Sentence Transformers: all-mpnet-base-v2
sentence-transformers/all-mpnet-base-v2The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and text ranking.
Провайдер для Sentence Transformers: all-mpnet-base-v2
Hubris маршрутизирует запросы к лучшему доступному провайдеру с автоматическим fallback при сбоях.
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Sentence Transformers: all-MiniLM-L6-v2
The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering, similarity scoring, and text ranking.