RRepoGEO

REPOGEO REPORT · LITE

huggingface/datatrove

Default branch main · commit a035d36e · scanned 5/23/2026, 6:11:54 AM

GitHub: 3,065 stars · 264 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
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 huggingface/datatrove, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening paragraph to emphasize LLM training data

    Why:

    CURRENT
    DataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality. DataTrove processing pipelines are platform-agnostic, running out of the box locally or on a slurm cluster. Its (relatively) low memory usage and multiple step design makes it ideal for large workloads, such as to process an LLM's training data.
    COPY-PASTE FIX
    DataTrove is a specialized library for processing, filtering, and deduplicating *massive text datasets specifically for training large language models (LLMs)*. It provides a set of prebuilt, platform-agnostic processing blocks and a framework to easily add custom functionality, designed for large workloads and low memory usage, making it ideal for LLM training data pipelines.
  • mediumhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://github.com/huggingface/datatrove

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 huggingface/datatrove
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
apache/spark
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. apache/spark · recommended 1×
  2. dask/dask · recommended 1×
  3. ray-project/ray · recommended 1×
  4. huggingface/datasets · recommended 1×
  5. pola-rs/polars · recommended 1×
  • CATEGORY QUERY
    How can I efficiently process, filter, and deduplicate very large text datasets for AI training?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark (apache/spark)
    2. Dask (dask/dask)
    3. Ray (ray-project/ray)
    4. Hugging Face Datasets Library (huggingface/datasets)
    5. Polars (pola-rs/polars)
    6. Faiss (facebookresearch/faiss)
    7. DataFusion (apache/arrow-datafusion)

    AI recommended 7 alternatives but never named huggingface/datatrove. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a framework to build platform-agnostic data processing pipelines for massive text workloads?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark
    2. Apache Flink
    3. Dask
    4. Ray
    5. Apache Beam

    AI recommended 5 alternatives but never named huggingface/datatrove. 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 huggingface/datatrove?
    pass
    AI named huggingface/datatrove explicitly

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

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

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

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huggingface/datatrove — 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