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EurecaMoment
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data-juicer-card

Clean and preprocess datasets using Data-Juicer tools.

Updated May 7, 2026
Search Tags:#data quality#data cleaning#text normalization#dataset preprocessing#Data-Juicer
One-click install
npx skills add https://github.com/EurecaMoment/BenchClaw --skill data-juicer-card
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-juicer-card
Source: https://github.com/EurecaMoment/BenchClaw/tree/main/BenchClaw/data-juicer_card
Command: npx skills add https://github.com/EurecaMoment/BenchClaw --skill data-juicer-card

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires data_juicer, and includes scripts (resource) components.

What problem does it solve?

This Skill simplifies the process of dataset cleaning, filtering, normalization, deduplication, and quality control, enabling reproducible data processing with Data-Juicer.

Core Features & Use Cases

  • Dataset Cleaning: Handle tasks like HTML tag removal, URL filtering, and text length adjustment.
  • Data Filtering: Filter text based on character length and word count.
  • Use Case: When preparing a dataset for analysis or benchmarking, this Skill can help ensure the data is clean and standardized.

Quick Start

Clean the dataset at the path '/path/to/input.jsonl' and save the results to '/path/to/output/cleaned.jsonl'.

Frequently Asked Questions about data-juicer-card

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I clean and preprocess a dataset for benchmarking?▼

Dataset cleaning handles HTML tag removal, URL filtering, and text length adjustment to ensure your data is clean and standardized for analysis or benchmarking tasks.

What is the best way to normalize text and remove duplicates in JSONL datasets?▼

Text normalization and deduplication streamline dataset preparation by processing JSONL datasets to filter text based on character length and word count, ensuring reproducible data quality control.

Do I need a specific conda environment to run Data-Juicer for data quality control?▼

Yes, executing Data-Juicer tools for dataset preprocessing and data quality control requires setting up the 'data_juicer' conda environment beforehand.

How do I filter text based on character length and word count during dataset preprocessing?▼

You filter text by character length and word count by applying data filtering operations that process input JSONL files and save the cleaned results to a specified output path.

Does dataset preprocessing with Data-Juicer support reproducible data cleaning workflows?▼

Yes, dataset preprocessing with Data-Juicer simplifies data cleaning, filtering, and deduplication, explicitly enabling reproducible data processing workflows for dataset preparation.

What are the limitations of using Data-Juicer for dataset cleaning?▼

Data-Juicer focuses on text normalization, deduplication, and filtering tasks like HTML tag removal and URL filtering, tailored specifically for dataset preparation rather than general data analysis.

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