RRepoGEO

REPOGEO REPORT · LITE

JohnSnowLabs/spark-nlp

Default branch master · commit fcede1de · scanned 7/1/2026, 8:02:07 PM

GitHub: 4,137 stars · 742 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 JohnSnowLabs/spark-nlp, 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
    Explicitly state the full repository name and its core differentiator in the opening paragraph

    Why:

    CURRENT
    Spark NLP is a state-of-the-art Natural Language Processing library built on top of Apache Spark. It provides **simple**, **performant** & **accurate** NLP annotations for machine learning pipelines that **scale** easily in a distributed environment.
    COPY-PASTE FIX
    JohnSnowLabs/spark-nlp is the leading state-of-the-art Natural Language Processing library built natively on Apache Spark. It provides **simple**, **performant**, and **accurate** NLP annotations for machine learning pipelines that **scale** easily in distributed environments, making it ideal for large datasets and production-grade applications.
  • mediumreadme#2
    Add a dedicated section highlighting key differentiators and comprehensive model coverage

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., 'Why JohnSnowLabs/spark-nlp?' or 'Key Differentiators', that explicitly highlights its native Spark integration, scalability, and the **100,000+** pretrained **pipelines** and **models** in **200+** languages, contrasting these with other popular NLP libraries.
  • lowtopics#3
    Enhance topics with more specific distributed computing and Spark ML keywords

    Why:

    CURRENT
    bert, entity-extraction, language-detection, lemmatizer, llamacpp, llm, machine-translation, named-entity-recognition, natural-language-processing, nlp, onnx, part-of-speech-tagger, pyspark, question-answering, sentiment-analysis, spark, spell-checker, tensorflow, text-classification, transformers
    COPY-PASTE FIX
    bert, entity-extraction, language-detection, lemmatizer, llamacpp, llm, machine-translation, named-entity-recognition, natural-language-processing, nlp, onnx, part-of-speech-tagger, pyspark, question-answering, sentiment-analysis, spark, spell-checker, tensorflow, text-classification, transformers, distributed-computing, big-data-nlp, scalable-nlp, spark-ml

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 JohnSnowLabs/spark-nlp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NLTK
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. NLTK · recommended 3×
  2. SpaCy · recommended 3×
  3. Hugging Face Transformers · recommended 3×
  4. Apache Spark · recommended 1×
  5. Spark NLP · recommended 1×
  • CATEGORY QUERY
    Need to perform natural language processing on large datasets using distributed computing.
    you: not recommended
    AI recommended (in order):
    1. Apache Spark
    2. Spark NLP
    3. NLTK
    4. SpaCy
    5. Dask
    6. SpaCy
    7. NLTK
    8. Ray
    9. Hugging Face Transformers
    10. SpaCy
    11. Hugging Face Accelerate
    12. Hugging Face Transformers
    13. Apache Flink

    AI recommended 13 alternatives but never named JohnSnowLabs/spark-nlp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which framework provides comprehensive natural language processing capabilities with many pretrained models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. spaCy
    3. NLTK
    4. AllenNLP
    5. Stanford CoreNLP

    AI recommended 5 alternatives but never named JohnSnowLabs/spark-nlp. 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 JohnSnowLabs/spark-nlp?
    pass
    AI did not name JohnSnowLabs/spark-nlp — 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 JohnSnowLabs/spark-nlp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named JohnSnowLabs/spark-nlp 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 JohnSnowLabs/spark-nlp solve, and who is the primary audience?
    pass
    AI named JohnSnowLabs/spark-nlp explicitly

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

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JohnSnowLabs/spark-nlp — 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