---
language:
- ru

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

---

Модель BERT для расчетов эмбедингов предложений на русском языке. Модель основана на [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru) - имеет аналогичные размеры контекста (512), ембединга (768) и быстродействие.


## Использование:
```Python
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](https://github.com/avidale/encodechka):

| Model                              | CPU       | GPU      | size     |   Mean S  | Mean S+W   |   dim  |
|:-----------------------------------|----------:|---------:|---------:|----------:|-----------:|-------:|
| **sergeyzh/LaBSE-ru-turbo**        | **133.40**|**15.30** |**490**   |  **0.789**| **0.703**  | **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           |   133.40  |  15.30   |  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** |**0.802** |**0.320** |**0.401** |
| 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   |