Hi, I'm working at Sequnce Labaling model for NLP task (dlpy.applications.SequenceLabeling) and while I was trying to fit the data, I came across an error "ERROR: A floating-point overflow exception occurred, halting the analysis. This condition is usually caused by improperly scaled inputs, a large learning rate, or exploding gradients.". This is my code: model.fit(data='train_data', inputs=col_names[:10], texts=col_names[:10], target_sequence=col_names[10:], nominals=col_names[10:], text_parms=TextParms(init_input_embeddings='word_embeddings_100'), mini_batch_size=10, max_epochs=100, lr=0.000000000000001, log_level=2 ) Input consists of ten columns, there is one word in each column in each row, also the labels(varchar type) are represented in the same way. I tried to run it with diffrent learning rate, but every time I get the same error, any ideas how to fix that?
... View more