RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/XLM

Default branch main · commit cd281d32 · scanned 7/1/2026, 5:38:00 PM

GitHub: 2,929 stars · 499 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/XLM, 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
  • highreadme#1
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a line near the top of the README: "This project is licensed under the terms specified in the [LICENSE](LICENSE) file, which contains a custom research license."
  • mediumreadme#2
    Emphasize XLM's unique contributions in the README's opening

    Why:

    CURRENT
    PyTorch original implementation of Cross-lingual Language Model Pretraining. Includes:
    COPY-PASTE FIX
    XLM is the PyTorch original implementation of Cross-lingual Language Model Pretraining, notably introducing **Translation Language Modeling (TLM)** to effectively leverage parallel data. This repository includes:

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/XLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. Fairseq · recommended 1×
  3. spaCy · recommended 1×
  4. Google Cloud Translation API · recommended 1×
  5. DeepL API · recommended 1×
  • CATEGORY QUERY
    How can I implement cross-lingual language understanding for text classification or machine translation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Fairseq
    3. spaCy
    4. Google Cloud Translation API
    5. DeepL API
    6. Sentence-BERT (SBERT)

    AI recommended 6 alternatives but never named facebookresearch/XLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective tools for pretraining multilingual language models on large datasets efficiently?
    you: not recommended
    AI recommended (in order):
    1. Fairseq (facebookresearch/fairseq)
    2. Hugging Face Transformers (huggingface/transformers)
    3. Megatron-LM (NVIDIA/Megatron-LM)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. TensorFlow (tensorflow/tensorflow)

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