# Chinese Image-Text Retrieval Embdding with Taiyi

*author: David Wang*


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## Description

This operator extracts features for image or text(in Chinese) with [Taiyi(太乙)](https://arxiv.org/abs/2209.02970) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This method is developed by [IDEA-CCNL](https://github.com/IDEA-CCNL/Fengshenbang-LM/).


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## Code Example

Load an image from path './teddy.jpg' to generate an image embedding. 

Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding. 

*Write a pipeline with explicit inputs/outputs name specifications:*

```python
from towhee import pipe, ops, DataCollection

img_pipe = (
    pipe.input('url')
    .map('url', 'img', ops.image_decode.cv2_rgb())
    .map('img', 'vec', ops.image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', modality='image'))
    .output('img', 'vec')
)

text_pipe = (
    pipe.input('text')
    .map('text', 'vec', ops.image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', modality='text'))
    .output('text', 'vec')
)

DataCollection(img_pipe('./dog.jpg')).show()
DataCollection(text_pipe('一只小狗')).show()
```
<img src="./tabular1.png" alt="result1" style="height:60px;"/>
<img src="./tabular2.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***taiyi(model_name, modality)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of Taiyi. Supported model names: 
- taiyi-clip-roberta-102m-chinese
- taiyi-clip-roberta-large-326m-chinese


​   ***modality:*** *str*

​   Which modality(*image* or *text*) is used to generate the embedding. 

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## Interface

An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.


**Parameters:**

​	***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)*  or *str*

​  The data (image or text based on specified modality) to generate embedding.	



**Returns:** *numpy.ndarray*

​   The data embedding extracted by model.