dpr/README.md

# Text Embedding with dpr

*author: Kyle He*



## Desription

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, we 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].

## Reference

[1].https://huggingface.co/docs/transformers/v4.16.2/en/model_doc/longformer#transformers.LongformerConfig

[2].https://arxiv.org/abs/2004.04906

```python
from towhee import ops

text_encoder = ops.text_embedding.dpr(model_name="allenai/longformer-base-4096")
text_embedding = text_encoder("Hello, world.")
```

## Factory Constructor

Create the operator via the following factory method

***ops.text_embedding.dpr(model_name)***



## Interface

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.


**Parameters:**

​	***text***: *str*

​	The text in string.



**Returns**: *numpy.ndarray*

​	The text embedding extracted by model.



## Code Example

Use the pretrained model ('allenai/longformer-base-4096')
to generate a text embedding for the sentence "Hello, world.". 

 *Write the pipeline in simplified style*:

```python
import towhee.DataCollection as dc

dc.glob("Hello, world.")
  .text_embedding.dpr('longformer-base-4096')
  .show()
```

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

```python
from towhee import DataCollection as dc

dc.glob['text']('Hello, world.')
  .text_embedding.dpr['text', 'vec']('longformer-base-4096')
  .select('vec')
  .show()
```