RRepoGEO

REPOGEO REPORT · LITE

huggingface/swift-transformers

Default branch main · commit 50843f91 · scanned 6/27/2026, 11:26:26 AM

GitHub: 1,341 stars · 189 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
62 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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-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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    swift, transformers, nlp, machine-learning, deep-learning, huggingface, coreml, on-device-ml, swift-package, apple
  • highreadme#2
    Strengthen the README's opening sentence to highlight Hugging Face integration

    Why:

    CURRENT
    `swift-transformers` is a collection of utilities to help adopt language models in Swift apps.
    COPY-PASTE FIX
    `swift-transformers` is a Swift Package that provides an idiomatic Swift API for integrating Hugging Face Transformer models and tokenizers into your Swift applications, often leveraging Core ML for on-device inference.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    A relevant URL, such as a dedicated documentation page, project website, or the main Hugging Face blog post announcing the library.

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
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
apple/coremltools
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. apple/coremltools · recommended 1×
  2. NaturalLanguage Framework · recommended 1×
  3. apple/swift-nio · recommended 1×
  4. google/sentencepiece · recommended 1×
  5. Core ML · recommended 1×
  • CATEGORY QUERY
    Seeking a Swift package for efficient text tokenization and language model integration.
    you: #1
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/swift-transformers) ← you
    2. Core ML Tools (apple/coremltools)
    3. NaturalLanguage Framework
    4. SwiftNIO (apple/swift-nio)
    5. SentencePiece (google/sentencepiece)
    Show full AI answer
  • CATEGORY QUERY
    Are there Swift alternatives to popular Python NLP model libraries for mobile development?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Create ML
    3. Hugging Face Transformers.swift
    4. Natural Language
    5. Swift-NLP
    6. Turi Create
    7. TensorFlow Lite
    8. PyTorch Mobile

    AI recommended 8 alternatives but never named huggingface/swift-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-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 named huggingface/swift-transformers explicitly

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

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huggingface/swift-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