Update README.md

README.md

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 ---
 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-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-s-en-v1`: 35 million parameters.
+- `jina-embedding-l-en-v1`: 800 million parameters.
+- `jina-embedding-xl-en-v1`: 3 billion parameters (soon).
+- `jina-embedding-xxl-en-v1`: 11 billion parameters (soon).
+
+## Data & Parameters
+
+More info will be released together with the technique report.
+
+## Metrics
+
+We compared the model against `all-minilm-l6-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|
+
+
+|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-b-en-v1|0.736|0.804|0.745|0.844|0.793|0.873|0.481   |0.87|0.616  |
+
+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-b-en-v1')
+embeddings = model.encode(['sentence 1', 'sentence 2'])
+```
+
+## Fine-tuning [WIP]
+
+Please consider [Finetuner](https://github.com/jina-ai/finetuner).