DPR-Models

In Dense Passage Retrieval for Open-Domain Question Answering Karpukhin et al. trained models based on Google’s Natural Questions dataset:

  • facebook-dpr-ctx_encoder-single-nq-base

  • facebook-dpr-question_encoder-single-nq-base

They also trained models on the combination of Natural Questions, TriviaQA, WebQuestions, and CuratedTREC.

  • facebook-dpr-ctx_encoder-multiset-base

  • facebook-dpr-question_encoder-multiset-base

There is one model to encode passages and one model to encode question / queries.

Usage

To encode paragraphs, you need to provide a title (e.g. the Wikipedia article title) and the text passage. These must be seperated with a [SEP] token. For encoding paragraphs, we use the ctx_encoder.

Queries are encoded with question_encoder:

from sentence_transformers import SentenceTransformer, util
passage_encoder = SentenceTransformer('facebook-dpr-ctx_encoder-single-nq-base')

passages = [
    "London [SEP] London is the capital and largest city of England and the United Kingdom.",
    "Paris [SEP] Paris is the capital and most populous city of France.",
    "Berlin [SEP] Berlin is the capital and largest city of Germany by both area and population."
]

passage_embeddings = passage_encoder.encode(passages)

query_encoder = SentenceTransformer('facebook-dpr-question_encoder-single-nq-base')
query = "What is the capital of England?"
query_embedding = query_encoder.encode(query)

#Important: You must use dot-product, not cosine_similarity
scores = util.dot_score(query_embedding, passage_embeddings)
print("Scores:", scores)

Important note: When you use these models, you have to use them with dot-product (e.g. as implemented in util.dot_score) and not with cosine similarity.