We observed that generally the embedding representation is very rich and information dense. For example, reducing the dimensionality of the inputs using SVD or PCA, even by 10%, generally results in worse downstream performance on specific tasks.
Guided by the risk information-seeking and processing model, this study examines positive and negative affect separately in their influence on information-seeking intentions and avoidance through structural equation analyses. The highlight is that information avoidance seems to be driven by positive affect, while information seeking seems to be more heavily influenced by negative affect. Another interesting finding is that informational subjective norms are positively related to both seeking and avoidance, which suggests that one’s social environment has the potential to strongly influence the way he or she handles climate change information. Implications for theory and practice are discussed.
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome - GitHub - jerryji1993/DNABERT: DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
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