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final_regressor = models/sequencespan_data.model
switching to BILOU encoding for sequence span labeling
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/sequencespan_data.cache
Reading datafile = train-sets/sequencespan_data
num sources = 1
average since instance current true current predicted cur cur predic cache examples
loss last counter output prefix output prefix pass pol made hits gener beta
6.000000 6.000000 1 [2 1 1 2 2 1 6 7 7 ..] [1 1 1 1 1 1 1 1 1 ..] 0 0 15 0 15 0.000000
5.000000 4.000000 2 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 6 4 1 6 7 7 ..] 1 0 30 0 30 0.000000
2.500000 0.000000 4 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 3 0 60 0 60 0.000001
1.250000 0.000000 8 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 7 0 120 0 120 0.000001
0.625000 0.000000 16 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 15 0 240 0 240 0.000002
finished run
number of examples per pass = 1
passes used = 20
weighted example sum = 20
weighted label sum = 0
average loss = 0.5
total feature number = 900
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