Ausklasser is a text classification model designed to identify apprenticeship job advertisements (AOJAs) from regular job advertisements (ROJAs) in the German language. The model is built on the distilBERT architecture, offering an efficient and compact solution for processing German Online Job Advertisements (OJAs).
Developed by Kai Krüger at the German Federal Institute for Vocational Education and Training, this model is intended for researchers and professionals involved in labor market analysis, specifically for distinguishing between apprenticeship and regular job listings in German.
Training, data and experiments are described in
the corresponding publicationAusklasser achieved high accuracy and generalization capabilities in both training and testing. Specifically, it demonstrated an accuracy of 0.98 on the test set and 0.9 in training evaluation.
The model is available on Hugging Face and can be utilized for classifying German OJAs into four categories:
1# Example Python code for using the Ausklasser model
2from transformers import AutoTokenizer, AutoModelForSequenceClassification
3
4tokenizer = AutoTokenizer.from_pretrained("KKrueger/ausklasser")
5model = AutoModelForSequenceClassification.from_pretrained("KKrueger/ausklasser")
6
7# Example text
8text = "Your German job advertisement text here"
9
10# Tokenize and predict
11inputs = tokenizer(text, return_tensors="pt")
12outputs = model(**inputs)
13
14# Process outputs (for example, convert to labels)
15#
