# Text Embedding with DPR
*author: Kyle He*
## Description
This operator uses Dense Passage Retrieval (DPR) to convert long text to embeddings.
Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research.
It was introduced in Dense Passage Retrieval for Open-Domain Question Answering by Vladimir Karpukhin,
Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih[1].
**DPR** models were proposed in "Dense Passage Retrieval for Open-Domain Question Answering"[2].
In this work, they show that retrieval can be practically implemented using dense representations alone,
where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework[2].
### References
[1].https://huggingface.co/docs/transformers/model_doc/dpr
[2].https://arxiv.org/abs/2004.04906
## Code Example
Use the pre-trained model "facebook/dpr-ctx_encoder-single-nq-base"
to generate a text embedding for the sentence "Hello, world.".
*Write the pipeline*:
```python
from towhee import pipe, ops, DataCollection
p = (
pipe.input('text')
.map('text', 'vec', ops.text_embedding.dpr(model_name='facebook/dpr-ctx_encoder-single-nq-base'))
.output('text', 'vec')
)
DataCollection(p('Hello, world.')).show()
```
## Factory Constructor
Create the operator via the following factory method:
***text_embedding.dpr(model_name="facebook/dpr-ctx_encoder-single-nq-base")***
**Parameters:**
***model_name***: *str*
The model name in string.
The default value is "facebook/dpr-ctx_encoder-single-nq-base".
Supported model names:
- facebook/dpr-ctx_encoder-single-nq-base
- facebook/dpr-ctx_encoder-multiset-base
## 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.