RRepoGEO

REPOGEO REPORT · LITE

yuanxiaosc/Entity-Relation-Extraction

Default branch master · commit 17bb6ef1 · scanned 5/22/2026, 9:53:20 PM

GitHub: 1,231 stars · 270 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)

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

AI VISIBILITY SCORE
22 /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
1 / 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 yuanxiaosc/Entity-Relation-Extraction, 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.

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 yuanxiaosc/Entity-Relation-Extraction
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
spaCy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. spaCy · recommended 1×
  2. NLTK · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. flair · recommended 1×
  5. Stanford CoreNLP · recommended 1×
  • CATEGORY QUERY
    How to extract structured knowledge triples from unstructured text using a pipeline approach?
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. NLTK
    3. Hugging Face Transformers
    4. flair
    5. Stanford CoreNLP
    6. OpenNRE
    7. SetFit
    8. neuralcoref
    9. huggingface/neuralcoref

    AI recommended 9 alternatives but never named yuanxiaosc/Entity-Relation-Extraction. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for schema-guided entity and relation extraction from natural language sentences.
    you: not recommended
    AI recommended (in order):
    1. OpenNRE (thunlp/OpenNRE)
    2. SpaCy (explosion/spaCy)
    3. AllenNLP (allenai/allennlp)
    4. Haystack (deepset-ai/haystack)
    5. SetFit (huggingface/setfit)
    6. Stanford CoreNLP (stanfordnlp/CoreNLP)

    AI recommended 6 alternatives but never named yuanxiaosc/Entity-Relation-Extraction. 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 yuanxiaosc/Entity-Relation-Extraction?
    pass
    AI did not name yuanxiaosc/Entity-Relation-Extraction — 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?

  • If a team adopts yuanxiaosc/Entity-Relation-Extraction in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yuanxiaosc/Entity-Relation-Extraction 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 yuanxiaosc/Entity-Relation-Extraction solve, and who is the primary audience?
    pass
    AI did not name yuanxiaosc/Entity-Relation-Extraction — 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 yuanxiaosc/Entity-Relation-Extraction. 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/yuanxiaosc/Entity-Relation-Extraction.svg)](https://repogeo.com/en/r/yuanxiaosc/Entity-Relation-Extraction)
HTML
<a href="https://repogeo.com/en/r/yuanxiaosc/Entity-Relation-Extraction"><img src="https://repogeo.com/badge/yuanxiaosc/Entity-Relation-Extraction.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

yuanxiaosc/Entity-Relation-Extraction — 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