---
license: apache-2.0
language:
- en
inference: false
---

<br><br>

<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="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." width="150px">
</p>


<p align="center">
<b>The text embedding suit trained by Jina AI, Finetuner team.</b>
</p>


## Intented Usage & Model Info

`jina-embedding-s-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 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-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* bert-base size (soon).
- `jina-embedding-6b-en-v1`: 6 billion parameters 30* bert-base size(soon).

## Data & Parameters

More info will be released together with the technique report.

## 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    |context|
|------------------------------|-----|------|
|all-minilm-l6-v2|33m      |128|
|all-mpnet-base-v2 |110m     |128|
|ada-embedding-002|Unknown/API based  |8192|
|jina-embedding-s-en-v1|35m      |512|
|jina-embedding-b-en-v1|110m      |512|
|jina-embedding-l-en-v1|330m      |512|


|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-s-en-v1|0.738|0.781|0.732|0.833|0.785|0.859|0.471   |0.852|0.567  |
|jina-embedding-b-en-v1|0.736|0.804|0.745|0.844|0.793|0.873|0.481   |0.87|0.616  |
|jina-embedding-l-en-v1|0.735|0.829|0.759|0.844|0.8|0.888|0.465   |0.876|0.645  |

For more tasks and metrics, please checkout [MTEB](https://huggingface.co/spaces/mteb/leaderboard) benchmark.

## Usage [WIP]

```python
!pip install finetuner[text]
import finetuner
model = finetuner.get_model('jinaai/jina-embedding-s-en-v1')
embeddings = model.encode(['sentence 1', 'sentence 2'])
```

## Fine-tuning [WIP]

Please consider [Finetuner](https://github.com/jina-ai/finetuner).