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This paper describes our submission on the COVID-19 literature annotationtask at Biocreative VII. We proposed an approach that exploits the knowledge ofthe globally non-optimal weights, usually rejected, to build a richrepresentation of each label. Our proposed approach consists of two stages: (1)A bagging of various initializations of the training data that features weaklytrained weights, (2) A stacking of heterogeneous vocabulary models based onBERT and RoBERTa Embeddings. The aggregation of these weak insights performsbetter than a classical globally efficient model. The purpose is thedistillation of the richness of knowledge to a simpler and lighter model. Oursystem obtains an Instance-based F1 of 92.96 and a Label-based micro-F1 of91.35.

BagBERT: BERT-based bagging-stacking for multi-topic classification