RRepoGEO

REPOGEO REPORT · LITE

huggingface/swift-coreml-transformers

Default branch master · commit 47cb600b · scanned 5/11/2026, 4:38:41 PM

GitHub: 1,684 stars · 175 forks

AI VISIBILITY SCORE
28 /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
2 / 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-coreml-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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the archived status to clarify the repo's purpose first

    Why:

    CURRENT
    # This repo is not actively maintained and has been archived. For an in-development replacement, please head over to swift-transformers!
    COPY-PASTE FIX
    # Swift Core ML implementations of Transformers: GPT-2, DistilGPT-2, BERT, DistilBERT for on-device NLP

*Note: This repo is not actively maintained and has been archived. For an in-development replacement, please head over to swift-transformers!*
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    swift, coreml, transformers, nlp, gpt2, bert, distilbert, machine-learning, ios, on-device-ai
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.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
0 / 2
0% of queries surface huggingface/swift-coreml-transformers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
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. coremltools · recommended 2×
  3. Hugging Face Transformers · recommended 2×
  4. TensorFlow Lite · recommended 2×
  5. MLX · recommended 1×
  • CATEGORY QUERY
    How can I run large language models like GPT-2 for text generation on-device with Swift?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. coremltools
    3. Hugging Face Transformers
    4. MLX
    5. mlx-examples
    6. ONNX Runtime
    7. onnx-coreml
    8. tf2onnx
    9. llama.cpp
    10. TensorFlow Lite
    11. TensorFlowLiteSwift
    12. tokenizers

    AI recommended 12 alternatives but never named huggingface/swift-coreml-transformers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to implement BERT-based question answering in a Swift iOS application?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Hugging Face Transformers
    3. coremltools
    4. transformers
    5. Hugging Face optimum
    6. Turi Create
    7. TensorFlow Lite
    8. PyTorch Mobile

    AI recommended 8 alternatives but never named huggingface/swift-coreml-transformers. 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 huggingface/swift-coreml-transformers?
    pass
    AI named huggingface/swift-coreml-transformers 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/swift-coreml-transformers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/swift-coreml-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-coreml-transformers solve, and who is the primary audience?
    pass
    AI did not name huggingface/swift-coreml-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?

Embed your GEO score

Drop this badge into the README of huggingface/swift-coreml-transformers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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huggingface/swift-coreml-transformers — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite