Generating 2-grams for all namespaces.
creating quadratic features for pairs: :: 
WARNING: duplicate namespace interactions were found. Removed: 4278.
You can use --leave_duplicate_interactions to disable this behaviour.
final_regressor = hash_seed5.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/rcv1_mini.dat.cache
Reading datafile = train-sets/rcv1_mini.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    32385
0.747407 0.494815            2            2.0  -1.0000  -0.2966     3741
0.772949 0.798490            4            4.0  -1.0000  -1.0000    71631
1.622789 2.472629            8            8.0   1.0000  -0.1378     2211
1.372107 1.121425           16           16.0   1.0000   0.7096     3570
1.483492 1.594877           32           32.0  -1.0000  -0.0765     4278
1.323154 1.162815           64           64.0   1.0000   0.0982     5671
1.230178 1.137203          128          128.0  -1.0000   0.0846     8778
1.093884 0.957590          256          256.0  -1.0000  -0.9719    38781

finished run
number of examples per pass = 250
passes used = 2
weighted example sum = 500.000000
weighted label sum = -60.000000
average loss = 0.638042
best constant = -0.120000
best constant's loss = 0.985600
total feature number = 8353018
