RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/multimodal

Default branch main · commit 3c2a85a3 · scanned 6/27/2026, 7:22:42 PM

GitHub: 1,724 stars · 172 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 facebookresearch/multimodal, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    pytorch, multimodal, deep-learning, machine-learning, computer-vision, natural-language-processing, generative-ai, multi-task-learning, research, facebookresearch
  • highreadme#2
    Refine the README's introductory sentence to emphasize framework scope

    Why:

    CURRENT
    **TorchMultimodal** is a PyTorch library for training state-of-the-art multimodal multi-task models at scale, including both content understanding and generative models.
    COPY-PASTE FIX
    **TorchMultimodal** is a comprehensive PyTorch *framework* and *ecosystem* for building and training state-of-the-art multimodal multi-task models at scale, supporting both content understanding and generative AI research.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://pytorch.org/multimodal

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/multimodal
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Lightning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Lightning · recommended 1×
  2. Hugging Face Transformers · recommended 1×
  3. TensorFlow · recommended 1×
  4. JAX · recommended 1×
  5. Keras · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for training multi-task models with diverse data inputs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Hugging Face Transformers
    3. TensorFlow
    4. JAX
    5. Keras

    AI recommended 5 alternatives but never named facebookresearch/multimodal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a modular Python library for multimodal model development with pre-trained components.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. OpenCLIP (mlfoundations/open_clip)
    3. PyTorch-Lightning (Lightning-AI/lightning)
    4. MMDetection (open-mmlab/mmdetection)
    5. MMEngine (open-mmlab/mmengine)
    6. Keras (keras-team/keras)
    7. DeepPavlov (deepmipt/DeepPavlov)

    AI recommended 7 alternatives but never named facebookresearch/multimodal. 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/multimodal?
    pass
    AI named facebookresearch/multimodal 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/multimodal in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/multimodal 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/multimodal solve, and who is the primary audience?
    pass
    AI named facebookresearch/multimodal 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/multimodal. 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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facebookresearch/multimodal — 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