Text Classification
Transformers
Safetensors
German
modernbert
political-text-analysis
stance-detection
text-embeddings-inference
Instructions to use schlenker/moderngbert-parl-german-stance-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schlenker/moderngbert-parl-german-stance-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="schlenker/moderngbert-parl-german-stance-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("schlenker/moderngbert-parl-german-stance-detection") model = AutoModelForSequenceClassification.from_pretrained("schlenker/moderngbert-parl-german-stance-detection") - Notebooks
- Google Colab
- Kaggle
Detection of Group Appeals
This model serves as a baseline of the LREC 2026 submission Appeal, Align, Divide? Stance Detection for Group-Directed Messages in German Parliamentary Debates.
moderngbert-parl-german-stance-detection is trained to detect group appeals in a given text sequence, i.e., to assign a stance (favour, against, neither) to a previously identified social group. Refer to our paper for further details on model training and evaluation.
π Model Overview
- Task Type: Single-label, multi-class sequence classification
- Base Model: ModernGBERT 134M
- Fine-tuning method: full fine-tuning
- Language: German
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Model tree for schlenker/moderngbert-parl-german-stance-detection
Base model
LSX-UniWue/ModernGBERT_134M