RRepoGEO

REPOGEO REPORT · LITE

thunlp/ERNIE

Default branch master · commit 514cbe42 · scanned 5/11/2026, 1:53:40 PM

GitHub: 1,420 stars · 264 forks

AI VISIBILITY SCORE
35 /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
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/ERNIE, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    nlp, language-model, knowledge-graph, entity-typing, relation-classification, pytorch, pre-trained-models, deep-learning, ai, machine-learning
  • highreadme#2
    Reposition README's opening to emphasize knowledge-enhanced NLP

    Why:

    CURRENT
    ERNIE is a sub-project of OpenSKL, providing an open-sourced toolkit (Enhanced language Representation with Informative Entities) for augmenting pre-trained language models with knowledge graph representations.
    COPY-PASTE FIX
    ERNIE is a knowledge-enhanced pre-trained language model toolkit, developed as a sub-project of OpenSKL, for augmenting language models with knowledge graph representations.
  • mediumabout#3
    Refine repository description for clarity

    Why:

    CURRENT
    Source code and dataset for ACL 2019 paper "ERNIE: Enhanced Language Representation with Informative Entities"
    COPY-PASTE FIX
    ERNIE: A knowledge-enhanced pre-trained language model toolkit and dataset from the ACL 2019 paper "Enhanced Language Representation with Informative Entities".

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 thunlp/ERNIE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DGL (Deep Graph Library)
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. DGL (Deep Graph Library) · recommended 3×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Weaviate · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How can I enhance language models with external knowledge graphs for better understanding?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Weaviate
    4. Pinecone
    5. PyTorch-BigGraph (PBG)
    6. DGL (Deep Graph Library)
    7. Ampligraph
    8. DGL (Deep Graph Library)
    9. PyTorch Geometric (PyG)
    10. Hugging Face Transformers
    11. OpenNRE (Open-source Neural Relation Extraction)

    AI recommended 11 alternatives but never named thunlp/ERNIE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for improving entity typing and relation classification using knowledge?
    you: not recommended
    AI recommended (in order):
    1. TransE
    2. RotatE
    3. ComplEx
    4. DGL (Deep Graph Library)
    5. PyG (PyTorch Geometric)
    6. ERNIE (Enhanced Representation through kNowledge IntEgration)
    7. K-BERT
    8. LUKE (Language Understanding with Knowledge-based Embeddings)
    9. SpaCy's Matcher
    10. Stardog
    11. OpenNRE
    12. Snorkel

    AI recommended 12 alternatives but never named thunlp/ERNIE. 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 thunlp/ERNIE?
    pass
    AI named thunlp/ERNIE 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/ERNIE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thunlp/ERNIE 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/ERNIE solve, and who is the primary audience?
    pass
    AI named thunlp/ERNIE explicitly

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

Embed your GEO score

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MARKDOWN (README)
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thunlp/ERNIE — 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