RRepoGEO

REPOGEO REPORT · LITE

BatsResearch/bonito

Default branch main · commit 1e1efb23 · scanned 6/3/2026, 1:58:30 AM

GitHub: 825 stars · 56 forks

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 BatsResearch/bonito, 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
    Clarify Bonito's domain in the README's opening sentence

    Why:

    CURRENT
    Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. This repo is a lightweight library for Bonito to easily create synthetic datasets built on top of the Hugging Face `transformers` and `vllm` libraries.
    COPY-PASTE FIX
    Bonito is a lightweight Python library for generating high-quality synthetic instruction tuning datasets for Large Language Models (LLMs) from unannotated text, without relying on proprietary APIs like GPT. It provides an open-source model for conditional task generation, built on Hugging Face `transformers` and `vllm`.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/BatsResearch/bonito
  • lowtopics#3
    Add 'instruction-tuning' to the repository topics

    Why:

    CURRENT
    domain-adaptation, gpt, llm, synthetic-data, synthetic-dataset-generation, task-adaptation, zero-shot-learning
    COPY-PASTE FIX
    domain-adaptation, gpt, llm, synthetic-data, synthetic-dataset-generation, task-adaptation, zero-shot-learning, instruction-tuning

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 BatsResearch/bonito
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. Llama 2 · recommended 1×
  3. Mistral · recommended 1×
  4. Falcon · recommended 1×
  5. BERT · recommended 1×
  • CATEGORY QUERY
    How to create instruction tuning datasets from unlabeled text without using proprietary LLMs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Llama 2
    3. Mistral
    4. Falcon
    5. BERT
    6. RoBERTa
    7. t5-base-finetuned-question-generation-ap
    8. valhalla/t5-base-qg-hl
    9. deepset/roberta-base-squad2
    10. distilbert-base-cased-distilled-squad
    11. facebook/bart-large-cnn
    12. google/pegasus-xsum
    13. PEGASUS
    14. T5
    15. tuner007/pegasus_paraphrase
    16. t5-base
    17. SpaCy
    18. NLTK

    AI recommended 18 alternatives but never named BatsResearch/bonito. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for generating synthetic datasets to fine-tune LLMs for specific downstream tasks.
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Synthetic Data Vault (SDV) (sdv-dev/SDV)
    3. Hugging Face `datasets` library (huggingface/datasets)
    4. Snorkel AI
    5. Faker (joke2k/faker)
    6. DataSynthesizer (DataResponsibly/DataSynthesizer)

    AI recommended 6 alternatives but never named BatsResearch/bonito. 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 BatsResearch/bonito?
    pass
    AI named BatsResearch/bonito explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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