LaBSE-ru-turbo Open-source BERT Model - Free Deployment for Precise Calculation of Russian Sentence Embeddings

Labse Ru Turbo

Developed by sergeyzh

BERT model for computing Russian sentence embeddings, developed based on cointegrated/LaBSE-en-ru with optimized Russian language processing performance

Text Embedding

Transformers

OtherOpen Source License:MIT #Russian sentence embeddings #Efficient inference #Semantic similarity calculation

Downloads 3,987

Release Time : 6/27/2024

Model Overview

This model is specifically designed for generating embedding representations of Russian sentences, suitable for tasks such as sentence similarity calculation and semantic search. While maintaining the same architecture as the original LaBSE model, it has been optimized for Russian.

Model Features

Russian language optimization

Specially optimized for Russian, outperforming the original LaBSE model on Russian language tasks

Efficient inference

High inference speed on both CPU and GPU, suitable for production environment deployment

Multi-task support

Performs well on various Russian NLP tasks, including classification, clustering, retrieval, etc.

Model Capabilities

Sentence embedding generation

Semantic similarity calculation

Text classification

Text clustering

Information retrieval

Question answering systems

Use Cases

Semantic search

News retrieval

Used in semantic search systems for news articles

Achieved NDCG@10 of 0.694 on the ruMTEB benchmark

Question answering systems

Answer reranking

Improving answer ranking quality in question answering systems

Achieved MAP@10 of 0.687 on the ruMTEB benchmark

Text classification

Geographic review classification

Classifying geography-related reviews

Achieved accuracy of 0.438 on the ruMTEB benchmark

🚀 BERT Model for Russian Sentence Embeddings

This is a BERT model designed for calculating sentence embeddings in Russian. It is based on cointegrated/LaBSE-en-ru, with similar context size (512), embedding dimension (768), and performance.

📦 Model Information

Property	Details
Language	Russian
Pipeline Tag	Sentence Similarity
Tags	Russian, Pretraining, Embeddings, Feature Extraction, Sentence Similarity, Sentence Transformers, Transformers
Datasets	IlyaGusev/gazeta, zloelias/lenta-ru
License	MIT
Base Model	cointegrated/LaBSE-en-ru

🚀 Quick Start

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('sergeyzh/LaBSE-ru-turbo')

sentences = ["привет мир", "hello world", "здравствуй вселенная"]
embeddings = model.encode(sentences)
print(util.dot_score(embeddings, embeddings))

📊 Metrics

Benchmark on encodechka

Model	CPU	GPU	size	Mean S	Mean S+W	dim
sergeyzh/LaBSE-ru-turbo	120.40	8.05	490	0.789	0.702	768
BAAI/bge-m3	523.40	22.50	2166	0.787	0.696	1024
intfloat/multilingual-e5-large	506.80	30.80	2136	0.780	0.686	1024
intfloat/multilingual-e5-base	130.61	14.39	1061	0.761	0.669	768
sergeyzh/rubert-tiny-turbo	5.51	3.25	111	0.749	0.667	312
intfloat/multilingual-e5-small	40.86	12.09	449	0.742	0.645	384
cointegrated/LaBSE-en-ru	120.40	8.05	490	0.739	0.667	768

Model	STS	PI	NLI	SA	TI	IA	IC	ICX	NE1	NE2
sergeyzh/LaBSE-ru-turbo	0.864	0.748	0.490	0.814	0.974	0.806	0.815	0.801	0.305	0.404
BAAI/bge-m3	0.864	0.749	0.510	0.819	0.973	0.792	0.809	0.783	0.240	0.422
intfloat/multilingual-e5-large	0.862	0.727	0.473	0.810	0.979	0.798	0.819	0.773	0.224	0.374
intfloat/multilingual-e5-base	0.835	0.704	0.459	0.796	0.964	0.783	0.802	0.738	0.235	0.376
sergeyzh/rubert-tiny-turbo	0.828	0.722	0.476	0.787	0.955	0.757	0.780	0.685	0.305	0.373
intfloat/multilingual-e5-small	0.822	0.714	0.457	0.758	0.957	0.761	0.779	0.691	0.234	0.275
cointegrated/LaBSE-en-ru	0.794	0.659	0.431	0.761	0.946	0.766	0.789	0.769	0.340	0.414

Benchmark on ruMTEB

Model Name	Metric	sbert_large_ mt_nlu_ru	sbert_large_ nlu_ru	LaBSE-ru-sts	LaBSE-ru-turbo	multilingual-e5-small	multilingual-e5-base	multilingual-e5-large
CEDRClassification	Accuracy	0.368	0.358	0.418	0.451	0.401	0.423	0.448
GeoreviewClassification	Accuracy	0.397	0.400	0.406	0.438	0.447	0.461	0.497
GeoreviewClusteringP2P	V-measure	0.584	0.590	0.626	0.644	0.586	0.545	0.605
HeadlineClassification	Accuracy	0.772	0.793	0.633	0.688	0.732	0.757	0.758
InappropriatenessClassification	Accuracy	0.646	0.625	0.599	0.615	0.592	0.588	0.616
KinopoiskClassification	Accuracy	0.503	0.495	0.496	0.521	0.500	0.509	0.566
RiaNewsRetrieval	NDCG@10	0.214	0.111	0.651	0.694	0.700	0.702	0.807
RuBQReranking	MAP@10	0.561	0.468	0.688	0.687	0.715	0.720	0.756
RuBQRetrieval	NDCG@10	0.298	0.124	0.622	0.657	0.685	0.696	0.741
RuReviewsClassification	Accuracy	0.589	0.583	0.599	0.632	0.612	0.630	0.653
RuSTSBenchmarkSTS	Pearson correlation	0.712	0.588	0.788	0.822	0.781	0.796	0.831
RuSciBenchGRNTIClassification	Accuracy	0.542	0.539	0.529	0.569	0.550	0.563	0.582
RuSciBenchGRNTIClusteringP2P	V-measure	0.522	0.504	0.486	0.517	0.511	0.516	0.520
RuSciBenchOECDClassification	Accuracy	0.438	0.430	0.406	0.440	0.427	0.423	0.445
RuSciBenchOECDClusteringP2P	V-measure	0.473	0.464	0.426	0.452	0.443	0.448	0.450
SensitiveTopicsClassification	Accuracy	0.285	0.280	0.262	0.272	0.228	0.234	0.257
TERRaClassification	Average Precision	0.520	0.502	0.587	0.585	0.551	0.550	0.584

Model Name	Metric	sbert_large_ mt_nlu_ru	sbert_large_ nlu_ru	LaBSE-ru-sts	LaBSE-ru-turbo	multilingual-e5-small	multilingual-e5-base	multilingual-e5-large
Classification	Accuracy	0.554	0.552	0.524	0.558	0.551	0.561	0.588
Clustering	V-measure	0.526	0.519	0.513	0.538	0.513	0.503	0.525
MultiLabelClassification	Accuracy	0.326	0.319	0.340	0.361	0.314	0.329	0.353
PairClassification	Average Precision	0.520	0.502	0.587	0.585	0.551	0.550	0.584
Reranking	MAP@10	0.561	0.468	0.688	0.687	0.715	0.720	0.756
Retrieval	NDCG@10	0.256	0.118	0.637	0.675	0.697	0.699	0.774
STS	Pearson correlation	0.712	0.588	0.788	0.822	0.781	0.796	0.831
Average	Average	0.494	0.438	0.582	0.604	0.588	0.594	0.630

📄 License

This project is licensed under the MIT License.

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