RRepoGEO

REPOGEO REPORT · LITE

ShannonAI/mrc-for-flat-nested-ner

Default branch master · commit 457b0759 · scanned 6/1/2026, 3:04:01 PM

GitHub: 679 stars · 118 forks

AI VISIBILITY SCORE
17 /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
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 ShannonAI/mrc-for-flat-nested-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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to clarify repo's purpose and audience

    Why:

    CURRENT
    # A Unified MRC Framework for Named Entity Recognition 
The repository contains the code of the recent research advances in Shannon.AI. 

**A Unified MRC Framework for Named Entity Recognition** <br>
Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu and Jiwei Li<br>
In ACL 2020. paper<br>
    COPY-PASTE FIX
    # A Unified MRC Framework for Named Entity Recognition 

This repository provides the official PyTorch implementation for the ACL 2020 paper "A Unified MRC Framework for Named Entity Recognition" by Li et al. It offers a novel approach to both flat and nested Named Entity Recognition by reframing the task as Machine Reading Comprehension. Researchers and practitioners can use this code to reproduce results, experiment with MRC-based NER, and apply the framework to their own NLP tasks.

**A Unified MRC Framework for Named Entity Recognition** <br>
Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu and Jiwei Li<br>
In ACL 2020. paper<br>
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of the MIT License.

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 ShannonAI/mrc-for-flat-nested-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. AllenNLP · recommended 1×
  3. spaCy · recommended 1×
  4. DeepPavlov · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How to implement named entity recognition using a machine reading comprehension approach?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. AllenNLP
    3. spaCy
    4. DeepPavlov
    5. PyTorch
    6. TensorFlow

    AI recommended 6 alternatives but never named ShannonAI/mrc-for-flat-nested-ner. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective deep learning methods for unified flat and nested named entity recognition?
    you: not recommended
    AI recommended (in order):
    1. Span-BERT
    2. Span-RoBERTa
    3. Biaffine Span Parser
    4. AllenNLP (allenai/allennlp)
    5. BERT
    6. RoBERTa
    7. GlobalPointer
    8. T5
    9. BART

    AI recommended 9 alternatives but never named ShannonAI/mrc-for-flat-nested-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
    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 ShannonAI/mrc-for-flat-nested-ner?
    pass
    AI did not name ShannonAI/mrc-for-flat-nested-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 ShannonAI/mrc-for-flat-nested-ner in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ShannonAI/mrc-for-flat-nested-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 ShannonAI/mrc-for-flat-nested-ner solve, and who is the primary audience?
    pass
    AI did not name ShannonAI/mrc-for-flat-nested-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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ShannonAI/mrc-for-flat-nested-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