# Russian Image-Text Retrieval Embdding with CLIP
*author: David Wang*
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## Description
This operator extracts features for image or text with [CLIP ](https://arxiv.org/abs/2103.00020 ) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This is a Russian version of CLIP adopted from [ai-forever/ru-clip ](https://github.com/ai-forever/ru-clip ).
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## Code Example
Load an image from path './teddy.jpg' to generate an image embedding.
Read the text 'Плюшевый мишка на скейтборде на Таймс-сквер.' to generate an text embedding.
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
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.ru_clip(model_name='ruclip-vit-base-patch32-224', modality='image'))
.output('img', 'vec')
)
text_pipe = (
pipe.input('text')
.map('text', 'vec', ops.image_text_embedding.ru_clip(model_name='ruclip-vit-base-patch32-224', modality='text'))
.output('text', 'vec')
)
DataCollection(img_pipe('./teddy.jpg')).show()
DataCollection(text_pipe('Плюшевый мишка на скейтборде на Таймс-сквер.')).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
***ru_clip(model_name, modality)***
**Parameters:**
** *model_name:*** *str*
The model name of CLIP. Supported model names:
- ruclip-vit-base-patch32-224
- ruclip-vit-base-patch16-224
- ruclip-vit-large-patch14-224
- ruclip-vit-large-patch14-336
- ruclip-vit-base-patch32-384
- ruclip-vit-base-patch16-384
** *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.
