license: mit
language:
- en
base_model:
- MoritzLaurer/deberta-v3-large-zeroshot-v2.0
- mlburnham/deberta-v3-large-polistance-affect-v1.1
pipeline_tag: zero-shot-classification
library_name: transformers
tags:
- politics
- text-classification
Model Card for groupappeals_classifier_positive
This model classifies the valence of rhetorical appeals by politicians to groups ("group appeals") in political speech.
Model Details
Model Description
This model adapts Mike Burnham's zero shot model for political stance detection, which is itself an adaptation of Moritz Laurer's zero shot model for classifying political texts. It is trained for the more specific use of classifying the valence of rhetorical appeals by politicians to groups ("group appeals") in political speech. The model takes in sentences that are formatted so as to mention the sender/speaker and the group mentioned (i.e. the 'dyad') of the form: "Politician from {party} mentioning a group ({group}): '{text}'". It returns the probability that the speaker is making a positive appeal to the group.
- Developed by: Christoffer H. Dausgaard & Frederik Hjorth
- Model type: Fine-tuned DeBERTa-model
- License: mit
- Finetuned from model: deberta-v3-base-polistance-affect-v1.0
- Paper [optional]: {{ paper | default("[More Information Needed]", true)}}
Uses
How to Get Started with the Model
Training Details
Training Data
The model was trained using a subset of the ParlSpeech v2 dataset that covers the universe of parliamentary speeches in the UK House of Commons from 1988-2019. The subset consists of 2,534 sentences manually coded by the authors. The sentences were randomly sampled within party- and group-strata, with oversampling of negative sentences.
Training Procedure
Preprocessing [optional]
Training Hyperparameters
- Training regime: {{ training_regime | default("[More Information Needed]", true)}}
Evaluation
Citation [optional]
BibTeX: