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+ cdec cfg 'test/example/cdec.ini'
+feature: WordPenalty (no config parameters)
+State is 0 bytes for feature WordPenalty
+feature: KLanguageModel (with config parameters 'test/example/nc-wmt11.en.srilm.gz')
+Loading the LM will be faster if you build a binary file.
+Reading test/example/nc-wmt11.en.srilm.gz
+----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
+****************************************************************************************************
+Loaded 5-gram KLM from test/example/nc-wmt11.en.srilm.gz (MapSize=49581)
+State is 98 bytes for feature KLanguageModel test/example/nc-wmt11.en.srilm.gz
+feature: RuleIdentityFeatures (no config parameters)
+State is 0 bytes for feature RuleIdentityFeatures
+feature: RuleNgramFeatures (no config parameters)
+State is 0 bytes for feature RuleNgramFeatures
+feature: RuleShape (no config parameters)
+ Example feature: Shape_S00000_T00000
+State is 0 bytes for feature RuleShape
+Seeding random number sequence to 1072059181
+
+dtrain
+Parameters:
+ k 100
+ N 4
+ T 3
+ scorer 'stupid_bleu'
+ sample from 'kbest'
+ filter 'uniq'
+ learning rate 0.0001
+ gamma 0
+ loss margin 0
+ pairs 'XYX'
+ hi lo 0.1
+ pair threshold 0
+ select weights 'VOID'
+ l1 reg 0 'none'
+ cdec cfg 'test/example/cdec.ini'
+ input 'test/example/nc-wmt11.1k.gz'
+ output '-'
+ stop_after 10
+(a dot represents 10 inputs)
+Iteration #1 of 3.
+ . 10
+Stopping after 10 input sentences.
+WEIGHTS
+ Glue = -0.0293
+ WordPenalty = +0.049075
+ LanguageModel = +0.24345
+ LanguageModel_OOV = -0.2029
+ PhraseModel_0 = +0.0084102
+ PhraseModel_1 = +0.021729
+ PhraseModel_2 = +0.014922
+ PhraseModel_3 = +0.104
+ PhraseModel_4 = -0.14308
+ PhraseModel_5 = +0.0247
+ PhraseModel_6 = -0.012
+ PassThrough = -0.2161
+ ---
+ 1best avg score: 0.16872 (+0.16872)
+ 1best avg model score: -1.8276 (-1.8276)
+ avg # pairs: 1121.1
+ avg # rank err: 555.6
+ avg # margin viol: 0
+ non0 feature count: 277
+ avg list sz: 77.2
+ avg f count: 90.96
+(time 0.1 min, 0.6 s/S)
+
+Iteration #2 of 3.
+ . 10
+WEIGHTS
+ Glue = -0.3526
+ WordPenalty = +0.067576
+ LanguageModel = +1.155
+ LanguageModel_OOV = -0.2728
+ PhraseModel_0 = -0.025529
+ PhraseModel_1 = +0.095869
+ PhraseModel_2 = +0.094567
+ PhraseModel_3 = +0.12482
+ PhraseModel_4 = -0.36533
+ PhraseModel_5 = +0.1068
+ PhraseModel_6 = -0.1517
+ PassThrough = -0.286
+ ---
+ 1best avg score: 0.18394 (+0.015221)
+ 1best avg model score: 3.205 (+5.0326)
+ avg # pairs: 1168.3
+ avg # rank err: 594.8
+ avg # margin viol: 0
+ non0 feature count: 543
+ avg list sz: 77.5
+ avg f count: 85.916
+(time 0.083 min, 0.5 s/S)
+
+Iteration #3 of 3.
+ . 10
+WEIGHTS
+ Glue = -0.392
+ WordPenalty = +0.071963
+ LanguageModel = +0.81266
+ LanguageModel_OOV = -0.4177
+ PhraseModel_0 = -0.2649
+ PhraseModel_1 = -0.17931
+ PhraseModel_2 = +0.038261
+ PhraseModel_3 = +0.20261
+ PhraseModel_4 = -0.42621
+ PhraseModel_5 = +0.3198
+ PhraseModel_6 = -0.1437
+ PassThrough = -0.4309
+ ---
+ 1best avg score: 0.2962 (+0.11225)
+ 1best avg model score: -36.274 (-39.479)
+ avg # pairs: 1109.6
+ avg # rank err: 515.9
+ avg # margin viol: 0
+ non0 feature count: 741
+ avg list sz: 77
+ avg f count: 88.982
+(time 0.083 min, 0.5 s/S)
+
+Writing weights file to '-' ...
+done
+
+---
+Best iteration: 3 [SCORE 'stupid_bleu'=0.2962].
+This took 0.26667 min.