Instructions to use PH-Weingarten/AISOP-oop-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use PH-Weingarten/AISOP-oop-classifier with spaCy:
!pip install https://huggingface.co/PH-Weingarten/AISOP-oop-classifier/resolve/main/AISOP-oop-classifier-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("AISOP-oop-classifier") # Importing as module. import AISOP-oop-classifier nlp = AISOP-oop-classifier.load() - Notebooks
- Google Colab
- Kaggle
Commit History
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Verified signature# AISOP-fundid-classifiers This is a series of spacy models for the classification tasks with topics/labels in the concept map of the course "Objekt-orientierte Programmierung". ## Try it here - Install spaCy - `python python recognize.py l1-model l2-models "this is a text"` ## Web-App Packaging This model is part of the [AISOP-domain-OOP Domain](https://gitlab.com/aisop/aisop-domain-oop) which is designed to serve for the [AISOP-webapp](https://gitlab.com/aisop/aisop-webapp). The [python scripts](https://gitlab.com/aisop/aisop-webapp/-/tree/main/scripts/python?ref_type=heads) there use the models and the spaCy library to classify each "paragraph" of e-portfolios stored in HTML and even generate words using tesseract (if the picture is available) and annotate them too. The scripts enrich the HTML with `data-topic-*` attributes, indicating the presence of topics in the paragraphs. The scripts can be tested in the web-app in the `/debug/` road.6a82240 -
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