language: ru
license: apache-2.0
library_name: transformers
tags:
- russian
- morpheme-segmentation
- token-classification
- morphbert
- lightweight
- bert
- ru
- russ
pipeline_tag: token-classification
new_version: CrabInHoney/morphbert-tiny-v2-morpheme-segmentation-ru
MorphBERT-Tiny: Russian Morpheme Segmentation
This repository contains the CrabInHoney/morphbert-tiny-morpheme-segmentation-ru model, a highly compact transformer-based system fine-tuned for morpheme segmentation of Russian words. The model classifies each character of a given word into one of four morpheme categories: Prefix (PREF), Root (ROOT), Suffix (SUFF), or Ending (END).
Model Description
morphbert-tiny-morpheme-segmentation-ru leverages a lightweight transformer architecture, enabling efficient deployment and inference while maintaining high performance on the specific task of morphological analysis at the character level. Despite its diminutive size, the model demonstrates considerable accuracy in identifying the constituent morphemes within Russian words.
The model was derived through logit distillation from a larger teacher model, comparable in complexity to bert-base
Key Features:
- Task: Morpheme Segmentation (Token Classification at Character Level)
- Language: Russian (ru)
- Architecture: Transformer (BERT-like, optimized for size)
- Labels: PREF, ROOT, SUFF, END
Model Size & Specifications:
- Parameters: ~3.58 Million
- Tensor Type: F32
- Disk Footprint: ~14.3 MB
Usage
The model can be easily used with the Hugging Face transformers library. It processes words character by character.
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
model_name = "CrabInHoney/morphbert-tiny-morpheme-segmentation-ru"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
model.eval()
def analyze(word):
tokens = list(word)
encoded = tokenizer(tokens, is_split_into_words=True, return_tensors="pt", truncation=True, max_length=34)
with torch.no_grad():
logits = model(**encoded).logits
predictions = logits.argmax(dim=-1)[0]
word_ids = encoded.word_ids()
output = []
for i, word_idx in enumerate(word_ids):
if word_idx is not None and word_idx < len(tokens):
label_id = predictions[i].item()
label = model.config.id2label[label_id]
output.append(f"{tokens[word_idx]}:{label}")
return " / ".join(output)
# Примеры
for word in ["масляный", "предчувствий", "тарковский", "кот", "подгон"]:
print(f"{word} → {analyze(word)}")
Example Predictions
масляный → м:ROOT / а:ROOT / с:ROOT / л:ROOT / я:SUFF / н:SUFF / ы:END / й:END
предчувствий → п:PREF / р:PREF / е:PREF / д:PREF / ч:ROOT / у:ROOT / в:SUFF / с:SUFF / т:SUFF / в:SUFF / и:END / й:END
тарковский → т:ROOT / а:ROOT / р:ROOT / к:ROOT / о:SUFF / в:SUFF / с:SUFF / к:SUFF / и:END / й:END
кот → к:ROOT / о:ROOT / т:ROOT
подгон → п:PREF / о:PREF / д:PREF / г:ROOT / о:ROOT / н:ROOT
Performance
The model achieves an approximate character-level accuracy of 0.975 on its evaluation dataset.
Limitations
- Performance may vary on out-of-vocabulary words, neologisms, or highly complex morphological structures not sufficiently represented in the training data.
- The model operates strictly at the character level; it does not incorporate broader lexical or syntactic context.
- Ambiguous cases in morpheme boundaries might be resolved based on patterns learned during training, which may not always align with linguistic conventions in edge cases.