RRepoGEO

REPOGEO REPORT · LITE

urchade/GLiNER

Default branch main · commit dfc00617 · scanned 5/24/2026, 1:17:13 AM

GitHub: 3,207 stars · 275 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 urchade/GLiNER, 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 README's opening statement to emphasize zero-shot and lightweight nature

    Why:

    CURRENT
    GLiNER is a framework for training and deploying small Named Entity Recognition (NER) models with zero-shot capabilities.
    COPY-PASTE FIX
    GLiNER is a **zero-shot, lightweight, and generalist** framework for Named Entity Recognition (NER) that allows you to extract *any* entity types from text *without extensive model retraining*.
  • mediumtopics#2
    Add specific topics for zero-shot, custom entity extraction, and lightweight NLP

    Why:

    CURRENT
    information-extraction, large-language-models, named-entity-recognition, natural-language-processing, prompt-tuning
    COPY-PASTE FIX
    information-extraction, large-language-models, named-entity-recognition, natural-language-processing, prompt-tuning, zero-shot-ner, custom-entity-extraction, lightweight-nlp
  • mediumcomparison#3
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., "## Why Choose GLiNER? (vs. Traditional NER & LLMs)" with content like: "Unlike traditional NER models (e.g., spaCy, Flair) that require extensive labeled datasets and fine-tuning for new entity types, GLiNER offers **zero-shot entity extraction** out-of-the-box. It's also optimized to be **lightweight and efficient**, providing competitive performance with LLMs several times its size, making it ideal for resource-constrained environments and rapid prototyping without retraining."

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 urchade/GLiNER
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
spaCy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. spaCy · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. Python's re module · recommended 1×
  4. FuzzyWuzzy · recommended 1×
  5. Rasa NLU · recommended 1×
  • CATEGORY QUERY
    How to extract custom entity types from text without extensive model retraining?
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. Python's re module
    3. FuzzyWuzzy
    4. Hugging Face Transformers
    5. Rasa NLU
    6. Google Cloud Natural Language API

    AI recommended 6 alternatives but never named urchade/GLiNER. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a lightweight model for efficient named entity recognition in natural language processing.
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. Flair
    3. Stanza
    4. Hugging Face Transformers
    5. DistilBERT
    6. TinyBERT
    7. MiniLM
    8. NLTK

    AI recommended 8 alternatives but never named urchade/GLiNER. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 urchade/GLiNER?
    pass
    AI named urchade/GLiNER explicitly

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

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

    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 urchade/GLiNER. 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/urchade/GLiNER.svg)](https://repogeo.com/en/r/urchade/GLiNER)
HTML
<a href="https://repogeo.com/en/r/urchade/GLiNER"><img src="https://repogeo.com/badge/urchade/GLiNER.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

urchade/GLiNER — 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