RRepoGEO

REPOGEO REPORT · LITE

appvision-ai/fast-bert

Default branch main · commit cff2f913 · scanned 6/18/2026, 9:11:39 PM

GitHub: 1,916 stars · 339 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
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 the README's opening to highlight core value proposition

    Why:

    CURRENT
    # Fast-Bert

[](https://github.com/deepmipt/DeepPavlov/blob/master/LICENSE)
[](https://badge.fury.io/py/fast-bert)

**New - Learning Rate Finder for Text Classification Training...
    COPY-PASTE FIX
    # Fast-Bert: Super Easy BERT/XLNet Fine-tuning for Text Classification (fast.ai inspired)

Fast-Bert is a deep learning library designed to simplify and accelerate the training and deployment of BERT and XLNet based models for natural language processing tasks, starting with Text Classification. Inspired by fast.ai, it makes cutting-edge deep learning technologies accessible for data scientists and machine learning practitioners.
  • hightopics#2
    Expand repository topics to include specific tasks and benefits

    Why:

    CURRENT
    bert, fast-bert, fastai, transformers
    COPY-PASTE FIX
    bert, fast-bert, fastai, transformers, nlp, text-classification, fine-tuning, deep-learning-library, pytorch
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    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
Keras
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Keras · recommended 2×
  2. PyTorch Lightning · recommended 2×
  3. Hugging Face Transformers Trainer API · recommended 1×
  4. Simple Transformers · recommended 1×
  5. Ludwig · recommended 1×
  • CATEGORY QUERY
    What are simple tools for fine-tuning transformer models on custom text classification datasets?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Trainer API
    2. Simple Transformers
    3. Keras
    4. PyTorch Lightning
    5. Ludwig

    AI recommended 5 alternatives but never named appvision-ai/fast-bert. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library to quickly train and deploy advanced natural language processing models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. spaCy
    3. Flair
    4. Keras
    5. PyTorch Lightning
    6. fast.ai
    7. AllenNLP

    AI recommended 7 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
  • Prioritized action items8 vs 3 in Lite