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---
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** |
|