Bert-base-german-cased finetuned on the Valence level of the GLoHBCD Dataset (https://github.com/SelinaMeyer/GLoHBCD). The dataset leverages Motivational Interviewing client behaviour codes to evaluate user utterances across different dimensions and gauge user's stance and thoughts about behaviour change in the context of weight loss.
This model classifies German text around behaviour change as either "General Reason" (utterances about general reasons for or against change, 0), "ability" (utterances about the writer's perceived ability to change, 1), "desire" (utterances about desires for or against change, 2), or "need" (utterances about the need to change or not change, 3).
When using the model, please cite:
@InProceedings{meyer-elsweiler:2022:LREC,
author = {Meyer, Selina and Elsweiler, David},
title = {GLoHBCD: A Naturalistic German Dataset for Language of Health Behaviour Change on Online Support Forums},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {2226--2235},
url = {https://aclanthology.org/2022.lrec-1.239}}
