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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.
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