RRepoGEO

REPOGEO REPORT · LITE

pytorch/text

Default branch main · commit a5e61063 · scanned 5/13/2026, 9:02:05 AM

GitHub: 3,562 stars · 810 forks

AI VISIBILITY SCORE
62 /100
Needs work
Category recall
1 / 2
Avg rank #5.0 when recommended
Rule findings
2 pass · 0 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 pytorch/text, 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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    The README currently goes from the `torchtext` heading directly to the "WARNING" or component list.
    COPY-PASTE FIX
    Add the following sentence immediately after the `torchtext` heading: "TorchText provides models, data loaders, and abstractions for efficient language processing tasks, powered by PyTorch."
  • highreadme#2
    Reposition core value proposition above deprecation warning

    Why:

    CURRENT
    The current README starts with images, then the "WARNING", then the description of `torchtext` components.
    COPY-PASTE FIX
    Move the section starting with "This repository consists of:" and its bullet points to appear immediately after the new introductory sentence (from the previous action item) and *before* the "WARNING" section.
  • mediumtopics#3
    Add more specific topics for text data processing

    Why:

    CURRENT
    data-loader, dataset, deep-learning, models, nlp, pytorch
    COPY-PASTE FIX
    data-loader, dataset, deep-learning, models, nlp, pytorch, text-processing, text-data

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
1 / 2
50% of queries surface pytorch/text
Avg rank
#5.0
Lower is better. #1 = top recommendation.
Share of voice
7%
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. huggingface/datasets · recommended 1×
  3. explosion/spaCy · recommended 1×
  4. nltk/nltk · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    What are good libraries for preparing text data and using pre-trained NLP models in PyTorch?
    you: #5
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Datasets (huggingface/datasets)
    3. spaCy (explosion/spaCy)
    4. NLTK (nltk/nltk)
    5. TorchText (pytorch/text) ← you
    Show full AI answer
  • CATEGORY QUERY
    How can I efficiently load common NLP datasets and build deep learning models in Python?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. PyTorch
    4. torchtext
    5. TensorFlow
    6. tf.keras
    7. TensorFlow Datasets (tfds)
    8. Keras
    9. KerasNLP
    10. spaCy

    AI recommended 10 alternatives but never named pytorch/text. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 pytorch/text?
    pass
    AI named pytorch/text explicitly

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

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

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

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pytorch/text — 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