RRepoGEO

REPOGEO REPORT · LITE

joeynmt/joeynmt

Default branch main · commit cdc4d03d · scanned 6/8/2026, 12:26:47 AM

GitHub: 710 stars · 226 forks

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 joeynmt/joeynmt, 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
    Enhance the main README title (H1) to immediately convey its core educational and minimalist purpose

    Why:

    CURRENT
    #   Joey NMT
    COPY-PASTE FIX
    # Joey NMT: A Minimalist PyTorch Framework for Learning Neural Machine Translation
  • mediumhomepage#2
    Add a project homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://joeynmt.github.io/
  • mediumreadme#3
    Add a sentence to the 'Goal and Purpose' section that explicitly differentiates Joey NMT from other popular educational NMT resources

    Why:

    CURRENT
    In contrast to other NMT frameworks, we will **not** aim for the most recent features or speed through engineering or training tricks since this often goes in hand with an increase in code complexity and a decrease in readability. :eyes:
    COPY-PASTE FIX
    In contrast to other NMT frameworks, we will **not** aim for the most recent features or speed through engineering or training tricks since this often goes in hand with an increase in code complexity and a decrease in readability. :eyes: Unlike conceptual guides such as 'The Annotated Transformer', Joey NMT provides a complete, runnable framework for hands-on experimentation and modification.

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 joeynmt/joeynmt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
harvardnlp/annotated-transformer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. harvardnlp/annotated-transformer · recommended 2×
  2. PyTorch Tutorials · recommended 1×
  3. PyTorch Examples · recommended 1×
  4. allenai/allennlp · recommended 1×
  5. spro/practical-pytorch · recommended 1×
  • CATEGORY QUERY
    How to learn neural machine translation concepts with a simple PyTorch implementation?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Tutorials
    2. PyTorch Examples
    3. The Annotated Transformer (harvardnlp/annotated-transformer)
    4. AllenNLP (allenai/allennlp)
    5. Practical PyTorch (spro/practical-pytorch)

    AI recommended 5 alternatives but never named joeynmt/joeynmt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a clean PyTorch codebase to understand seq2seq and transformer NMT architectures.
    you: not recommended
    AI recommended (in order):
    1. OpenNMT-py (OpenNMT/OpenNMT-py)
    2. Hugging Face Transformers (huggingface/transformers)
    3. PyTorch Examples (seq2seq-translation) (pytorch/examples)
    4. Harvard NLP's "The Annotated Transformer" (harvardnlp/annotated-transformer)
    5. fairseq (facebookresearch/fairseq)
    6. Tensor2Tensor (tensorflow/tensor2tensor)

    AI recommended 6 alternatives but never named joeynmt/joeynmt. 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 joeynmt/joeynmt?
    pass
    AI named joeynmt/joeynmt explicitly

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

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

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

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joeynmt/joeynmt — 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