Practical overview
When it fits
Use it when you need to prototype or add text categorization in Python and can provide category examples or labels alongside the model approach you want to use.
Who it is for
- Python developers adding text categorization to an application or NLP workflow.
- Teams that want to prototype custom categories using examples instead of training a classifier from scratch.
Documented uses
- Assign text to custom categories using a dictionary of labels and example texts.
- Try zero-shot classification by supplying category labels to a Hugging Face classifier.
- Classify at sentence level or allow multiple category scores for one text.
- Use a single-label OneClassSVM option for outlier detection.
Requirements & limits
The repository specifies an MIT license.
- Few-shot use requires examples grouped by category; zero-shot use takes a list of labels and relies on Hugging Face zero-shot classifiers.
- The README shows spaCy pipeline integration and standalone use, so integration style can guide the choice.
- Multi-label mode permits category scores that do not sum to one.
- The documented OneClassSVM outlier-detection option returns label-score pairs, not probabilities.
- SetFit support is handled by a separate package, according to the README.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up spaCy classification with labeled examples
Add the Classy Classification component to a spaCy pipeline and retrieve category scores for a text.
Before you start
- Python and the classy-classification package, installed with pip.
- spaCy and the en_core_web_trf model used in the README example.
- A dictionary mapping category labels to example texts.
Steps
Install the package with pip install classy-classification.
Load spaCy and the en_core_web_trf model.
Add the classy_classification component with the example dictionary as data and model set to spacy.
Pass text to the pipeline and read the categories from the document's _.cats attribute.
Expected outcome
The pipeline returns category scores for the input text.
Guide limitations
- The README example uses a specific spaCy model and example category data; it does not establish performance for other datasets.
- This setup guide does not cover deployment or production operations.
Set up standalone few-shot classification
Create a standalone classifier from labeled examples and submit text for classification.
Before you start
- Python and the classy-classification package.
- A dictionary mapping category labels to example texts.
Steps
Install the package with pip install classy-classification.
Import ClassyClassifier from classy_classification and provide the labeled examples as data.
Call the classifier with a text string or use its pipe method with a list of texts.
Expected outcome
The classifier returns a classification result for the submitted text or texts.
Guide limitations
- The README documents this interface but does not specify resource requirements or deployment steps.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- davidberenstein1957/classy-classification · Official homepage title · retained in its source language