wtpsplit - The precise and adaptable text segmentation tool covering 85 languages

wtpsplit🪓: Robust, Efficient Text Segmentation

What is wtpsplit?

wtpsplit is an advanced tool designed for segmenting text into sentences or other meaningful units. At its core, the tool utilizes models to provide state-of-the-art sentence segmentation capabilities across a wide array of languages, making it versatile for use in multiple linguistic contexts.

The Models Behind wtpsplit

SaT (Segment Any Text): This model is the latest and most advanced, offering robust, efficient, and adaptable text segmentation across 85 languages. It has demonstrated high performance in tests across various corpora, achieving notable results while reducing computational requirements.
WtP (Where’s the Point?): The predecessor to SaT, this model emphasizes self-supervised multilingual segmentation, allowing for punctuation-agnostic sentence segmentation. Although not the latest, it remains supported for reproducibility purposes.

Key Features of wtpsplit

Multilingual Support: With effective segmentation capabilities in 85 languages, wtpsplit ensures accurate sentence identification regardless of language.
Efficiency and Adaptability: Built to provide high performance with less computational demand, minimizing resource use while maintaining accuracy.
Customizability: Use of adaptive modules (LoRA) allows tailored segmentation to fit specific language domains or styles, enhancing flexibility.

How to Use wtpsplit

To get started with wtpsplit, install the package via pip:

pip install wtpsplit

Basic Usage

Using Python, import and set up wtpsplit as follows:

from wtpsplit import SaT

sat = SaT("sat-3l")
sat.half().to("cuda")  # Optimize for GPU

To segment text:

result = sat.split("This is a test This is another test.")
# Output: ["This is a test ", "This is another test."]

For bulk text segmentation:

results = sat.split(["This is a test This is another test.", "And some more texts..."])
# Yields segmented lists for each text input

Advanced Functionality

ONNX Support for Speed: Enables even faster processing, ideal for applications needing rapid analysis.
Paragraph Segmentation: Beyond sentences, SaT can also segment texts into paragraphs, offering greater organizational options.
Domain Adaptation: Leverage LoRA modules to tailor segmentation models to specific document types, languages, or styles, such as legal texts, tweets, or even code-switching scenarios.

Choosing the Right Model

Depending on the use case, different models cater to various needs:

For general tasks, sat-3l-sm provides quick and efficient results.
For tasks needing high accuracy, 12-layer models like sat-12l-sm deliver the best performance.

Language Coverage

wtpsplit supports a broad range of languages, from widely used ones like English and Spanish to less common languages such as Xhosa and Yoruba. This extensive list ensures that users across different regions can utilize the tool effectively.

Final Thoughts

Whether you're looking to efficiently segment large text data sets or need precise sentence segmentation across multiple languages, wtpsplit offers a powerful solution with flexible features to suit an array of needs. From AI training and content analysis to language processing and beyond, wtpsplit stands as a reliable tool in natural language processing applications.

For extensive details on adapting models or using advanced endpoints, users can explore the technical facets of the tool further, benefiting from its comprehensive support for evolving linguistic projects.