RRepoGEO

REPOGEO REPORT · LITE

huggingface/swift-coreml-transformers

Default branch master · commit 47cb600b · scanned 6/21/2026, 9:37:43 PM

GitHub: 1,682 stars · 175 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 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

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

OVERALL DIRECTION
  • highabout#1
    Refine the repository description to highlight key capabilities and archived status

    Why:

    CURRENT
    Swift Core ML 3 implementations of GPT-2, DistilGPT-2, BERT, and DistilBERT for Question answering. Other Transformers coming soon!
    COPY-PASTE FIX
    Archived: Swift Core ML 3 implementations of GPT-2, DistilGPT-2, BERT, and DistilBERT for on-device inference on Apple platforms (iOS/macOS), enabling Question Answering and Text Generation.
  • mediumhomepage#2
    Add a homepage link to the replacement repository

    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. microsoft/onnxruntime · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. coremltools · recommended 1×
  5. transformers · recommended 1×
  • CATEGORY QUERY
    How can I implement on-device text generation using transformer models in a Swift application?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Hugging Face Transformers
    3. coremltools
    4. transformers
    5. MLModel
    6. Create ML
    7. swift-transformers
    8. ONNX Runtime

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

    Show full AI answer
  • CATEGORY QUERY
    What are good options for running BERT or GPT-2 models directly on iOS devices?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Hugging Face Transformers (huggingface/transformers)
    3. coremltools (apple/coremltools)
    4. ONNX Runtime (microsoft/onnxruntime)
    5. onnx-coreml (onnx/onnx-coreml)
    6. TensorFlow Lite (tensorflow/tensorflow)
    7. PyTorch Mobile (pytorch/pytorch)
    8. MLX (ml-explore/mlx)
    9. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 9 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 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?

  • 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

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MARKDOWN (README)
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huggingface/swift-coreml-transformers — 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