docs: add embedding page

CHANGELOG.md

@@ -37,6 +37,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Docs
 
+- Add Jina embeddings documentation page. ([#759](https://github.com/jina-ai/finetuner/pull/759))
+
 
 ## [0.7.8] - 2023-06-08
 

docs/get-started/installation.md

@@ -9,8 +9,7 @@ Make sure you have `Python 3.8+` installed on Linux/Mac/Windows:
 pip install -U finetuner
 ```
 
-If you want to encode your data locally with the {meth}`~finetuner.encode` function, you need to install `"finetuner[full]"`.
-In this case, some extra dependencies are installed which are necessary to do the inference, e.g., torch, torchvision, and open clip:
+If you want to submit a fine-tuning job on the cloud, please use:
 
 ```bash
 pip install "finetuner[full]"

docs/get-started/pretrained.md

@@ -0,0 +1,47 @@
+(pretrained-models)=
+# {octicon}`rocket` Jina Embeddings
+
+Starting with Finetuner 0.7.9,
+we have introduced a suite of pre-trained text embedding models licensed under Apache 2.0.
+The model have a range of useThese models have a variety of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
+The suite consists of the following models:
+
+- `jina-embedding-s-en-v1` **[Huggingface](jinaai/jina-embedding-s-en-v1)**: This is a compact model with just 35 million parameters, that performs lightning-fast inference while delivering impressive performance.
+- `jina-embedding-b-en-v1` **[Huggingface](jinaai/jina-embedding-b-en-v1)**: This model has a size of 110 million parameters, performs fast inference and delivers better performance than our smaller model.
+- `jina-embedding-l-en-v1` **[Huggingface](jinaai/jina-embedding-l-en-v1)**: This is a relatively large model with a size of 330 million parameters, that performs single-gpu inference and delivers better performance than our other model.
+
+## Usage
+
+```python
+import finetuner
+
+model = finetuner.build_model('jinaai/jina-embedding-s-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]))
+```
+
+## Training Data
+
+Jina Embeddings is a suite of language models that have been trained using Jina AI's Linnaeus-Clean dataset.
+This dataset consists of 380 million query-document pairs of sentences.
+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.
+
+## Characteristics
+
+Each Jina embedding model can encode up to 512 tokens,
+with any further tokens being truncated.
+The models have different output dimensionalities, as shown in the table below:
+
+|Name|param    |context| Dimension |
+|------------------------------|-----|------|-----------|
+|jina-embedding-s-en-v1|35m      |512| 512       |
+|jina-embedding-b-en-v1|110m      |512| 768       |
+|jina-embedding-l-en-v1|330m      |512| 1024      |
+
+## Performance
+
+Please refer to the [Huggingface](jinaai/jina-embedding-s-en-v1) page.

docs/index.md

@@ -26,6 +26,7 @@
 
 get-started/how-it-works
 get-started/installation
+get-started/pretrained
 walkthrough/index
 ```