RRepoGEO

REPOGEO REPORT · LITE

namisan/mt-dnn

Default branch master · commit 3228e7c2 · scanned 6/30/2026, 9:27:13 PM

GitHub: 2,260 stars · 408 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
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 namisan/mt-dnn, 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
    Add a concise, problem-solution statement at the very top of the README

    Why:

    CURRENT
    The current README starts with 'New Release' and 'Update' sections before the main title and description.
    COPY-PASTE FIX
    MT-DNN is a powerful PyTorch-based framework designed for building a single, robust deep learning model that achieves state-of-the-art performance across diverse Natural Language Understanding (NLU) tasks. It provides an efficient multi-task learning solution, eliminating the need to train separate models for each NLU application.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL (e.g., a GitHub Pages site or documentation portal) to the repository's 'About' section.
  • lowtopics#3
    Add 'nlu-framework' and 'pytorch-library' to repository topics

    Why:

    CURRENT
    bert, deep-learning, machine-reading-comprehension, microsoft, multi-task-learning, named-entity-recognition, natural-language-understanding, nlp, pytorch, ranking
    COPY-PASTE FIX
    bert, deep-learning, machine-reading-comprehension, microsoft, multi-task-learning, named-entity-recognition, natural-language-understanding, nlp, pytorch, ranking, nlu-framework, pytorch-library

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 namisan/mt-dnn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. BERT · recommended 1×
  3. RoBERTa · recommended 1×
  4. XLNet · recommended 1×
  5. T5 · recommended 1×
  • CATEGORY QUERY
    How to build a single deep learning model for various natural language understanding tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. BERT
    3. RoBERTa
    4. XLNet
    5. T5
    6. GPT-3/GPT-4
    7. OpenAI API
    8. SpaCy (explosion/spaCy)
    9. Flair (flairNLP/flair)
    10. AllenNLP (allenai/allennlp)
    11. TensorFlow Text (tensorflow/text)
    12. TensorFlow Hub

    AI recommended 12 alternatives but never named namisan/mt-dnn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What PyTorch libraries enable multi-task deep neural networks for advanced NLP applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. AllenNLP
    4. DeepPavlov
    5. Catalyst
    6. fairseq

    AI recommended 6 alternatives but never named namisan/mt-dnn. 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 namisan/mt-dnn?
    pass
    AI named namisan/mt-dnn explicitly

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

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

    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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namisan/mt-dnn — 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