jina-embedding-s-en

With a compact size of just 35 million parameters, the model enables lightning-fast inference while still delivering impressive performance. Additionally, we provide the following options:

• jina-embedding-t-en-v1: 14 million parameters.
• jina-embedding-s-en-v1: 35 million parameters (you are here).
• jina-embedding-b-en-v1: 110 million parameters.
• jina-embedding-l-en-v1: 330 million parameters.
• jina-embedding-1b-en-v1: 1.2 billion parameters, 10 times bert-base (soon).
• jina-embedding-6b-en-v1: 6 billion parameters, 30 times bert-base (soon).

Data & Parameters

Please checkout our technical blog.

Metrics

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

Usage

Use with Jina AI Finetuner

!pip install finetuner
import finetuner

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

Fine-tuning

Please consider Finetuner.

Plans

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.

Contact

Citation

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

@misc{günther2023jina,
      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},
      year={2023},
      eprint={2307.11224},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}