RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/fairseq-lua

Default branch main · commit 7a6fff0f · scanned 5/27/2026, 5:08:06 AM

GitHub: 3,726 stars · 604 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/fairseq-lua, 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
  • highabout#1
    Update the repository description to explicitly mention Lua/Torch and its status

    Why:

    CURRENT
    Facebook AI Research Sequence-to-Sequence Toolkit
    COPY-PASTE FIX
    Facebook AI Research Sequence-to-Sequence Toolkit for Lua/Torch (legacy, unsupported)
  • mediumreadme#2
    Clarify the license information in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Specify License Name(s) here, e.g., a custom Facebook Research License or a combination of licenses]. Please refer to the LICENSE file for full details.

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/fairseq-lua
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/fairseq
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/fairseq · recommended 1×
  2. OpenNMT/OpenNMT-py · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. tensorflow/tensor2tensor · recommended 1×
  5. marian-nmt/marian · recommended 1×
  • CATEGORY QUERY
    What are robust open-source toolkits for sequence-to-sequence learning in neural machine translation?
    you: not recommended
    AI recommended (in order):
    1. fairseq (facebookresearch/fairseq)
    2. OpenNMT-py (OpenNMT/OpenNMT-py)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Tensor2Tensor (T2T) (tensorflow/tensor2tensor)
    5. Marian NMT (marian-nmt/marian)

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

    Show full AI answer
  • CATEGORY QUERY
    Which deep learning frameworks support sequence-to-sequence models specifically within the Torch ecosystem?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers
    3. fairseq
    4. PyTorch-Lightning
    5. OpenNMT-py

    AI recommended 5 alternatives but never named facebookresearch/fairseq-lua. 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/fairseq-lua?
    pass
    AI named facebookresearch/fairseq-lua 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/fairseq-lua in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/fairseq-lua 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/fairseq-lua solve, and who is the primary audience?
    pass
    AI named facebookresearch/fairseq-lua 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/fairseq-lua — 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