---
language:
- ru

pipeline_tag: sentence-similarity

tags:
- russian
- pretraining
- embeddings
- tiny
- feature-extraction
- sentence-similarity
- sentence-transformers
- transformers
- mteb

datasets:
- IlyaGusev/gazeta
- zloelias/lenta-ru
- HuggingFaceFW/fineweb-2

license: mit


---


Быстрая модель BERT для русского языка с размером ембеддинга 256 и длиной контекста 512. Модель получена методом последовательной дистилляции моделей [sergeyzh/rubert-tiny-turbo](https://huggingface.co/sergeyzh/rubert-tiny-turbo) и [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). Выигрывает по скорости  у [rubert-tiny-turbo](https://huggingface.co/sergeyzh/rubert-tiny-turbo) при аналогичном качестве на CPU в ~x1.4, на GPU в ~x1.2 раза.



## Использование
```Python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('sergeyzh/rubert-tiny-lite')

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

print(model.similarity(embeddings, embeddings))
```

## Метрики

Оценки модели на бенчмарке [encodechka](https://github.com/avidale/encodechka):

| model                              | STS      | PI       | NLI      | SA       | TI       |
|:-----------------------------------|:---------|:---------|:---------|:---------|:---------|
| BAAI/bge-m3                        |  0.864   |  0.749   |  0.510   |  0.819   |  0.973   |
| intfloat/multilingual-e5-large     |  0.862   |  0.727   |  0.473   |  0.810   |  0.979   |
| **sergeyzh/rubert-tiny-lite**      |  0.839   |  0.712   |  0.488   |  0.788   |  0.949   |
| intfloat/multilingual-e5-base      |  0.835   |  0.704   |  0.459   |  0.796   |  0.964   |
| [sergeyzh/rubert-tiny-turbo](https://huggingface.co/sergeyzh/rubert-tiny-turbo)     |  0.828   |  0.722   |  0.476   |  0.787   |  0.955   |
| intfloat/multilingual-e5-small     |  0.822   |  0.714   |  0.457   |  0.758   |  0.957   |
| cointegrated/rubert-tiny2          |  0.750   |  0.651   |  0.417   |  0.737   |  0.937   |

Оценки модели на бенчмарке [ruMTEB](https://habr.com/ru/companies/sberdevices/articles/831150/):

|Model Name                         | Metric              | rubert-tiny2    | [rubert-tiny-turbo](https://huggingface.co/sergeyzh/rubert-tiny-turbo) | rubert-tiny-lite | multilingual-e5-small | multilingual-e5-base | multilingual-e5-large |
|:----------------------------------|:--------------------|----------------:|------------------:|------------------:|----------------------:|---------------------:|----------------------:|
|CEDRClassification                 | Accuracy            |      0.369      |        0.390      |        0.407      |        0.401          |        0.423         |       **0.448**       |
|GeoreviewClassification            | Accuracy            |      0.396      |        0.414      |        0.423      |        0.447          |        0.461         |       **0.497**       |
|GeoreviewClusteringP2P             | V-measure           |      0.442      |        0.597      |      **0.611**    |        0.586          |        0.545         |         0.605         |
|HeadlineClassification             | Accuracy            |      0.742      |        0.686      |        0.652      |        0.732          |        0.757         |       **0.758**       |
|InappropriatenessClassification    | Accuracy            |      0.586      |        0.591      |        0.588      |        0.592          |        0.588         |       **0.616**       |
|KinopoiskClassification            | Accuracy            |      0.491      |        0.505      |        0.507      |        0.500          |        0.509         |       **0.566**       |
|RiaNewsRetrieval                   | NDCG@10             |      0.140      |        0.513      |        0.617      |        0.700          |        0.702         |       **0.807**       |
|RuBQReranking                      | MAP@10              |      0.461      |        0.622      |        0.631      |        0.715          |        0.720         |       **0.756**       |
|RuBQRetrieval                      | NDCG@10             |      0.109      |        0.517      |        0.511      |        0.685          |        0.696         |       **0.741**       |
|RuReviewsClassification            | Accuracy            |      0.570      |        0.607      |        0.615      |        0.612          |        0.630         |       **0.653**       |
|RuSTSBenchmarkSTS                  | Pearson correlation |      0.694      |        0.787      |        0.799      |        0.781          |        0.796         |       **0.831**       |
|RuSciBenchGRNTIClassification      | Accuracy            |      0.456      |        0.529      |        0.544      |        0.550          |        0.563         |       **0.582**       |
|RuSciBenchGRNTIClusteringP2P       | V-measure           |      0.414      |        0.481      |        0.510      |        0.511          |        0.516         |       **0.520**       |
|RuSciBenchOECDClassification       | Accuracy            |      0.355      |        0.415      |        0.424      |        0.427          |        0.423         |       **0.445**       |
|RuSciBenchOECDClusteringP2P        | V-measure           |      0.381      |        0.411      |        0.438      |        0.443          |        0.448         |       **0.450**       |
|SensitiveTopicsClassification      | Accuracy            |      0.220      |        0.244      |      **0.282**    |        0.228          |        0.234         |         0.257         |
|TERRaClassification                | Average Precision   |      0.519      |        0.563      |        0.574      |        0.551          |        0.550         |       **0.584**       |
                                                                                                  
|Model Name                         | Metric              | rubert-tiny2    | [rubert-tiny-turbo](https://huggingface.co/sergeyzh/rubert-tiny-turbo) | rubert-tiny-lite  | multilingual-e5-small | multilingual-e5-base | multilingual-e5-large |
|:----------------------------------|:--------------------|----------------:|------------------:|------------------:|----------------------:|----------------------:|---------------------:|
|Classification                     | Accuracy            |      0.514      |        0.535      |        0.536      |        0.551          |        0.561          |      **0.588**       |
|Clustering                         | V-measure           |      0.412      |        0.496      |        0.520      |        0.513          |        0.503          |      **0.525**       |
|MultiLabelClassification           | Accuracy            |      0.294      |        0.317      |        0.344      |        0.314          |        0.329          |      **0.353**       |
|PairClassification                 | Average Precision   |      0.519      |        0.563      |        0.574      |        0.551          |        0.550          |      **0.584**       |
|Reranking                          | MAP@10              |      0.461      |        0.622      |        0.631      |        0.715          |        0.720          |      **0.756**       |
|Retrieval                          | NDCG@10             |      0.124      |        0.515      |        0.564      |        0.697          |        0.699          |      **0.774**       |
|STS                                | Pearson correlation |      0.694      |        0.787      |        0.799      |        0.781          |        0.796          |      **0.831**       |
|Average                            | Average             |      0.431      |        0.548      |        0.567      |        0.588          |        0.594          |      **0.630**       |