# 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 ## Code Example Use the pretrained 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 dc dc.stream(["Hello, world."]) .text_embedding.dpr("facebook/dpr-ctx_encoder-single-nq-base") .show() ``` ## Factory Constructor Create the operator via the following factory method ***ops.text_embedding.dpr(model_name)*** ## 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". 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.