3-layer
Collection
очень быстрые модели
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3 items
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Updated
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1
Быстрая модель BERT для русского языка с размером ембеддинга 256 и длиной контекста 512. Модель получена методом последовательной дистилляции моделей sergeyzh/rubert-tiny-turbo и BAAI/bge-m3. Выигрывает по скорости у rubert-tiny-turbo при аналогичном качестве на CPU в ~x1.4, на GPU в ~x1.2 раза.
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))
Оценки модели на бенчмарке 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 | 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:
Model Name | Metric | rubert-tiny2 | 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 | rubert-tiny-lite | multilingual-e5-small | multilingual-e5-base | multilingual-e5-large |
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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 |