# Text Embedding with Realm

*author: Jael Gu*

<br />

## Description

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]

### References

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

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

<br />

## Code Example

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

*Write the pipeline*:

```python
import towhee

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

<br />

## 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.
The default value is "google/realm-cc-news-pretrained-embedder".

Supported model name:
- google/realm-cc-news-pretrained-embedder

<br />

## Interface

The operator takes a piece of 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.