RRepoGEO

REPOGEO REPORT · LITE

shibing624/textgen

Default branch main · commit 172fc00a · scanned 6/10/2026, 12:56:51 AM

GitHub: 979 stars · 113 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 shibing624/textgen, 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 H1/Introduction to highlight unique value

    Why:

    CURRENT
    # TextGen: Implementation of Text Generation models
    COPY-PASTE FIX
    # TextGen: Out-of-the-box Text Generation models for fine-tuning LLMs, with a focus on Chinese NLP.
  • mediumtopics#2
    Add topics to emphasize Chinese NLP focus

    Why:

    CURRENT
    bart, bert, chatglm, chatgpt, gpt2, llama, seq2seq, t5, text-generation, textgen, xlnet
    COPY-PASTE FIX
    bart, bert, chatglm, chatgpt, gpt2, llama, seq2seq, t5, text-generation, textgen, xlnet, chinese-nlp, chinese-llm
  • mediumabout#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://github.com/shibing624/textgen

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 shibing624/textgen
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. PyTorch-Lightning · recommended 1×
  3. Keras · recommended 1×
  4. OpenNMT-py · recommended 1×
  5. fairseq · recommended 1×
  • CATEGORY QUERY
    Looking for a Python library to implement and fine-tune various text generation models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch-Lightning
    3. Keras
    4. OpenNMT-py
    5. fairseq
    6. DeepSpeed

    AI recommended 6 alternatives but never named shibing624/textgen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I easily experiment with and deploy diverse large language models for text generation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers & Inference API
    2. transformers
    3. Hugging Face Hub
    4. Hugging Face Spaces
    5. Hugging Face Inference Endpoints
    6. OpenAI API
    7. OpenAI Playground UI
    8. Google Cloud Vertex AI
    9. Generative AI Studio
    10. Anthropic API
    11. Anthropic Console
    12. Replicate
    13. LM Studio
    14. Ollama
    15. AWS SageMaker JumpStart
    16. AWS SageMaker

    AI recommended 16 alternatives but never named shibing624/textgen. 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 shibing624/textgen?
    pass
    AI named shibing624/textgen explicitly

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

  • If a team adopts shibing624/textgen in production, what risks or prerequisites should they evaluate first?
    pass
    AI named shibing624/textgen 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 shibing624/textgen solve, and who is the primary audience?
    pass
    AI named shibing624/textgen 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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shibing624/textgen — 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