# Text Embedding with DPR

*author: Kyle He*

<br />

## 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

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## 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.dc2 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()
```

<img src="./result.png" width="800px"/>

<br />

## 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

<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.