RRepoGEO

REPOGEO REPORT · LITE

huggingface/swift-transformers

Default branch main · commit 2fa33e1f · scanned 5/16/2026, 2:07:12 PM

GitHub: 1,326 stars · 180 forks

AI VISIBILITY SCORE
49 /100
Critical
Category recall
1 / 2
Avg rank #3.0 when recommended
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 huggingface/swift-transformers, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening paragraph to clarify its purpose for local inference

    Why:

    CURRENT
    `swift-transformers` is a collection of utilities to help adopt language models in Swift apps.
    COPY-PASTE FIX
    `swift-transformers` is a native Swift library for efficient, on-device (or server-side Swift) inference with Hugging Face transformer models, enabling advanced AI capabilities directly within your Apple applications (iOS, macOS, watchOS, tvOS).
  • lowhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://swiftpackageindex.com/huggingface/swift-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
1 / 2
50% of queries surface huggingface/swift-transformers
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. OpenAI API · recommended 1×
  4. Google Gemini API · recommended 1×
  5. Hugging Face Inference API · recommended 1×
  • CATEGORY QUERY
    How can I integrate large language models into my Swift iOS application?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Google Gemini API
    3. Hugging Face Inference API
    4. Transformers.js
    5. Core ML
    6. Hugging Face Transformers
    7. MLX
    8. Llama.cpp

    AI recommended 8 alternatives but never named huggingface/swift-transformers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Swift libraries are available for efficient text tokenization and model inference?
    you: #3
    AI recommended (in order):
    1. Core ML
    2. Hugging Face Transformers
    3. Swift-Transformers (huggingface/swift-transformers) ← you
    4. MLX (ml-explore/mlx)
    5. Natural Language
    6. Swift-NLP (Swift-NLP/Swift-NLP)
    7. Tokenizers (huggingface/tokenizers)
    8. ONNX Runtime (microsoft/onnxruntime)
    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 huggingface/swift-transformers?
    pass
    AI did not name huggingface/swift-transformers — 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 huggingface/swift-transformers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/swift-transformers 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/swift-transformers solve, and who is the primary audience?
    pass
    AI did not name huggingface/swift-transformers — 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?

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