Fig. 2: NLP model and predictive performance.
From: Text-based predictions of COVID-19 diagnosis from self-reported chemosensory descriptions
A. The DistilBERT model used for text analysis. Input text responses were first converted into tokens by the tokenizer. Then the relationship and interactions among tokens were learned by the transformer encoder. The final output is a single value between 0 and 1 in this binary classification task. B. The AUC-ROCs of tenfold cross-validations experiments are shown as boxplots for option 5 class and option 6 class predictions. Horizontal lines represent medians and the mean values are labeled. The whiskers represent the maximum and minimum values, whereas the bottom and top of boxes represent the first (25%) and third (75%) quartile.
