RRepoGEO

REPOGEO REPORT · LITE

loujie0822/Pre-trained-Models

Default branch master · commit 680cf017 · scanned 6/7/2026, 11:13:02 PM

GitHub: 545 stars · 107 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 loujie0822/Pre-trained-Models, 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 clear English introductory sentence to the README

    Why:

    COPY-PASTE FIX
    Add this line immediately after the H1: `This repository provides a comprehensive, continuously updated survey and collection of resources on Pre-trained Language Models (PLMs) in NLP.`
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    nlp, pre-trained-models, language-models, plms, deep-learning, machine-learning, survey, papers, research, model-compression, self-supervised-learning
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to clearly state the terms of use for the content.

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 loujie0822/Pre-trained-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Word2Vec
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Word2Vec · recommended 1×
  2. GloVe · recommended 1×
  3. ELMo · recommended 1×
  4. GPT · recommended 1×
  5. BERT · recommended 1×
  • CATEGORY QUERY
    Explain the core concepts and evolution of pre-trained language models in NLP.
    you: not recommended
    AI recommended (in order):
    1. Word2Vec
    2. GloVe
    3. ELMo
    4. GPT
    5. BERT
    6. RoBERTa
    7. XLNet
    8. T5
    9. GPT-2
    10. GPT-3
    11. GPT-4

    AI recommended 11 alternatives but never named loujie0822/Pre-trained-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the most effective techniques for optimizing and compressing large language models?
    you: not recommended
    AI recommended (in order):
    1. AutoGPTQ
    2. bitsandbytes
    3. AWQ (Activation-aware Weight Quantization)
    4. ONNX Runtime
    5. PyTorch's native QAT tools
    6. TensorFlow Model Optimization Toolkit
    7. SparseML
    8. Hugging Face Optimum
    9. Hugging Face Transformers
    10. LoRA (Low-Rank Adaptation)
    11. PEFT (Parameter-Efficient Fine-tuning) library
    12. FlashAttention
    13. Google's Medusa
    14. TensorRT
    15. OpenVINO
    16. TVM (Apache TVM)

    AI recommended 16 alternatives but never named loujie0822/Pre-trained-Models. 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 loujie0822/Pre-trained-Models?
    pass
    AI did not name loujie0822/Pre-trained-Models — 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 loujie0822/Pre-trained-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named loujie0822/Pre-trained-Models 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 loujie0822/Pre-trained-Models solve, and who is the primary audience?
    pass
    AI did not name loujie0822/Pre-trained-Models — 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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loujie0822/Pre-trained-Models — 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