RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/lingua

Default branch main · commit 437d680e · scanned 6/28/2026, 10:58:57 PM

GitHub: 4,760 stars · 273 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
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 facebookresearch/lingua, 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
    llm, large-language-models, pytorch, deep-learning, machine-learning, ai-research, model-training, inference, nlp
  • highreadme#2
    Reposition the README's opening sentence to emphasize LLM research

    Why:

    CURRENT
    Meta Lingua is a minimal and fast LLM training and inference library designed for research. Meta Lingua uses easy-to-modify PyTorch components in order to try new architectures, losses, data, etc.
    COPY-PASTE FIX
    Meta Lingua is a lean, efficient, and easy-to-hack **PyTorch library for Large Language Model (LLM) research**, focusing on training, inference, and evaluation. It provides easy-to-modify components for experimenting with new architectures, losses, and data.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://facebookresearch.github.io/lingua/ (or a link to relevant documentation/project page)

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/lingua
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Lightning · recommended 2×
  2. Hugging Face Accelerate · recommended 1×
  3. Catalyst · recommended 1×
  4. PyTorch-Ignite · recommended 1×
  5. Plain PyTorch · recommended 1×
  • CATEGORY QUERY
    What are some lean PyTorch frameworks for experimenting with new LLM architectures?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Hugging Face Accelerate
    3. Catalyst
    4. PyTorch-Ignite
    5. Plain PyTorch

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

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient, end-to-end library for LLM training, inference, and evaluation.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. OpenAI API
    5. Triton Inference Server

    AI recommended 5 alternatives but never named facebookresearch/lingua. 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/lingua?
    pass
    AI did not name facebookresearch/lingua — 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 facebookresearch/lingua in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/lingua 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/lingua solve, and who is the primary audience?
    pass
    AI named facebookresearch/lingua explicitly

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

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facebookresearch/lingua — 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