RRepoGEO

REPOGEO REPORT · LITE

kaiyinzhou/BERT-NER

Default branch master · commit 0f77e478 · scanned 5/28/2026, 12:53:11 AM

GitHub: 1,279 stars · 325 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 kaiyinzhou/BERT-NER, 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 README's opening to clarify purpose and audience

    Why:

    CURRENT
    ## For better performance, you can try NLPGNN, see NLPGNN for more details.

# BERT-NER Version 2

Use Google's BERT for named entity recognition （CoNLL-2003 as the dataset）.
    COPY-PASTE FIX
    # BERT-NER Version 2: A Clear & Simple Implementation for Named Entity Recognition with Google's BERT

This repository provides a straightforward, annotated implementation for Named Entity Recognition (NER) using Google's BERT model, specifically fine-tuned on the CoNLL-2003 dataset. It's designed for NLP practitioners, researchers, and students looking for a clear example to quickly understand and adapt BERT for NER tasks, focusing on data preprocessing and layer design.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/kaiyinzhou/BERT-NER
  • lowreadme#3
    Relocate the NLPGNN mention to a 'Related Projects' section

    Why:

    CURRENT
    ## For better performance, you can try NLPGNN, see NLPGNN for more details.
    COPY-PASTE FIX
    ## Related Projects

For exploring alternative or potentially higher-performing approaches, consider NLPGNN.

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 kaiyinzhou/BERT-NER
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Flair
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Flair · recommended 2×
  2. AllenNLP · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. spaCy · recommended 1×
  5. Keras/TensorFlow · recommended 1×
  • CATEGORY QUERY
    What are effective deep learning approaches for identifying named entities in text?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. spaCy
    3. Flair
    4. AllenNLP
    5. Keras/TensorFlow
    6. PyTorch

    AI recommended 6 alternatives but never named kaiyinzhou/BERT-NER. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to fine-tune a large pre-trained language model for named entity extraction?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. SpaCy
    3. spacy-transformers
    4. KerasNLP
    5. Flair
    6. AllenNLP
    7. Prodigy
    8. Doccano
    9. Label Studio

    AI recommended 9 alternatives but never named kaiyinzhou/BERT-NER. 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 kaiyinzhou/BERT-NER?
    pass
    AI named kaiyinzhou/BERT-NER explicitly

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

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

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

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kaiyinzhou/BERT-NER — 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