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text_splitter

Split text into paragraphs, sentences, or custom-length segments using Python standard libraries.

541|171|Updated May 3, 2018
Search Tags:#text processing#document analysis#text segmentation#data chunking#segmentation mode
One-click install
npx skills add https://github.com/cas-bigdatalab/piflow --skill text-splitter-cas-bigdatalab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: text_splitter
Source: https://github.com/cas-bigdatalab/piflow/tree/main/workspace/skills/text_splitter
Command: npx skills add https://github.com/cas-bigdatalab/piflow --skill text-splitter-cas-bigdatalab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines the process of segmenting long texts into manageable chunks, saving time and effort for users dealing with large volumes of textual data.

Core Features & Use Cases

  • Text Segmentation: Automatically split texts into paragraphs, sentences, or custom-sized segments.
  • Flexible Modes: Supports various segmentation modes including paragraph, sentence, fixed character length, non-empty lines, and section titles.
  • Use Case: Ideal for researchers or analysts who need to process large documents like scientific papers or reports, where segmenting the text into manageable parts can aid in analysis and review.

Quick Start

Use the text_splitter skill to segment the text in 'long_document.txt' into paragraphs and output the result to 'output_chunks.jsonl'.

Frequently Asked Questions about text_splitter

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

FAQPage Schema
How do I split a long document into smaller chunks for analysis?▼

Text segmentation automates splitting long documents into manageable chunks for efficient analysis. You can segment texts into paragraphs, sentences, or custom-sized segments to streamline processing large volumes of textual data.

What are the available modes for text segmentation?▼

Available text segmentation modes include paragraph, sentence, fixed character length, non-empty lines, and section titles. These flexible modes allow parsing and splitting text based on specified criteria.

Can I segment scientific papers and large reports by section titles?▼

Yes, text segmentation is ideal for researchers or analysts processing large documents like scientific papers or reports. You can split these texts using section titles or other modes to aid analysis and review.

How do I output segmented text chunks to a JSONL file?▼

You can segment text in a source document and output the result to a JSONL file. The text_splitter script utilizes Python standard libraries to parse and split text based on your specified criteria.

Do I need external dependencies to perform custom character length segmentation?▼

No external dependencies are required. The text segmentation script relies entirely on Python standard libraries to parse and split text based on specified criteria like custom character length.

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