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-# taiyi
+# Chinese Image-Text Retrieval Embdding with Taiyi
+
+*author: David Wang*
+
+
+
+
+
+
+## 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/).
+
+
+
+
+
+## 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 the pipeline in simplified style*:
+
+```python
+import towhee
+
+towhee.glob('./dog.jpg') \
+ .image_decode() \
+ .image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', modality='image') \
+ .show()
+
+towhee.dc(["一只小狗"]) \
+ .image_text_embedding.taiyi(model_name='taiyi-clip-roberta-102m-chinese', modality='text') \
+ .show()
+```
+
+
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+import towhee
+
+towhee.glob['path']('./dog.jpg') \
+ .image_decode['path', 'img']() \
+ .image_text_embedding.taiyi['img', 'vec'](model_name='taiyi-clip-roberta-102m-chinese', modality='image') \
+ .select['img', 'vec']() \
+ .show()
+
+towhee.dc['text'](["一只小狗"]) \
+ .image_text_embedding.taiyi['text','vec'](model_name='taiyi-clip-roberta-102m-chinese', modality='text') \
+ .select['text', 'vec']() \
+ .show()
+```
+
+
+
+
+
+
+
+
+## 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.
+
+
+
+
+
+## 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.
+
+
+
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