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Быстрая модель BERT для расчетов эмбеддингов предложений на русском языке. Модель основана на [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) - имеет аналогичные размеры контекста (2048),
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|Model Name | Metric | sbert_large_ mt_nlu_ru | sbert_large_ nlu_ru | rubert-tiny2 | rubert-tiny-turbo | multilingual-e5-small | multilingual-e5-base | multilingual-e5-large |
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|Classification | Accuracy | 0
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|Clustering | V-measure | **0
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|MultiLabelClassification | Accuracy | 0
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|PairClassification | Average Precision | 0
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|Reranking | MAP@10 | 0
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|Retrieval | NDCG@10 | 0
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|STS | Pearson correlation | 0
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|Average | Average | 0
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Быстрая модель BERT для расчетов эмбеддингов предложений на русском языке. Модель основана на [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) - имеет аналогичные размеры контекста (2048), ембеддинга (312) и быстродействие.
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|Model Name | Metric | sbert_large_ mt_nlu_ru | sbert_large_ nlu_ru | rubert-tiny2 | rubert-tiny-turbo | multilingual-e5-small | multilingual-e5-base | multilingual-e5-large |
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|Classification | Accuracy | 0.554 | 0.552 | 0.514 | 0.535 | 0.551 | 0.561 | **0.588** |
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|Clustering | V-measure | **0.526** | 0.519 | 0.412 | 0.496 | 0.513 | 0.503 | 0.525 |
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|MultiLabelClassification | Accuracy | 0.326 | 0.319 | 0.294 | 0.317 | 0.314 | 0.329 | **0.353** |
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|PairClassification | Average Precision | 0.520 | 0.502 | 0.519 | 0.563 | 0.551 | 0.550 | **0.584** |
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|Reranking | MAP@10 | 0.561 | 0.468 | 0.461 | 0.622 | 0.715 | 0.720 | **0.756** |
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|Retrieval | NDCG@10 | 0.256 | 0.118 | 0.124 | 0.515 | 0.697 | 0.699 | **0.774** |
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|STS | Pearson correlation | 0.712 | 0.588 | 0.694 | 0.787 | 0.781 | 0.796 | **0.831** |
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|Average | Average | 0.494 | 0.438 | 0.431 | 0.548 | 0.588 | 0.594 | **0.630** |
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