BAAI: bge-base-en-v1.5
baai/bge-base-en-v1.5The bge-base-en-v1.5 embedding model converts English sentences and paragraphs into 768-dimensional dense vectors, delivering efficient, high-quality semantic embeddings optimized for retrieval, semantic search, and document-matching workflows. This version (v1.5) features improved similarity-score distribution and stronger retrieval performance out of the box.
Провайдер для BAAI: bge-base-en-v1.5
Hubris маршрутизирует запросы к лучшему доступному провайдеру с автоматическим fallback при сбоях.
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Другие модели от baai
BAAI: bge-large-en-v1.5
The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks in English.
BAAI: bge-m3
The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.