RRepoGEO

REPOGEO REPORT · LITE

msgi/nlp-journey

Default branch master · commit 830ea07c · scanned 6/28/2026, 8:57:19 PM

GitHub: 1,628 stars · 376 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
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 msgi/nlp-journey, 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
  • hightopics#1
    Expand repository topics to improve category matching

    Why:

    CURRENT
    deep-learning, paper
    COPY-PASTE FIX
    natural-language-processing, nlp, deep-learning, machine-learning, transformers, topic-modeling, word-embeddings, named-entity-recognition, text-classification, text-generation, text-similarity, machine-translation, learning-path, resources, papers, code-examples
  • mediumreadme#2
    Add a descriptive subtitle to the README H1

    Why:

    CURRENT
    # nlp journey
    COPY-PASTE FIX
    # nlp journey: A Curated Learning Path and Resource Collection for Natural Language Processing
  • lowabout#3
    Refine the repository description to emphasize its curated nature

    Why:

    CURRENT
    Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc.
    COPY-PASTE FIX
    A curated collection of documents, papers, and code examples for a comprehensive Natural Language Processing learning journey, covering Topic Models, Word Embeddings, Named Entity Recognition, Text Classification, Text Generation, Text Similarity, and Machine Translation.

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 msgi/nlp-journey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. Hugging Face · recommended 1×
  3. NLP Progress · recommended 1×
  4. sebastianruder/awesome-nlp · recommended 1×
  5. Distill.pub · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated collection of deep learning NLP papers and code?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Hugging Face
    3. NLP Progress
    4. Awesome NLP (sebastianruder/awesome-nlp)
    5. Distill.pub
    6. arXiv

    AI recommended 6 alternatives but never named msgi/nlp-journey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the essential resources for understanding modern transformer architectures in NLP?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. Keras (keras-team/keras)

    AI recommended 4 alternatives but never named msgi/nlp-journey. 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 msgi/nlp-journey?
    pass
    AI did not name msgi/nlp-journey — likely talking about a different project

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

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

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

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msgi/nlp-journey — 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