Sentence Transformers: multi-qa-mpnet-base-dot-v1embedding

Описание

The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.

Возможности

Модальности

Характеристики

API и примеры кода

from openai import OpenAI

client = OpenAI(
    base_url="https://infergate.ru/api/v1",
    api_key="ig-•••",
)

resp = client.embeddings.create(
    model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
    input="Текст для векторизации",
)
print(resp.data[0].embedding[:8])

Поддерживаемые параметры запроса