RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/pytext

Default branch main · commit 08754b48 · scanned 5/11/2026, 4:26:39 PM

GitHub: 6,299 stars · 789 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/pytext, 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
    Add a sentence to the README clarifying PyText's historical significance

    Why:

    COPY-PASTE FIX
    This project was notable for its configuration-driven approach to building and deploying deep learning NLP models, serving as a precursor to current PyTorch NLP frameworks. While deprecated, it offers valuable insights into the evolution of PyTorch-based NLP frameworks.
  • mediumreadme#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms found in the LICENSE file. Please refer to the LICENSE file for specific details regarding usage and distribution.

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/pytext
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch-Lightning · recommended 1×
  3. spaCy · recommended 1×
  4. AllenNLP · recommended 1×
  5. Catalyst · recommended 1×
  • CATEGORY QUERY
    What deep learning framework is best for NLP tasks using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch-Lightning
    3. spaCy
    4. AllenNLP
    5. Catalyst

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

    Show full AI answer
  • CATEGORY QUERY
    How can I quickly build and deploy production-ready NLP models with PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. PyTorch Lightning
    4. FastAI
    5. ONNX Runtime
    6. TorchServe
    7. NVIDIA Triton Inference Server

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

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

Embed your GEO score

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MARKDOWN (README)
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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite