Модель BERT для расчетов эмбеддингов предложений на русском языке. Модель основана на cointegrated/LaBSE-en-ru - имеет аналогичные размеры контекста (512), ембеддинга (768) и быстродействие.
Использование:
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))
Метрики
Оценки модели на бенчмарке 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 |
Оценки модели на бенчмарке 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 |
- Downloads last month
- 3,225
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for sergeyzh/LaBSE-ru-turbo
Base model
cointegrated/LaBSE-en-ru