chkla/parlbert-topic-german

Welcome to ParlBERT-Topic-German!

🏷 Model description

This model was trained on \~10k manually annotated interpellations (📚 Breunig/ Schnatterer 2019) with topics from the Comparative Agendas Project to classify text into one of twenty labels (annotation codebook).

Note: "Interpellation is a formal request of a parliament to the respective government."([Wikipedia](https://en.wikipedia.org/wiki/Interpellation(politics)))_

🗃 Dataset

🏃🏼‍♂️Model training

ParlBERT-Topic-German was fine-tuned on a domain adapted model (GermanBERT fine-tuned on DeuParl) for topic modeling with an interpellations dataset (📚 Breunig/ Schnatterer 2019) from the Comparative Agendas Project.

🤖 Use

from transformers import pipeline

pipeline_classification_topics = pipeline("text-classification", model="chkla/parlbert-topic-german", return_all_scores=False)
text = "Das Sachgebiet Investive Ausgaben des Bundes Bundesfinanzminister Apel hat gemäß BMF Finanznachrichten vom 1. Januar erklärt, die Investitionsquote des Bundes sei in den letzten zehn Jahren nahezu konstant geblieben."
pipeline_classification_topics(text) # Macroeconomics

📊 Evaluation

The model was evaluated on an evaluation set (20%):

⚠️ Limitations

Models are often highly topic dependent. Therefore, the model may perform less well on different topics and text types not included in the training set.

👥 Cite

@article{klamm2022frameast,
  title={FrameASt: A Framework for Second-level Agenda Setting in Parliamentary Debates through the Lense of Comparative Agenda Topics},
  author={Klamm, Christopher and Rehbein, Ines and Ponzetto, Simone},
  journal={ParlaCLARIN III at LREC2022},
  year={2022}
}

🐦 Twitter: @chklamm