RRepoGEO

REPOGEO REPORT · LITE

thunlp/OpenNRE

Default branch master · commit 8e42fd71 · scanned 5/27/2026, 9:47:37 PM

GitHub: 4,463 stars · 1,052 forks

AI VISIBILITY SCORE
66 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 thunlp/OpenNRE, 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
    Reposition README opening to clarify toolkit for NRE research

    Why:

    CURRENT
    OpenNRE is a sub-project of OpenSKL, providing an **Opensource **N**eural **R**elation **E**xtraction toolkit for extracting structured knowledge from plain text, with ATT as key features to consider relation-associated text information.
    COPY-PASTE FIX
    OpenNRE is an open-source and extensible **Neural Relation Extraction (NRE) toolkit** designed for **NLP researchers and developers** to easily implement, evaluate, and compare state-of-the-art NRE models for structured knowledge extraction and knowledge graph construction.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    [Insert URL to project website, documentation, or related academic page here]
  • lowtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    relation-extraction
    COPY-PASTE FIX
    relation-extraction, neural-relation-extraction, knowledge-graph-construction, nlp-toolkit, pytorch

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
1 / 2
50% of queries surface thunlp/OpenNRE
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
spaCy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. spaCy · recommended 2×
  2. OpenIE (Open Information Extraction) in Stanford CoreNLP · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. AllenNLP · recommended 1×
  5. SetFit (Sentence Transformer Fine-tuning for Few-Shot Classification) · recommended 1×
  • CATEGORY QUERY
    How can I automatically extract structured relationships between entities from unstructured text data?
    you: not recommended
    AI recommended (in order):
    1. OpenIE (Open Information Extraction) in Stanford CoreNLP
    2. spaCy
    3. Hugging Face Transformers
    4. AllenNLP
    5. SetFit (Sentence Transformer Fine-tuning for Few-Shot Classification)
    6. Rasa NLU
    7. Apache OpenNLP

    AI recommended 7 alternatives but never named thunlp/OpenNRE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source Python library helps build knowledge graphs by extracting relations from text?
    you: #2
    AI recommended (in order):
    1. spaCy
    2. OpenNRE ← you
    3. Haystack
    4. Textacy
    5. Stanza
    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 thunlp/OpenNRE?
    pass
    AI named thunlp/OpenNRE explicitly

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

  • If a team adopts thunlp/OpenNRE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thunlp/OpenNRE 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 thunlp/OpenNRE solve, and who is the primary audience?
    pass
    AI named thunlp/OpenNRE explicitly

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

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thunlp/OpenNRE — 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