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

Intented Usage & Model Info

jina-embedding-t-en-v1 is a tiny 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 tiny small parameter size of just 14 million parameters, the model enables lightning-fast inference on CPU, while still delivering impressive performance. Additionally, we provide the following options:

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:

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

Inference Speed

We encoded a single sentence "What is the current weather like today?" 10k times on:

  1. cpu: MacBook Pro 2020, 2 GHz Quad-Core Intel Core i5
  2. gpu: 1 Nvidia 3090

And recorded time spent to demonstrate the embedding speed:

Name param dimension time@cpu time@gpu
jina-embedding-t-en-v1 14m 312 5.78s 2.36s
all-minilm-l6-v2 23m 384 11.95s 2.70s
jina-embedding-s-en-v1 35m 512 17.25s 2.81s

Usage

Use with Jina AI Finetuner

!pip install finetuner
import finetuner

model = finetuner.build_model('jinaai/jina-embedding-t-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-t-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

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

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}
}
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Dataset used to train jinaai/jina-embedding-t-en-v1

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