RRepoGEO

REPOGEO REPORT · LITE

appvision-ai/fast-bert

Default branch main · commit cff2f913 · scanned 5/9/2026, 2:16:59 AM

GitHub: 1,919 stars · 340 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 appvision-ai/fast-bert, 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 README's opening to highlight 'easy-to-use' and 'fast.ai-inspired' for BERT/XLNet fine-tuning

    Why:

    CURRENT
    Fast-Bert is the deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification. The work on FastBert is built on solid foundations provided by the excellent Hugging Face BERT PyTorch library and is inspired by fast.ai and strives to make the cutting edge deep learning technologies accessible for the vast community of machine learning practitioners.
    COPY-PASTE FIX
    Fast-Bert is an easy-to-use, fast.ai-inspired deep learning library that simplifies fine-tuning and deploying BERT and XLNet models for natural language processing tasks, starting with multi-class and multi-label text classification. Built on Hugging Face Transformers, it makes cutting-edge NLP accessible for data scientists and developers.
  • mediumtopics#2
    Expand repository topics to include specific NLP tasks and methods

    Why:

    CURRENT
    bert, fast-bert, fastai, transformers
    COPY-PASTE FIX
    bert, fast-bert, fastai, transformers, text-classification, nlp, fine-tuning, deep-learning-library, multi-label-classification
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/appvision-ai/fast-bert

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 appvision-ai/fast-bert
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. keras-team/keras · recommended 1×
  3. Lightning-AI/lightning · recommended 1×
  4. ThilinaRajapakse/simpletransformers · recommended 1×
  5. ludwig-ai/ludwig · recommended 1×
  • CATEGORY QUERY
    What's an easy-to-use library for fine-tuning large language models for text classification?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Keras (keras-team/keras)
    3. PyTorch Lightning (Lightning-AI/lightning)
    4. Simple Transformers (ThilinaRajapakse/simpletransformers)
    5. Ludwig (ludwig-ai/ludwig)
    6. FastAI (fastai/fastai)

    AI recommended 6 alternatives but never named appvision-ai/fast-bert. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a tool for efficient multi-label text classification with pre-trained transformer models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Keras
    3. PyTorch Lightning
    4. fast.ai
    5. Flair

    AI recommended 5 alternatives but never named appvision-ai/fast-bert. 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 appvision-ai/fast-bert?
    pass
    AI did not name appvision-ai/fast-bert — 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 appvision-ai/fast-bert in production, what risks or prerequisites should they evaluate first?
    pass
    AI named appvision-ai/fast-bert 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 appvision-ai/fast-bert solve, and who is the primary audience?
    pass
    AI named appvision-ai/fast-bert explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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