RRepoGEO

REPOGEO REPORT · LITE

huggingface/setfit

Default branch main · commit c8155900 · scanned 5/27/2026, 8:02:43 PM

GitHub: 2,742 stars · 262 forks

AI VISIBILITY SCORE
92 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
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 huggingface/setfit, 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
  • mediumreadme#1
    Strengthen README's opening problem statement

    Why:

    CURRENT
    SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers.
    COPY-PASTE FIX
    SetFit is an efficient and prompt-free framework designed to solve the problem of training effective text classifiers with very few labeled examples, by few-shot fine-tuning of Sentence Transformers.
  • mediumtopics#2
    Add 'text-classification' to repository topics

    Why:

    CURRENT
    few-shot-learning, nlp, sentence-transformers
    COPY-PASTE FIX
    few-shot-learning, nlp, sentence-transformers, text-classification
  • mediumabout#3
    Refine repository 'About' description for problem-solution framing

    Why:

    CURRENT
    Efficient few-shot learning with Sentence Transformers
    COPY-PASTE FIX
    Solve few-shot text classification with SetFit: efficient, prompt-free fine-tuning of Sentence Transformers.

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
2 / 2
100% of queries surface huggingface/setfit
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
BERT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. BERT · recommended 1×
  2. RoBERTa · recommended 1×
  3. XLM-R · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. GPT-3.5 · recommended 1×
  • CATEGORY QUERY
    What are the best methods for few-shot text classification using minimal training data?
    you: #1
    AI recommended (in order):
    1. SetFit ← you
    2. BERT
    3. RoBERTa
    4. XLM-R
    5. Hugging Face Transformers
    6. GPT-3.5
    7. GPT-4
    8. Claude
    9. Sentence Transformer
    10. all-MiniLM-L6-v2
    11. paraphrase-mpnet-base-v2
    12. KNN
    13. SVM
    14. Prototypical Networks
    15. MAML
    16. Reptile
    Show full AI answer
  • CATEGORY QUERY
    Tools for efficient few-shot text classification that don't require manual prompt engineering?
    you: #1
    AI recommended (in order):
    1. SetFit (huggingface/setfit) ← you
    2. Lightly (lightly-ai/lightly)
    3. OpenAI API
    4. Hugging Face Transformers with PEFT (huggingface/transformers)
    5. Snorkel (snorkel-team/snorkel)
    6. Prodigy
    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 huggingface/setfit?
    pass
    AI named huggingface/setfit explicitly

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

  • If a team adopts huggingface/setfit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/setfit 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 huggingface/setfit solve, and who is the primary audience?
    pass
    AI named huggingface/setfit 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 huggingface/setfit. 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/huggingface/setfit.svg)](https://repogeo.com/en/r/huggingface/setfit)
HTML
<a href="https://repogeo.com/en/r/huggingface/setfit"><img src="https://repogeo.com/badge/huggingface/setfit.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

huggingface/setfit — 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