RRepoGEO

REPOGEO REPORT · LITE

NVIDIA-NeMo/Curator

Default branch main · commit 1dc5479f · scanned 6/27/2026, 4:16:49 AM

GitHub: 1,634 stars · 292 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)

3 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 NVIDIA-NeMo/Curator, 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
    Strengthen README's opening sentence to emphasize GPU-acceleration and NVIDIA

    Why:

    CURRENT
    NeMo Curator helps ML engineers and data teams build repeatable, GPU-accelerated pipelines that load, filter, deduplicate, and transform large text, image, video, and audio datasets for AI training.
    COPY-PASTE FIX
    NVIDIA NeMo Curator is a GPU-accelerated toolkit for ML engineers and data teams, enabling scalable data preprocessing and curation of large text, image, video, and audio datasets for AI training, especially for LLMs.
  • mediumcomparison#2
    Add a 'Why NeMo Curator?' comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Why NeMo Curator? (vs. Spark, Dask, Hugging Face Datasets)

NeMo Curator stands apart from general-purpose data processing frameworks by offering GPU-accelerated, end-to-end pipelines specifically designed for large-scale AI training data across text, image, video, and audio modalities. Unlike CPU-bound tools, Curator leverages NVIDIA GPUs to deliver superior performance for tasks like deduplication, quality filtering, and transformation, making it ideal for LLM and multi-modal model development.
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the official project or product page URL (e.g., https://developer.nvidia.com/nemo-curator).

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 NVIDIA-NeMo/Curator
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/datasets
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/datasets · recommended 2×
  2. apache/spark · recommended 1×
  3. dask/dask · recommended 1×
  4. DataBricks · recommended 1×
  5. ray-project/ray · recommended 1×
  • CATEGORY QUERY
    How can I preprocess and curate very large text datasets for training LLMs efficiently?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark (apache/spark)
    2. Dask (dask/dask)
    3. Hugging Face Datasets library (huggingface/datasets)
    4. DataBricks
    5. Ray (ray-project/ray)
    6. cuDF (rapidsai/cudf)
    7. ClickHouse (ClickHouse/ClickHouse)

    AI recommended 7 alternatives but never named NVIDIA-NeMo/Curator. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for scalable deduplication and quality filtering of AI training data across modalities?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Delta Lake
    3. MLflow (mlflow/mlflow)
    4. Apache Spark
    5. Spark NLP (JohnSnowLabs/spark-nlp)
    6. Spark MLlib
    7. Hugging Face Datasets Library (huggingface/datasets)
    8. 🤗 Transformers (huggingface/transformers)
    9. Google Cloud Dataflow
    10. Apache Beam (apache/beam)
    11. AWS Glue
    12. Faiss (facebookresearch/faiss)
    13. Annoy (spotify/annoy)
    14. DVC (Data Version Control) (iterative/dvc)
    15. Pachyderm (pachyderm/pachyderm)

    AI recommended 15 alternatives but never named NVIDIA-NeMo/Curator. 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 NVIDIA-NeMo/Curator?
    pass
    AI named NVIDIA-NeMo/Curator explicitly

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

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

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

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NVIDIA-NeMo/Curator — 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