Sentence Transformers: all-mpnet-base-v2

Sentence Transformers: paraphrase-MiniLM-L6-v2

The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval...

Contesto: 1K
Input: text
Output: embeddings

Input: $0.0050Output: Gratuitoper milione di token

Sentence Transformers: all-MiniLM-L12-v2

The all-MiniLM-L12-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, clustering, and...

Contesto: 1K
Input: text
Output: embeddings

Input: $0.0050Output: Gratuitoper milione di token

Sentence Transformers: multi-qa-mpnet-base-dot-v1

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.

Contesto: 1K
Input: text
Output: embeddings

Input: $0.0050Output: Gratuitoper milione di token

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,...

Contesto: 1K
Input: text
Output: embeddings

Input: $0.0050Output: Gratuitoper milione di token