RRepoGEO

REPOGEO REPORT · LITE

macanv/BERT-BiLSTM-CRF-NER

Default branch master · commit ccf3f093 · scanned 6/19/2026, 11:47:27 PM

GitHub: 4,904 stars · 1,245 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
22 /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
1 / 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 macanv/BERT-BiLSTM-CRF-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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. Choose and add the text for a standard open-source license (e.g., MIT, Apache-2.0) that best suits your project.
  • highreadme#2
    Reposition the README's opening to highlight Chinese NER and service deployment

    Why:

    CURRENT
    # BERT-BiLSTM-CRF-NER
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning
    COPY-PASTE FIX
    # BERT-BiLSTM-CRF-NER
A production-ready TensorFlow solution for Chinese Named Entity Recognition (NER) using BERT fine-tuning, with a simple Flask service for deployment.
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    bert, bert-bilstm-crf, blstm, crf, named-entity-recognition, ner
    COPY-PASTE FIX
    bert, bert-bilstm-crf, blstm, crf, named-entity-recognition, ner, chinese-ner, tensorflow-ner, nlp-service, flask-service

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 macanv/BERT-BiLSTM-CRF-NER
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. spaCy · recommended 1×
  3. Flair · recommended 1×
  4. PyTorch · recommended 1×
  5. PyTorch Lightning · recommended 1×
  • CATEGORY QUERY
    How to build a robust named entity recognition system using advanced neural architectures?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. spaCy
    3. Flair
    4. PyTorch
    5. PyTorch Lightning
    6. TensorFlow/Keras
    7. AllenNLP

    AI recommended 7 alternatives but never named macanv/BERT-BiLSTM-CRF-NER. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a deployable named entity recognition service, especially for processing Chinese text.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Natural Language API
    2. Baidu AI Cloud NLP
    3. Amazon Comprehend
    4. Microsoft Azure Cognitive Services for Language
    5. Hugging Face Transformers (huggingface/transformers)
    6. spaCy (explosion/spaCy)
    7. OpenNMT (OpenNMT/OpenNMT-py)

    AI recommended 7 alternatives but never named macanv/BERT-BiLSTM-CRF-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 macanv/BERT-BiLSTM-CRF-NER?
    pass
    AI did not name macanv/BERT-BiLSTM-CRF-NER — 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 macanv/BERT-BiLSTM-CRF-NER in production, what risks or prerequisites should they evaluate first?
    pass
    AI named macanv/BERT-BiLSTM-CRF-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 macanv/BERT-BiLSTM-CRF-NER solve, and who is the primary audience?
    pass
    AI did not name macanv/BERT-BiLSTM-CRF-NER — 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?

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macanv/BERT-BiLSTM-CRF-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