In Dense Passage Retrieval for Open-Domain Question Answering Karpukhin et al. trained models based on Google’s Natural Questions dataset:
They also trained models on the combination of Natural Questions, TriviaQA, WebQuestions, and CuratedTREC.
There is one model to encode passages and one model to encode question / queries.
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.