This model classifies whether a spoken utterance is complete (ready for response) or incomplete (speaker still talking). Trained on phone call transcripts for real-time voice chat applications.

Base Model: DeepPavlov/rubert-base-cased
Task: Binary classification (complete/incomplete turn detection)
Language: Russian

Performance

Metric	Score
Accuracy	90.00%
Precision	91.33%
Recall	100.00%
F1-Score	90.91%

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Silxxor/russian-turn-detector")
model = AutoModelForSequenceClassification.from_pretrained("Silxxor/russian-turn-detector")

text = "да я понимаю что"
inputs = tokenizer(text, return_tensors="pt", max_length=64, truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()

# 0 = incomplete turn, 1 = complete turn
print("Complete" if prediction == 1 else "Incomplete")

Training Data

Source: ASR transcripts from Russian phone calls
Sentence length: 3-15 words
Complete sentences labeled as 1
Incomplete sentences (20-60% truncated from end) labeled as 0
Balanced dataset

Preprocessing

Removed common Russian fillers: эээ, ммм, ааа, ну, вот, типа, короче
Lowercased text
Normalized whitespace
Max sequence length: 64 tokens

Training Details

Epochs: 3
Batch size: 16
Learning rate: 2e-5
Weight decay: 0.01
Optimizer: AdamW
Best model selection: F1 score

Limitations

Trained on synthetically truncated sentences, not natural speech interruptions
Optimized for phone call scenarios with ASR artifacts
May not generalize to other Russian speech domains
Performance may degrade on very short (<3 words) or long (>15 words) utterances

Intended Use

Real-time voice assistants and conversational AI systems that need to determine when a speaker has finished their turn in Russian.