Given a text, its output format is: "{ENT_TYPE}:{span}; {ENT_TYPE}:{span}..."
For training speed, we only use the first 10,000 sentences (not documents) from train set; 1,000 sentences from validation set;
we save the model when its val_loss (NLL) reaches the minimum.
The model could be used as a pretrained backbone on downstream fine-tuning NER tasks.
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