only testing
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = sequencespan_data.predict
warning: setting searn_beam=1 is kind of a weird thing to do -- just don't use a beam at all
switching to BILOU encoding for sequence span labeling
using no cache
Reading datafile = train-sets/sequencespan_data
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
average    since      sequence         example            current label      current predicted  current   cur   cur         predic.        examples
loss       last        counter          weight          sequence prefix        sequence prefix features  pass   pol            made          gener.
0.000000   0.000000          1        1.000000   [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..]        0     0     0              79               0

finished run
number of examples per pass = 1
passes used = 1
weighted example sum = 1
weighted label sum = 0
average loss = 0
best constant = -inf
total feature number = 0