README.md

---
pipeline_tag: sentence-similarity
tags:
  - finetuner
  - feature-extraction
  - sentence-similarity
datasets:
  - negation-dataset
language: en
license: apache-2.0
---

<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/OpenAI API  |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|0.736|0.78|0.745|0.84|0.79|0.868|0.484   |0.856|0.606  |
|jina-embedding-b-en-v1|**0.74**|0.792|0.752|0.851|0.801|0.88|0.505   |0.871|0.64  |
|jina-embedding-l-en-v1|0.739|**0.844**|0.778|**0.863**|0.829|0.896|0.526   |**0.882**|0.652  |

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

## Usage

```python
!pip install finetuner
import finetuner

model = finetuner.build_model('jinaai/jina-embedding-l-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]))
```

## 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.