# Text Embedding with Transformers

*author: Jael Gu*

<br />

## 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).

<br />

## Code Example

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

*Write the pipeline*:

```python
from towhee import dc


dc.stream(["Hello, world."]) \
  .text_embedding.transformers(model_name="distilbert-base-cased") \
  .to_list()
```

<br />

## 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` from [auto_transformers.py](https://towhee.io/text-embedding/transformers/src/branch/main/auto_transformers.py).

<br />

## 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.