RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/DPR

Default branch main · commit a31212dc · scanned 6/25/2026, 3:32:43 PM

GitHub: 1,866 stars · 312 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface facebookresearch/DPR, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen the README's opening to emphasize its utility for building systems

    Why:

    CURRENT
    # Dense Passage Retrieval

Dense Passage Retrieval (`DPR`) - is a set of tools and models for state-of-the-art open-domain Q&A research.
    COPY-PASTE FIX
    # Dense Passage Retrieval

Dense Passage Retrieval (`DPR`) provides a robust set of tools and models for building state-of-the-art open-domain Question Answering (QA) systems. It is designed for researchers and developers implementing advanced passage retrieval techniques.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    open-domain-qa, question-answering, information-retrieval, deep-learning, natural-language-processing, nlp, passage-retrieval, dense-retrieval, embeddings, machine-learning, ai
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://www.aclweb.org/anthology/2020.emnlp-main.550

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface facebookresearch/DPR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 3×
  2. Haystack · recommended 2×
  3. Faiss · recommended 2×
  4. Elasticsearch · recommended 2×
  5. Hugging Face Datasets · recommended 1×
  • CATEGORY QUERY
    How to build an open-domain question answering system using deep learning models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. Haystack
    4. Faiss
    5. Elasticsearch
    6. PyTorch
    7. TensorFlow
    8. Gensim

    AI recommended 8 alternatives but never named facebookresearch/DPR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques for retrieving relevant passages for question answering tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Faiss
    3. Weaviate
    4. Elasticsearch
    5. Apache Lucene
    6. Rank BM25
    7. Haystack
    8. LlamaIndex
    9. ColBERT
    10. Hugging Face Transformers

    AI recommended 10 alternatives but never named facebookresearch/DPR. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of facebookresearch/DPR?
    pass
    AI named facebookresearch/DPR explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts facebookresearch/DPR in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/DPR explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo facebookresearch/DPR solve, and who is the primary audience?
    pass
    AI did not name facebookresearch/DPR — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of facebookresearch/DPR. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/facebookresearch/DPR.svg)](https://repogeo.com/en/r/facebookresearch/DPR)
HTML
<a href="https://repogeo.com/en/r/facebookresearch/DPR"><img src="https://repogeo.com/badge/facebookresearch/DPR.svg" alt="RepoGEO" /></a>
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facebookresearch/DPR — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite