Update README.md

README.md

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+pipeline_tag: sentence-similarity
+tags:
+  - finetuner
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+datasets:
+  - jinaai/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 set trained by Jina AI, Finetuner team.</b>
+</p>
+
+
+## Intented Usage & Model Info
+
+`jina-embedding-t-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 14 million parameters,
+the model enables lightning-fast inference while still delivering impressive performance.
+Additionally, we provide the following options:
+
+- `jina-embedding-t-en-v1`: 14 million parameters **(you are here)**.
+- `jina-embedding-s-en-v1`: 35 million parameters.
+- `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    |dimension|
+|------------------------------|-----|------|
+|all-minilm-l6-v2|33m      |384|
+|all-mpnet-base-v2 |110m     |768|
+|ada-embedding-002|Unknown/OpenAI API  |8192|
+|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|0.714|0.775|0.723|0.825|0.771|0.863|0.479   |0.841|0.542  |
+|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|0.735|0.792|0.752|0.851|0.801|0.89|0.546   |0.871|0.586  |
+|jina-embedding-l-en-v1|0.739|**0.844**|0.778|**0.863**|0.821|0.896|0.566   |**0.882**|0.608  |
+
+## Usage
+
+Use with Jina AI Finetuner
+
+```python
+!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 directly 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-t-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.