Feb 24, 2022 · Conclusions: Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is ...
Feb 8, 2024 · Transformer-based models outperform traditional techniques in classifying eating disorder-related tweets, though they require more computational resources.
Feb 24, 2022 · We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain.
Dec 3, 2021 · Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for ...
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating ...
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating ...
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating ...
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Sep 24, 2024 · ... Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study ... model ...
... Representations From Transformer (BERT)–Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study. 10 ...
This narrative review explores current Machine Learning (ML) and Artificial Intelligence (AI) applications in the domain of EDs, with a specific emphasis on ...