# facebookresearch/DPR

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

Repository: https://github.com/facebookresearch/DPR
Canonical: https://ross.abutalabs.com/products/dpr
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2023-04-06T07:36:18+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2304, "days_push": 1245, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1869, forks 311 (observed 2026-08-28T04:05:46.483104+00:00)

## What it is
DPR is a set of tools and pretrained models for dense passage retrieval in open-domain question answering, based on the EMNLP 2020 paper from Facebook AI Research. It provides bi-encoder retriever training, an extractive reader/ranker, FAISS-based inference, and data processing utilities.

## Use cases
- retrieve relevant passages for open-domain question answering
- train a dense bi-encoder retriever on QA datasets
- build semantic search over Wikipedia with FAISS
- run extractive QA with a reader and ranker pipeline
- mine hard negatives for retrieval model training
- reproduce DPR paper results with pretrained checkpoints

## When to choose
- you need state-of-the-art dense passage retrieval for open-domain QA research
- you want pretrained DPR checkpoints and Wikipedia embeddings
- you are reproducing or extending the DPR paper's experiments

## When to avoid
- you need a production-ready, actively maintained retrieval library
- you want a simple plug-and-play RAG pipeline rather than research tooling
- you need multi-vector or late-interaction retrieval beyond bi-encoders

## Facets
- artifact type: library
- maturity: maintenance
- function: rag, search-engine, machine-learning, nlp
- domain: machine-learning
- platform: python
- tags: dense-retrieval, question-answering, bi-encoder, faiss, research-code, open-domain-qa, natural-language-processing, retrieval-augmented-generation, search

## Member repositories
- facebookresearch/DPR (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:46.483104+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:15:11.882294+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/DPR (fetched 2026-08-28T04:05:46.483104+00:00, sha af29b4c2602b)
- Data as of 2026-08-30T08:39:29.467469+00:00.