RRepoGEO

REPOGEO REPORT · LITE

nadavbra/protein_bert

Default branch master · commit 69a1122b · scanned 6/3/2026, 12:03:12 AM

GitHub: 576 stars · 109 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 nadavbra/protein_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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    protein-language-model, protein-bert, deep-learning, tensorflow, keras, bioinformatics, protein-sequence-analysis, machine-learning, transformer-models, long-sequence-modeling, state-of-the-art
  • highabout#2
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    ProteinBERT is a state-of-the-art protein language model built on Keras/TensorFlow, pretrained on ~106M proteins, featuring global-attention layers for efficient processing of extremely long protein sequences.
  • mediumreadme#3
    Reposition README's opening to highlight long sequence processing

    Why:

    CURRENT
    What is ProteinBERT? ProteinBERT is a protein language model pretrained on ~106M proteins from UniRef90. The pretrained model can be fine-tuned on any protein-related task in a matter of minutes. ProteinBERT achieves state-of-the-art performance on a wide range of benchmarks. ProteinBERT is built on Keras/TensorFlow.
    COPY-PASTE FIX
    What is ProteinBERT? ProteinBERT is a state-of-the-art protein language model pretrained on ~106M proteins from UniRef90. Built on Keras/TensorFlow, it features innovative global-attention layers that enable efficient processing of extremely long protein sequences (tens of thousands of amino acids) with linear complexity. The pretrained model can be fine-tuned on any protein-related task in minutes, achieving state-of-the-art performance on a wide range of benchmarks.

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 nadavbra/protein_bert
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ProtTrans
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ProtTrans · recommended 1×
  2. ESM Models · recommended 1×
  3. Hugging Face Transformers Library · recommended 1×
  4. BERT · recommended 1×
  5. RoBERTa · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune a large language model for protein sequence analysis tasks?
    you: not recommended
    AI recommended (in order):
    1. ProtTrans
    2. ESM Models
    3. Hugging Face Transformers Library
    4. BERT
    5. RoBERTa
    6. OpenFold
    7. BioNeMo
    8. DeepMind's Gato

    AI recommended 8 alternatives but never named nadavbra/protein_bert. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What deep learning models efficiently process extremely long protein sequences for function prediction?
    you: not recommended
    AI recommended (in order):
    1. HyenaDNA
    2. Longformer
    3. Performer
    4. Reformer
    5. Linformer
    6. LSTMs
    7. GRUs
    8. BiLSTMs
    9. TCNs
    10. Attention-Free Transformers

    AI recommended 10 alternatives but never named nadavbra/protein_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
    fail

    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 nadavbra/protein_bert?
    pass
    AI did not name nadavbra/protein_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 nadavbra/protein_bert in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nadavbra/protein_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 nadavbra/protein_bert solve, and who is the primary audience?
    pass
    AI named nadavbra/protein_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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