TextDescriptives: A Python package for calculating a large variety of metrics from text

Lasse Hansen, Ludvig Renbo Olsen, Kenneth Enevoldsen
1/5/2023
cs.CL

No official code found. Best implementation: HLasse/TextDescriptives (367 stars, Apache-2.0, last commit 2026-03-20).

Abstract

TextDescriptives is a Python package for calculating a large variety of metrics from text. It is built on top of spaCy and can be easily integrated into existing workflows. The package has already been used for analysing the linguistic stability of clinical texts, creating features for predicting neuropsychiatric conditions, and analysing linguistic goals of primary school students. This paper describes the package and its features.

View on arXivView PDF

Code Implementations(1)

HLasse/TextDescriptivesCommunity100%
Apache-2.0

A Python library for calculating a large variety of metrics from text

36726TeX, PythonJan 28, 20207 months agoApache-2.0
dependency-distancedescriptive-statisticsnlppythonreadability+5 more

Similar Papers

A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics

80% match2 repos2252018

The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian Language

79% match2023

VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models

79% match1 repo312023

MuLER: Detailed and Scalable Reference-based Evaluation

79% match2023

QualEval: Qualitative Evaluation for Model Improvement

78% match2023

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

78% match1 repo802023

Cite this paper

@article{hansen2023textdescriptives,
  title  = {TextDescriptives: A Python package for calculating a large variety of metrics from text},
  author = {Lasse Hansen and Ludvig Renbo Olsen and Kenneth Enevoldsen},
  year   = {2023},
  eprint = {2301.02057},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.02057v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2301.02057.svg)](https://codeofpaper.com/paper/2301.02057)

Discussion