RRepoGEO

REPOGEO REPORT · LITE

hikariming/chat-dataset-baseline

Default branch main · commit d6fefc9b · scanned 5/27/2026, 2:53:22 AM

GitHub: 1,191 stars · 97 forks

AI VISIBILITY SCORE
28 /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
2 / 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 hikariming/chat-dataset-baseline, 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 to highlight the curated dataset

    Why:

    CURRENT
    # 🚀 进化中的中文对话模型资源库 🚀
    COPY-PASTE FIX
    # 🚀 人工精调中文对话数据集：LLM微调基线与代码 🚀
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory of the repository with the Apache-2.0 license text.
  • mediumhomepage#3
    Set the GitHub repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/hikariming/chat-dataset-baseline

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 hikariming/chat-dataset-baseline
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LCCC (Large-scale Cleaned Chinese Conversation Corpus)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LCCC (Large-scale Cleaned Chinese Conversation Corpus) · recommended 1×
  2. Douban Conversation Corpus · recommended 1×
  3. SMP-MCC (SMP Chinese Conversational Corpus) · recommended 1×
  4. OpenSubtitles Chinese Corpus · recommended 1×
  5. Tsinghua-CMRC (Tsinghua Chinese Machine Reading Comprehension) · recommended 1×
  • CATEGORY QUERY
    Need a curated dataset to fine-tune a conversational AI model for Mandarin Chinese.
    you: not recommended
    AI recommended (in order):
    1. LCCC (Large-scale Cleaned Chinese Conversation Corpus)
    2. Douban Conversation Corpus
    3. SMP-MCC (SMP Chinese Conversational Corpus)
    4. OpenSubtitles Chinese Corpus
    5. Tsinghua-CMRC (Tsinghua Chinese Machine Reading Comprehension)
    6. WMT Chinese-English Parallel Corpus

    AI recommended 6 alternatives but never named hikariming/chat-dataset-baseline. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for SFT training a Chinese LLM with custom data?
    you: not recommended
    AI recommended (in order):
    1. Baichuan2-7B/13B-Chat
    2. Qwen-7B/14B-Chat
    3. ChatGLM3-6B
    4. Yi-6B/34B-Chat
    5. Llama-2-7B/13B-Chat
    6. LoRA
    7. QLoRA
    8. Hugging Face's PEFT (huggingface/peft)
    9. AdamW
    10. Hugging Face Transformers (huggingface/transformers)
    11. Accelerate (huggingface/accelerate)
    12. DeepSpeed (microsoft/DeepSpeed)
    13. FSDP
    14. TensorBoard
    15. Weights & Biases (W&B) (wandb/wandb)

    AI recommended 15 alternatives but never named hikariming/chat-dataset-baseline. 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 hikariming/chat-dataset-baseline?
    pass
    AI did not name hikariming/chat-dataset-baseline — 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 hikariming/chat-dataset-baseline in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hikariming/chat-dataset-baseline 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 hikariming/chat-dataset-baseline solve, and who is the primary audience?
    pass
    AI named hikariming/chat-dataset-baseline 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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hikariming/chat-dataset-baseline — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite