# 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* :
```python
from towhee import dc
dc.stream(["Hello, world."]) \
.text_embedding.transformers(model_name="distilbert-base-cased") \
.to_list()
```
## 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 ).
## 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.
