This is a SpanMarker model trained on the Acronym Identification dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder. See train.py for the training script. Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: tomaarsen/span-marker-bert-base-acronyms. You can finetune this model on your own dataset. - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 2 Carbon emissions were measured using CodeCarbon.