# Text Embedding with Transformers

*author: Jael Gu*



## Desription

A text embedding operator takes a sentence, paragraph, or document in string as an input
and output an embedding vector in ndarray which captures the input's core semantic elements.
This operator uses the REALM model, which is a retrieval-augmented language model that firstly retrieves documents from a textual knowledge corpus and then utilizes retrieved documents to process question answering tasks. [1]
The original model was proposed in REALM: Retrieval-Augmented Language Model Pre-Training by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.[2]

### Reference

[1].https://huggingface.co/docs/transformers/model_doc/realm

[2].https://arxiv.org/abs/2002.08909

## Code Example

Use the pretrained model "google/realm-cc-news-pretrained-embedder"
to generate a text embedding for the sentence "Hello, world.". 

 *Write the pipeline*:

```python
from towhee import dc


dc.stream(["Hello, world."])
  .text_embedding.realm(model_name="google/realm-cc-news-pretrained-embedder")
  .show()
```

## Factory Constructor

Create the operator via the following factory method

***text_embedding.transformers(model_name="google/realm-cc-news-pretrained-embedder")***

**Parameters:**

​	***model_name***: *str*

​	The model name in string.
You can get the list of supported model names by calling `get_model_list` from [realm.py](https://towhee.io/text-embedding/realm/src/branch/main/realm.py).


## Interface

The operator takes a text in string as input.
It loads tokenizer and pre-trained model using model name.
and then return text embedding in ndarray.


**Parameters:**

​	***text***: *str*

​	The text in string.



**Returns**:

​    *numpy.ndarray*

​	The text embedding extracted by model.