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Generating 3-grams for all namespaces.
Generating 1-skips for all namespaces.
Num weight bits = 18
learning rate = 2.56e+06
initial_t = 128000
power_t = 1
decay_learning_rate = 1
final_regressor = models/0001.model
creating cache_file = train-sets/0001.dat.cache
Reading datafile = train-sets/0001.dat
num sources = 1
average since example example current current current
loss last counter weight label predict features
1.000000 1.000000 1 1.0 1.0000 0.0000 290
1.000000 1.000000 2 2.0 0.0000 1.0000 608
0.500351 0.000703 4 4.0 0.0000 0.0000 794
0.399940 0.299528 8 8.0 0.0000 0.0000 860
0.415501 0.431061 16 16.0 1.0000 0.9107 128
0.453621 0.491742 32 32.0 0.0000 0.5372 176
0.451956 0.450291 64 64.0 0.0000 0.0000 350
0.428071 0.404187 128 128.0 1.0000 1.0000 620
0.311152 0.194233 256 256.0 0.0000 0.0000 410
0.187697 0.064242 512 512.0 0.0000 0.0000 278
0.093848 0.000000 1024 1024.0 1.0000 1.0000 170
finished run
number of examples per pass = 200
passes used = 8
weighted example sum = 1600
weighted label sum = 728
average loss = 0.060063
best constant = 1.0069
total feature number = 717536
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