jina-embeddings-en

About

What jina-embeddings-en is

Quick Start The easiest way to starting using jina-embeddings-v2-base-en is to use Jina AI's Embedding API. Intended Usage & Model Info jina-embeddings-v2-base-en is an English, monolingual 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. The backbone jina-bert-v2-base-en is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc. With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference. Additionally, we provide the following embedding models: - jina-embeddings-v2-small-en: 33 million parameters. - jina-embeddings-v2-base-en: 137 million parameters (you are here). - jina-embeddings-v2-base-zh: Chinese-English Bilingual embeddings. - jina-embeddings-v2-base-de: German-English Bilingual embeddings. - jina-embeddings-v2-base-es: Spanish-English Bilingual embeddings. Data & Parameters Jina Embeddings V2 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 from transformers package. If you only want to ha

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-en for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-embeddings-en 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-en","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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