# 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 is implemented with pretrained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).

## Code Example

Use the pretrained model 'distilbert-base-cased'
to generate a text embedding for the sentence "Hello, world.". 

 *Write the pipeline in simplified style*:

```python
from towhee import dc


dc.stream(["Hello, world."])
  .text_embedding.transformers('distilbert-base-cased')
  .show()
```

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

```python
from towhee import dc


dc.stream['txt'](["Hello, world."])
  .text_embedding.transformers['txt', 'vec']('distilbert-base-cased')
  .select('txt', 'vec')  
  .show()
```


## Factory Constructor

Create the operator via the following factory method

***text_embedding.transformers(model_name="bert-base-uncased")***

**Parameters:**

​	***model_name***: *str*

​	The model name in string.
You can get the list of supported model names by calling `get_model_list`.


## Interface

The operator takes a text in string as input.
It loads tokenizer and pre-trained model using model name.
Text embeddings are returned in ndarray.


**Parameters:**

​	***text***: *str*

​	The text in string.



**Returns**:

​    *numpy.ndarray*

​	The text embedding extracted by model.