jina-embeddings-de

About

What jina-embeddings-de is

Quick Start The easiest way to starting using jina-embeddings-v2-base-de is to use Jina AI's Embedding API. Intended Usage & Model Info jina-embeddings-v2-base-de is a German/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 German-English input without bias. Additionally, we provide the following embedding models: jina-embeddings-v2-base-de ist ein zweisprachiges Text Embedding Modell für Deutsch und Englisch, welches Texteingaben mit einer Länge von bis zu 8192 Token unterstützt. Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT, welche mithilfe einer symmetrische Variante von ALiBi längere Eingabetexte erlaubt. Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert, gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren. Des Weiteren stellen wir folgende Embedding-Modelle bereit: - jina-embeddings-v2-small-en: 33 million parameters. - jina-embeddings-v2-base-en: 137 million parameters. - jina-embeddings-v2-base-zh: 161 million parameters Chinese-English Bilingual embeddings. - jina-embeddings-v2-base-de: 161 million parameters German-English Bilingual embeddings (you are here). - [jina-embeddings-v2-base-es](): Spanish-English Bilingual embeddings (soon). - jina-embeddings-v2-base-code: 161 million parameters code embeddings. Data & Parameters The data and training details are described in this technical report. Usage <details<summaryPlease apply mean pooling when integrating the model.</summary Why mean pooling? mean poooling takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an encode function to deal with this. However, if you would like to do it without using the default encode function: You can use Jina Embedding models directly f

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys jina-embeddings-de for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-embeddings-de below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"jina-embeddings-de","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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