The text embedding set trained by Jina AI.

Quick Start

The easiest way to starting using jina-embeddings-v2-base-es is to use Jina AI’s Embedding API.

Intended Usage & Model Info

jina-embeddings-v2-base-es is a Spanish/English bilingual text embedding model supporting 8192 sequence length. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Spanish-English input without bias. Additionally, we provide the following embedding models:

jina-embeddings-v2-base-es es un modelo (embedding) de texto bilingüe Inglés/Español que admite una longitud de secuencia de 8192. Se basa en la arquitectura BERT (JinaBERT) que incorpora la variante bi-direccional simétrica de ALiBi para permitir una mayor longitud de secuencia. Hemos diseñado este modelo para un alto rendimiento en aplicaciones monolingües y bilingües, y está entrenando específicamente para admitir entradas mixtas de español e inglés sin sesgo. Adicionalmente, proporcionamos los siguientes modelos (embeddings):

In ollama hub we provide the following set of models:

• jina-embeddings-v2-small-en: 33 million parameters.
• jina-embeddings-v2-base-en: 137 million parameters.
• jina-embeddings-v2-base-de: German-English Bilingual embeddings.
• jina-embeddings-v2-base-es: Spanish-English Bilingual embeddings. (you are here).

Ollama Usage

This model is an embedding model, meaning it can only be used to generate embeddings.

You can get it by doing ollama pull jina/jina-embeddings-v2-base-es

REST API

curl http://localhost:11434/api/embeddings -d '{
  "model": "jina/jina-embeddings-v2-base-es",
  "prompt": "¿Qué tiempo hace hoy?"
}'

Python API

ollama.embeddings(model='jina/jina-embeddings-v2-base-es', prompt='¿Qué tiempo hace hoy?')

Javascript API

ollama.embeddings({ model: 'jina/jina-embeddings-v2-base-es', prompt: '¿Qué tiempo hace hoy?' })

Use Jina Embeddings for RAG

According to the latest blog post from LLamaIndex,

In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.

References

Technical Report

Jina Embeddings homepage