RRepoGEO

REPOGEO REPORT · LITE

dair-ai/Transformers-Recipe

Default branch main · commit a8d8c7c3 · scanned 6/25/2026, 8:23:15 PM

GitHub: 1,637 stars · 163 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
35 /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
3 / 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 dair-ai/Transformers-Recipe, 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
    Clarify README's opening statement to emphasize 'study guide' and 'curated resources'

    Why:

    CURRENT
    Transformers have accelerated the development of new techniques and models for natural language processing (NLP) tasks. While it has mostly been used for NLP tasks, it is now seeing heavy adoption in other areas such as computer vision and reinforcement learning. That makes it one of the most important modern concepts to understand and be able to apply. I am aware that a lot of machine learning and NLP students and practitioners are keen on learning about transformers. Therefore, I have prepared a study guide in the form of a list of resources and study materials to help guide students interested in learning about the world of Transformers.
    COPY-PASTE FIX
    This repository is a curated study guide and resource list designed to help machine learning and NLP students and practitioners understand and apply Transformer models. It provides a structured path through essential materials, from high-level introductions to in-depth architectural explanations, making it easier to navigate the complex world of Transformers.
  • mediumtopics#2
    Add more specific topics related to learning and study guides

    Why:

    CURRENT
    ai, deep-learning, machine-learning, natural-language-processing, nlp
    COPY-PASTE FIX
    ai, deep-learning, machine-learning, natural-language-processing, nlp, transformers, study-guide, learning-resources, educational
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://dair.ai

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 dair-ai/Transformers-Recipe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
The Illustrated Transformer
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. The Illustrated Transformer · recommended 1×
  2. huggingface/transformers · recommended 1×
  3. Attention Is All You Need · recommended 1×
  4. Transformers for Natural Language Processing · recommended 1×
  5. Stanford CS224N: Natural Language Processing with Deep Learning · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive guide to understand transformer models for NLP?
    you: not recommended
    AI recommended (in order):
    1. The Illustrated Transformer
    2. Hugging Face Transformers Documentation (huggingface/transformers)
    3. Attention Is All You Need
    4. Transformers for Natural Language Processing
    5. Stanford CS224N: Natural Language Processing with Deep Learning

    AI recommended 5 alternatives but never named dair-ai/Transformers-Recipe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best resources for a deep dive into transformer architecture and implementation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch
    3. PyTorch's nn.Transformer module
    4. TensorFlow
    5. TensorFlow's tf.keras.layers.MultiHeadAttention
    6. TensorFlow's tf.keras.layers.Transformer

    AI recommended 6 alternatives but never named dair-ai/Transformers-Recipe. 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 dair-ai/Transformers-Recipe?
    pass
    AI named dair-ai/Transformers-Recipe explicitly

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

  • If a team adopts dair-ai/Transformers-Recipe in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dair-ai/Transformers-Recipe 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 dair-ai/Transformers-Recipe solve, and who is the primary audience?
    pass
    AI named dair-ai/Transformers-Recipe explicitly

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

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dair-ai/Transformers-Recipe — RepoGEO report