RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/MetaCLIP

Default branch main · commit f47f7841 · scanned 5/9/2026, 6:42:59 PM

GitHub: 1,836 stars · 76 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 facebookresearch/MetaCLIP, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highhomepage#1
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2507.22062
  • mediumreadme#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project's licensing terms are detailed in the LICENSE file.

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 facebookresearch/MetaCLIP
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 6 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 6×
  2. Flickr30k · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. Hugging Face Datasets · recommended 1×
  5. CLIP · recommended 1×
  • CATEGORY QUERY
    How to build a vision-language model with strong performance across multiple languages?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. CLIP
    4. OpenCLIP
    5. BLIP
    6. BLIP-2
    7. LLaVA
    8. mPLUG-Owl
    9. CogVLM
    10. Conceptual Captions
    11. LAION-5B
    12. MS COCO
    13. Flickr30k
    14. PyTorch Lightning
    15. OpenNMT-py
    16. timm
    17. ViT
    18. ResNet
    19. Swin Transformer
    20. Fairseq
    21. TensorFlow
    22. Keras
    23. TensorFlow Hub

    AI recommended 23 alternatives but never named facebookresearch/MetaCLIP. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for training large-scale multilingual CLIP models effectively?
    you: not recommended
    AI recommended (in order):
    1. LAION-5B (laion-ai/laion-5b)
    2. Common Crawl
    3. Multilingual Wikipedia Dumps
    4. XLM-RoBERTa (XLM-R) (huggingface/transformers)
    5. mBERT (Multilingual BERT) (huggingface/transformers)
    6. mT5 (Multilingual T5) (huggingface/transformers)
    7. CamemBERT (huggingface/transformers)
    8. RuBERT (huggingface/transformers)
    9. ViT (Vision Transformer) (huggingface/transformers)
    10. OpenCLIP (mlfoundations/open_clip)
    11. CoCa (Contrastive Captioners) (lucidrains/CoCa-pytorch)
    12. PyTorch Lightning (Lightning-AI/lightning)
    13. DeepSpeed (microsoft/DeepSpeed)
    14. Hugging Face Accelerate (huggingface/accelerate)
    15. AdamW Optimizer
    16. NVIDIA Apex (NVIDIA/apex)
    17. PyTorch's native AMP (pytorch/pytorch)
    18. ImageNet
    19. COCO
    20. Flickr30k
    21. Multi30k

    AI recommended 21 alternatives but never named facebookresearch/MetaCLIP. 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 facebookresearch/MetaCLIP?
    pass
    AI named facebookresearch/MetaCLIP explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of facebookresearch/MetaCLIP. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite