---
title: jina-embedding-b-en-v1
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/jina-embedding-b-en-v1"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/jina-embedding-b-en-v1.md"
repository: AI-ModelScope/jina-embedding-b-en-v1
last_updated: 2023-10-30
license: apache-2.0
pipeline_tag: sentence-similarity
tasks:
- sentence-similarity
model_type:
- t5
architectures:
- T5EncoderModel
library_name:
- sentence-transformers
- pytorch
frameworks:
- pytorch
language:
- en
downloads: 35
stars: 0
tags:
- finetuner
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
---
# jina-embedding-b-en-v1
> jina-embedding-b-en-v1 - AI-ModelScope 在 ModelScope 开源的模型。The text embedding set trained by Jina AI , Finetuner team.
AI-ModelScope/jina-embedding-b-en-v1 是 ModelScope 魔搭社区上的sentence-similarity模型，采用 apache-2.0 许可。
- **Repository**: AI-ModelScope/jina-embedding-b-en-v1
- **License**: apache-2.0
- **Tasks**: sentence-similarity
- **Tags**: finetuner, sentence-transformers, feature-extraction, sentence-similarity, mteb
- **Downloads**: 35
- **Stars**: 0
- **Last updated**: 2023-10-30
Source: https://www.modelscope.cn/models/AI-ModelScope/jina-embedding-b-en-v1
---
---
The text embedding set trained by Jina AI, Finetuner team.
## Intented Usage & Model Info
`jina-embedding-b-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 standard size of 110 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 options:
- [`jina-embedding-t-en-v1`](https://huggingface.co/jinaai/jina-embedding-t-en-v1): 14 million parameters.
- [`jina-embedding-s-en-v1`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters
- [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters **(you are here)**.
- [`jina-embedding-l-en-v1`](https://huggingface.co/jinaai/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](https://arxiv.org/abs/2307.11224).
## 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 |
## Usage
Usage with Jina AI Finetuner:
```python
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-b-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:
```python
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]))
```
## Fine-tuning
Please consider [Finetuner](https://github.com/jina-ai/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](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
``` latex
@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}
}
```