StaRSE
StaRSE stands for Static Russian Sentence Embeddings. It is a compact Russian sentence embedding model implemented as a
Sentence-Transformers StaticEmbedding
endpoint.
The model is intended for CPU-friendly semantic similarity, clustering, classification features, and retrieval-style first-stage representations when a full Transformer encoder is too expensive to run at high throughput.
[!NOTE] StaRSE has 61.51M logical embedding parameters (
120,138 × 512), stored as binary sign bits plus one FP32 L2 norm vector.
Performance
Evaluation is reported on
MTEB(rus, v1.1)
across 23 tasks. The main score is mean_task_main_score = 51.16.
| Task type | Tasks | Mean score |
|---|---|---|
| Classification | 9 | 56.81 |
| Clustering | 3 | 51.80 |
| MultilabelClassification | 2 | 35.01 |
| PairClassification | 1 | 52.50 |
| Reranking | 2 | 41.88 |
| Retrieval | 3 | 39.09 |
| STS | 3 | 62.18 |
Usage
Install Sentence Transformers:
pip install -U sentence-transformers
Load the model with trust_remote_code=True.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("BorisTM/starse", trust_remote_code=True)
sentences = [
"Партитуры Чайковского часто звучат в консерватории.",
"Балетная сцена хранит музыку Щелкунчика.",
"Футбольная команда выиграла матч.",
]
embeddings = model.encode(sentences, normalize_embeddings=True)
similarities = model.similarity(embeddings, embeddings)
print(embeddings.shape) # (3, 512)
print(tuple(similarities.shape)) # (3, 3)
print(similarities)
# tensor([[1.0000, 0.3521, 0.0626],
# [0.3521, 1.0000, 0.0420],
# [0.0626, 0.0420, 1.0000]])
Citation
@misc{starse2026,
title = {StaRSE: Compact Russian Sentence Embeddings with a Sign-Coded Static Encoder},
year = {2026},
url = {https://huggingface.co/BorisTM/starse}
}