nomagick / jina-embeddings

Embedding models that has been trained using Jina AI's Linnaeus-Clean dataset.

  • Public
  • 33 runs
  • GitHub
  • Paper
  • License



Run time and cost

This model costs approximately $0.031 to run on Replicate, or 32 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 138 seconds. The predict time for this model varies significantly based on the inputs.


Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications.

The text embedding set trained by Jina AI, Finetuner team.

Intented Usage & Model Info

jina-embedding-l-en-v1 is a language model that has been trained using Jina AI’s Linnaeus-Clean dataset. This dataset consists of 380 million pairs of sentences, which include both query-document pairs. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.

The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.

With a size of 330 million parameters, the model enables single-gpu inference while delivering better performance than our small and base model. Additionally, we provide the following options:

Data & Parameters

Please checkout our technical blog.


We compared the model against all-minilm-l6-v2/all-mpnet-base-v2 from sbert and text-embeddings-ada-002 from OpenAI:

Name param dimension
all-minilm-l6-v2 23m 384
all-mpnet-base-v2 110m 768
ada-embedding-002 Unknown/OpenAI API 1536
jina-embedding-t-en-v1 14m 312
jina-embedding-s-en-v1 35m 512
jina-embedding-b-en-v1 110m 768
jina-embedding-l-en-v1 330m 1024
Name STS12 STS13 STS14 STS15 STS16 STS17 TRECOVID Quora SciFact
all-minilm-l6-v2 0.724 0.806 0.756 0.854 0.79 0.876 0.473 0.876 0.645
all-mpnet-base-v2 0.726 0.835 0.78 0.857 0.8 0.906 0.513 0.875 0.656
ada-embedding-002 0.698 0.833 0.761 0.861 0.86 0.903 0.685 0.876 0.726
jina-embedding-t-en-v1 0.717 0.773 0.731 0.829 0.777 0.860 0.482 0.840 0.522
jina-embedding-s-en-v1 0.743 0.786 0.738 0.837 0.80 0.875 0.523 0.857 0.524
jina-embedding-b-en-v1 0.751 0.809 0.761 0.856 0.812 0.890 0.606 0.876 0.594
jina-embedding-l-en-v1 0.745 0.832 0.781 0.869 0.837 0.902 0.573 0.881 0.598


Use with Jina AI Finetuner

!pip install finetuner
import finetuner

model = finetuner.build_model('jinaai/jina-embedding-l-en-v1')
embeddings = finetuner.encode(
    data=['how is the weather today', 'What is the current weather like today?']
print(finetuner.cos_sim(embeddings[0], embeddings[1]))

Use with sentence-transformers:

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['how is the weather today', 'What is the current weather like today?']

model = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))


Please consider Finetuner.


  1. The development of jina-embedding-s-en-v2 is currently underway with two main objectives: improving performance and increasing the maximum sequence length.
  2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called jina-embedding-s/b/l-de-v1.


Join our Discord community and chat with other community members about ideas.


If you find Jina Embeddings useful in your research, please cite the following paper:

      title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models}, 
      author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},