language: de
widget:
- 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.
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)
🗃 Dataset
| party | speeches | tokens |
|---|---|---|
| CDU/CSU | 7,635 | 4,862,654 |
| SPD | 5,321 | 3,158,315 |
| AfD | 3,465 | 1,844,707 |
| FDP | 3,067 | 1,593,108 |
| The Greens | 2,866 | 1,522,305 |
| The Left | 2,671 | 1,394,089 |
| cross-bencher | 200 | 86,170 |
🏃🏼♂️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%):
| Label | F1 | support |
|---|---|---|
| International | 80.0 | 1,126 |
| Defense | 85.0 | 1,099 |
| Government | 71.3 | 989 |
| Civil Rights | 76.5 | 978 |
| Environment | 76.6 | 845 |
| Transportation | 86.0 | 800 |
| Law & Crime | 67.1 | 492 |
| Energy | 78.6 | 424 |
| Health | 78.2 | 418 |
| Domestic Com. | 64.4 | 382 |
| Immigration | 81.0 | 376 |
| Labor | 69.1 | 344 |
| Macroeconom. | 62.8 | 339 |
| Agriculture | 76.3 | 292 |
| Social Welfare | 49.2 | 253 |
| Technology | 63.0 | 252 |
| Education | 71.6 | 183 |
| Housing | 79.6 | 178 |
| Foreign Trade | 61.5 | 139 |
| Culture | 54.6 | 69 |
| Public Lands | 45.4 | 55 |
⚠️ 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