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cdec cfg 'test/example/cdec.ini'
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
****************************************************************************************************
Example feature: Shape_S00000_T00000
Seeding random number sequence to 2912000813
dtrain
Parameters:
k 100
N 4
T 2
scorer 'stupid_bleu'
sample from 'kbest'
filter 'uniq'
learning rate 1
gamma 0
loss margin 0
pairs 'XYX'
hi lo 0.1
pair threshold 0
select weights 'VOID'
l1 reg 0 'none'
max pairs 4294967295
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 2.
. 10
Stopping after 10 input sentences.
WEIGHTS
Glue = -637
WordPenalty = +1064
LanguageModel = +1175.3
LanguageModel_OOV = -1437
PhraseModel_0 = +1935.6
PhraseModel_1 = +2499.3
PhraseModel_2 = +964.96
PhraseModel_3 = +1410.8
PhraseModel_4 = -5977.9
PhraseModel_5 = +522
PhraseModel_6 = +1089
PassThrough = -1308
---
1best avg score: 0.16963 (+0.16963)
1best avg model score: 64485 (+64485)
avg # pairs: 1494.4
avg # rank err: 702.6
avg # margin viol: 0
non0 feature count: 528
avg list sz: 85.7
avg f count: 102.75
(time 0.083 min, 0.5 s/S)
Iteration #2 of 2.
. 10
WEIGHTS
Glue = -1196
WordPenalty = +809.52
LanguageModel = +3112.1
LanguageModel_OOV = -1464
PhraseModel_0 = +3895.5
PhraseModel_1 = +4683.4
PhraseModel_2 = +1092.8
PhraseModel_3 = +1079.6
PhraseModel_4 = -6827.7
PhraseModel_5 = -888
PhraseModel_6 = +142
PassThrough = -1335
---
1best avg score: 0.277 (+0.10736)
1best avg model score: -3110.5 (-67595)
avg # pairs: 1144.2
avg # rank err: 529.1
avg # margin viol: 0
non0 feature count: 859
avg list sz: 74.9
avg f count: 112.84
(time 0.067 min, 0.4 s/S)
Writing weights file to '-' ...
done
---
Best iteration: 2 [SCORE 'stupid_bleu'=0.277].
This took 0.15 min.
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