Efficient Passage Retrieval with Hashing for Open-domain Question Answering
Paper • 2106.00882 • Published
How to use castorini/bpr-nq-question-encoder with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="castorini/bpr-nq-question-encoder") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("castorini/bpr-nq-question-encoder")
model = AutoModel.from_pretrained("castorini/bpr-nq-question-encoder")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This model is converted from the original BPR repo and fitted into Pyserini:
Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. arXiv:2106.00882.