Domain adaptation is the process of fine-tuning pre-trained language models (PLMs) on domain-specific datasets to produce predictions that are better suited to the new datasets. Here, we re-train the BERT-base-uncased model on an unlabelled COVID-19 fake news dataset (Constraint@AAAI2021) using the masked language modeling (MLM) objective, where 15% of input text is masked, and the model is expected to predict the masked tokens.
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transformers pytorch bert fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

Domain adaptation is the process of fine-tuning pre-trained language models (PLMs) on domain-specific datasets to produce predictions that are better suited to the new datasets. Here, we re-train the BERT-base-uncased model on an unlabelled COVID-19 fake news dataset (Constraint@AAAI2021) using the masked language modeling (MLM) objective, where 15% of input text is masked, and the model is expected to predict the masked tokens.
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
