| 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 "Change Talk" (utterances in favour of change, 1) or "Sustain Talk" (utterances in favour of the status quo, 0). | |
| 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}} |