RRepoGEO

REPOGEO REPORT · LITE

brightmart/bert_language_understanding

Default branch master · commit cbe1db7b · scanned 6/16/2026, 10:58:10 PM

GitHub: 964 stars · 211 forks

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 brightmart/bert_language_understanding, 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
  • highlicense#1
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Add a `LICENSE` file to the repository root, for example, using the Apache-2.0 license.
  • highreadme#2
    Reposition the README H1 to clarify the project's purpose

    Why:

    CURRENT
    # Ideas from google's bert for language understanding: Pre-train TextCNN
    COPY-PASTE FIX
    # TensorFlow Implementation for BERT-inspired Language Understanding: Pre-training and Fine-tuning TextCNN Models
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a URL to the repository's homepage field (e.g., a project page, documentation, or a demo).

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 brightmart/bert_language_understanding
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. Hugging Face Transformers Library · recommended 1×
  3. PyTorch · recommended 1×
  4. torchtext · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How can I pre-train a deep language model for various text understanding applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch
    3. torchtext
    4. TensorFlow
    5. Keras
    6. DeepSpeed
    7. Megatron-LM
    8. JAX
    9. Flax
    10. Haiku

    AI recommended 10 alternatives but never named brightmart/bert_language_understanding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for fine-tuning pre-trained models for document classification tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Keras
    3. fast.ai
    4. spaCy
    5. PyTorch Lightning
    6. Flair
    7. AllenNLP

    AI recommended 7 alternatives but never named brightmart/bert_language_understanding. 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 brightmart/bert_language_understanding?
    pass
    AI named brightmart/bert_language_understanding explicitly

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

  • If a team adopts brightmart/bert_language_understanding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named brightmart/bert_language_understanding 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 brightmart/bert_language_understanding solve, and who is the primary audience?
    pass
    AI did not name brightmart/bert_language_understanding — 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?

Embed your GEO score

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brightmart/bert_language_understanding — 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