — share of fully correct words.
Performance
Benchmarks on a corpus of Russian classic literature: ~3.6M tokens total, of which ~1.71M are Russian words (~150K unique forms); the rest are punctuation and non-Russian tokens, which the tokenizer skips. Pure inference runs the full pipeline (C++ tokenizer → dedup → ONNX inference → JSONL writer) over the ~150K unique forms; real corpus runs the whole corpus with the SeenIndex cache, which — by Zipf's law — turns repeats into cache hits (~91% hit rate here).
Pure inference — ~150K unique word forms (words/s):
| Configuration | 1 worker | 4 workers |
|---|---|---|
| CPU — Ryzen 7 5700X | ~13,200 | ~17,600 |
| GPU — RTX 5060 Ti | ~19,500 | ~23,000 |
Real corpus — ~1.71M Russian words, sequential with cache (words/s):
| Configuration | Decoder | Throughput |
|---|---|---|
| CPU — Ryzen 7 5700X | argmax | ~141,000 |
| CPU — Ryzen 7 5700X | Viterbi | ~67,000 |
| GPU — RTX 5060 Ti (batch 2048) | argmax | ~246,000 |
Installation
cd slovorez/
conda activate <your_env>
# CPU (recommended)
pip install .[cpu]
Demo:
python -m src.main
GPU (optional, for advanced users)
CPU is the default, recommended.
If you specifically want CUDA execution:
pip install .[gpu]
GPU support is provided as-is and is intended for users who manage their own CUDA/cuDNN stack. onnxruntime-gpu does not bundle CUDA or cuDNN — you are responsible for providing compatible runtime libraries and putting them on the library search path. Start here:
- ONNX Runtime CUDA execution provider: https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html
- ONNX Runtime install / version matrix: https://onnxruntime.ai/docs/install/
If your CUDA/cuDNN stack doesn't match ONNX Runtime's requirements, ORT falls back to CPU. The simplest way to avoid manual DLL setup is to install a PyTorch build compiled against the same major CUDA version as ONNX Runtime — ORT then reuses PyTorch's CUDA/cuDNN libraries automatically.
How it works
The architecture is a Conv1D network with sequence labeling in the BIES scheme (morpheme boundaries as token labels). Tokenization and the lexer are implemented in C++; inference runs through ONNX Runtime on CPU.
License
Citation
1 maintainer
Metadata
1 maintainer
Release files for slovorez 0.1.0
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