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README.md
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Gbert finetuned on on-topic vs. off-topic sentences 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.
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This model classifies German text around behaviour change as either "Follow/Neutral" (not related to behaviour change, 0) or "Change Related" (related to behaviour change, 1) and can be used as a prefilter to spot sentences which can be used to infer information for a user's thoughts about behaviour change (see selmey/behaviour-change-valence-german; selmey/behaviour-change-labels-german; and selmey/behaviour-change-sublabels-german)
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**The model should be used in combination with the "deepset/gbert-base" tokenizer.**
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Gbert finetuned on on-topic vs. off-topic sentences 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.
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This model classifies German text around behaviour change as either "Follow/Neutral" (not related to behaviour change, 0) or "Change Related" (related to behaviour change, 1) and can be used as a prefilter to spot sentences which can be used to infer information for a user's thoughts about behaviour change (see selmey/behaviour-change-valence-german; selmey/behaviour-change-labels-german; and selmey/behaviour-change-sublabels-german)
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When applied to the GLoHBCD, it reaches a macro F1 score of 72.67% on the test set.
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**The model should be used in combination with the "deepset/gbert-base" tokenizer.**
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