RRepoGEO

REPOGEO REPORT · LITE

microsoft/MASS

Default branch master · commit 779f22fc · scanned 5/9/2026, 2:47:27 AM

GitHub: 1,121 stars · 204 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 microsoft/MASS, 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
    Strengthen the README's opening sentence to explicitly state its NLP domain

    Why:

    CURRENT
    # MASS

MASS: Masked Sequence to Sequence Pre-training for Language Generation, by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu, is a novel pre-training method for sequence to sequence based language generation tasks.
    COPY-PASTE FIX
    # MASS

This repository presents **MASS (Masked Sequence to Sequence Pre-training for Language Generation)**, a novel pre-training method for sequence-to-sequence based Natural Language Processing (NLP) tasks. Developed by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu, MASS randomly masks a sentence fragment in the encoder and predicts it in the decoder.
  • mediumreadme#2
    Clarify the project's license terms in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details, as it contains specific terms and conditions.

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 microsoft/MASS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
T5
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. T5 · recommended 1×
  2. BART · recommended 1×
  3. GPT-2 · recommended 1×
  4. GPT-3 · recommended 1×
  5. LLaMA · recommended 1×
  • CATEGORY QUERY
    What are effective pre-training techniques for sequence-to-sequence language generation tasks?
    you: not recommended
    AI recommended (in order):
    1. T5
    2. BART
    3. GPT-2
    4. GPT-3
    5. LLaMA
    6. mBART
    7. PEGASUS
    8. SimCSE

    AI recommended 8 alternatives but never named microsoft/MASS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to pre-train models for neural machine translation and text summarization?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Fairseq
    3. OpenNMT-py
    4. Tensor2Tensor (T2T)
    5. Marian NMT

    AI recommended 5 alternatives but never named microsoft/MASS. 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 microsoft/MASS?
    pass
    AI named microsoft/MASS explicitly

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

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

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

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microsoft/MASS — 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