RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/pytext

Default branch main · commit 08754b48 · scanned 6/21/2026, 9:26:58 PM

GitHub: 6,298 stars · 785 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/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
  • mediumreadme#1
    Add a clear license statement to the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms specified in the LICENSE file. Please refer to the LICENSE file for full details.
  • lowreadme#2
    Rephrase the 'Overview' section in past tense

    Why:

    CURRENT
    PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale.
    COPY-PASTE FIX
    PyText was a deep-learning based NLP modeling framework built on PyTorch. It addressed the often-conflicting requirements of enabling rapid experimentation and of serving models at scale.

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
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Lightning-AI/lightning · recommended 1×
  3. explosion/spaCy · recommended 1×
  4. flairNLP/flair · recommended 1×
  5. catalyst-team/catalyst · recommended 1×
  • CATEGORY QUERY
    What are good deep learning NLP frameworks built on PyTorch for production use?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch-Lightning (Lightning-AI/lightning)
    3. spaCy (explosion/spaCy)
    4. Flair (flairNLP/flair)
    5. Catalyst (catalyst-team/catalyst)

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

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch-based NLP library for scalable text classification and rapid experimentation.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch-Lightning
    3. Flair
    4. Keras
    5. Catalyst

    AI recommended 5 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?

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facebookresearch/pytext — 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