sergeyzh/rubert-tiny-turbo

Быстрая модель BERT для расчетов эмбедингов предложений на русском языке. Модель основана на cointegrated/rubert-tiny2 - имеет аналогичные размеры контекста (2048), ембединга (312) и быстродействие.

Использование

from sentence_transformers import SentenceTransformer, util

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

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

Метрики

Оценки модели на бенчмарке encodechka:

model	CPU	GPU	size	Mean S	Mean S+W	dim
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/rubert-tiny2	5.51	3.25	111	0.704	0.638	312

model	STS	PI	NLI	SA	TI	IA	IC	ICX	NE1	NE2
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/rubert-tiny2	0.750	0.651	0.417	0.737	0.937	0.746	0.757	0.638	0.360	0.386