RRepoGEO

REPOGEO REPORT · LITE

AnswerDotAI/ModernBERT

Default branch main · commit c6d94231 · scanned 6/23/2026, 10:27:56 AM

GitHub: 1,696 stars · 146 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 AnswerDotAI/ModernBERT, 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 H1 and opening paragraph to clarify research focus

    Why:

    CURRENT
    # Welcome!

This is the repository where you can find ModernBERT, our experiments to bring BERT into modernity via both architecture changes and scaling.
    COPY-PASTE FIX
    # ModernBERT: Research Repository for Next-Gen BERT Architectures and Scaling

This repository hosts ModernBERT, our cutting-edge research and experiments focused on advancing BERT models through novel architectural changes and efficient scaling techniques. It introduces FlexBERT, a modular approach to encoder building blocks, and is designed for researchers and practitioners exploring the frontiers of BERT pre-training and evaluation.
  • mediumtopics#2
    Add more specific topics to improve indexing

    Why:

    CURRENT
    bert, embeddings, llm, nlp
    COPY-PASTE FIX
    bert, embeddings, llm, nlp, transformer-architecture, model-scaling, modular-ai, flexbert, flash-attention
  • mediumreadme#3
    Add a clear 'Purpose and Audience' section to the README

    Why:

    COPY-PASTE FIX
    ## Purpose and Audience

This repository serves as the **research and experimental codebase for ModernBERT**, focusing on advanced pre-training, architectural innovations (like FlexBERT), and evaluation. It is primarily intended for researchers and developers interested in contributing to or understanding the core advancements of BERT-style models.

**For production-ready integration and general use with common NLP pipelines, please refer to the official ModernBERT Collection on HuggingFace.**

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 AnswerDotAI/ModernBERT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/optimum
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/optimum · recommended 3×
  2. huggingface/transformers · recommended 2×
  3. tensorflow/model-optimization · recommended 2×
  4. pytorch/pytorch · recommended 2×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    How can I improve the architecture and scaling of existing BERT models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Optimum (huggingface/optimum)
    3. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    4. Hugging Face Optimum (huggingface/optimum)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. OpenVINO (openvinotoolkit/openvino)
    7. NVIDIA TensorRT (NVIDIA/TensorRT)
    8. PyTorch Quantization API (pytorch/pytorch)
    9. Hugging Face Transformers (huggingface/transformers)
    10. Hugging Face Optimum (huggingface/optimum)
    11. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    12. DeepSpeed (microsoft/DeepSpeed)
    13. FairScale (facebookresearch/fairscale)
    14. PyTorch DistributedDataParallel (pytorch/pytorch)
    15. RoBERTa (facebookresearch/RoBERTa)
    16. ALBERT (google-research/ALBERT)
    17. ELECTRA (google-research/electra)
    18. Longformer (allenai/longformer)
    19. BigBird (google-research/bigbird)

    AI recommended 19 alternatives but never named AnswerDotAI/ModernBERT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a modular framework for building custom encoder models with state-of-the-art attention.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. Keras
    4. JAX/Flax
    5. Haiku
    6. Trax

    AI recommended 6 alternatives but never named AnswerDotAI/ModernBERT. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 AnswerDotAI/ModernBERT?
    pass
    AI named AnswerDotAI/ModernBERT explicitly

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

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

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

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AnswerDotAI/ModernBERT — 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