A Python library for calculating a large variety of metrics from text(s) using spaCy v.3 pipeline components and extensions.

pip install textdescriptives

• We now have a TextDescriptives-powered web-app so you can extract and downloads metrics without a single line of code! Check it out here
• Version 2.0 out with a new API, a new component, updated documentation, and tutorials! Components are now called by "textdescriptives/{metric_name}. New coherence component for calculating the semantic coherence between sentences. See the documentation for tutorials and more information!

Use extract_metrics to quickly extract your desired metrics. To see available methods you can simply run:

import textdescriptives as td
td.get_valid_metrics()
# {'quality', 'readability', 'all', 'descriptive_stats', 'dependency_distance', 'pos_proportions', 'information_theory', 'coherence'}

Set the spacy_model parameter to specify which spaCy model to use, otherwise, TextDescriptives will auto-download an appropriate one based on lang. If lang is set, spacy_model is not necessary and vice versa.

Specify which metrics to extract in the metrics argument. None extracts all metrics.

import textdescriptives as td

text = "The world is changed. I feel it in the water. I feel it in the earth. I smell it in the air. Much that once was is lost, for none now live who remember it."
# will automatically download the relevant model (´en_core_web_lg´) and extract all metrics
df = td.extract_metrics(text=text, lang="en", metrics=None)

# specify spaCy model and which metrics to extract
df = td.extract_metrics(text=text, spacy_model="en_core_web_lg", metrics=["readability", "coherence"])

To integrate with other spaCy pipelines, import the library and add the component(s) to your pipeline using the standard spaCy syntax. Available components are descriptive_stats, readability, dependency_distance, pos_proportions, coherence, and quality prefixed with textdescriptives/.

If you want to add all components you can use the shorthand textdescriptives/all.

import spacy
import textdescriptives as td
# load your favourite spacy model (remember to install it first using e.g. `python -m spacy download en_core_web_sm`)
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("textdescriptives/all") 
doc = nlp("The world is changed. I feel it in the water. I feel it in the earth. I smell it in the air. Much that once was is lost, for none now live who remember it.")

# access some of the values
doc._.readability
doc._.token_length

TextDescriptives includes convenience functions for extracting metrics from a Doc to a Pandas DataFrame or a dictionary.

td.extract_dict(doc)
td.extract_df(doc)

TextDescriptives has a detailed documentation as well as a series of Jupyter notebook tutorials. All the tutorials are located in the docs/tutorials folder and can also be found on the documentation website.