REPOGEO REPORT · LITE
JohnSnowLabs/spark-nlp
Default branch master · commit cca0c97c · scanned 5/20/2026, 5:47:16 AM
GitHub: 4,129 stars · 744 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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.
- highreadme#1Strengthen README opening for production-grade, scalable NLP on Spark
Why:
CURRENTSpark 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 FIXSpark NLP is the leading **production-grade**, 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 effortlessly** in **big data** and distributed environments, making it ideal for enterprise applications.
- mediumcomparison#2Add a dedicated comparison section to the README
Why:
COPY-PASTE FIX## Why Spark NLP? (Comparison to Alternatives) Spark NLP stands out from other NLP libraries by offering production-grade, scalable NLP capabilities natively on Apache Spark. Unlike single-machine libraries such as spaCy, NLTK, or Hugging Face Transformers (when used in single-node mode), Spark NLP is designed from the ground up for distributed processing of large datasets, integrating seamlessly into Spark ML pipelines. This makes it the ideal choice for big data NLP, enterprise applications, and environments requiring high throughput and fault tolerance.
- lowtopics#3Add more specific topics related to distributed and production NLP
Why:
CURRENTbert, 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 FIXbert, 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-nlp, big-data-nlp, production-nlp, spark-ml-pipelines, enterprise-nlp
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.
- Spark NLP · recommended 2×
- Hugging Face Transformers · recommended 2×
- NLTK · recommended 2×
- Apache OpenNLP · recommended 1×
- Stanford CoreNLP · recommended 1×
- CATEGORY QUERYSeeking a scalable NLP library for integrating text processing into Spark ML pipelines.you: not recommendedAI recommended (in order):
- Spark NLP
- Hugging Face Transformers
- Apache OpenNLP
- Stanford CoreNLP
- NLTK
AI recommended 5 alternatives but never named JohnSnowLabs/spark-nlp. This is the gap to close.
Show full AI answer
- CATEGORY QUERYHow can I integrate advanced natural language processing and LLM capabilities into PySpark applications?you: not recommendedAI recommended (in order):
- Spark NLP
- Hugging Face Transformers
- Databricks MLflow
- Mosaic AI
- LangChain
- spaCy
- NLTK
- Ray
AI recommended 8 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 completenesspass
- README presencepass
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?passAI 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?passAI 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?passAI named JohnSnowLabs/spark-nlp 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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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