diff options
Diffstat (limited to 'dtrain/test/example/expected-output')
| -rw-r--r-- | dtrain/test/example/expected-output | 130 |
1 files changed, 47 insertions, 83 deletions
diff --git a/dtrain/test/example/expected-output b/dtrain/test/example/expected-output index 25d2c069..05326763 100644 --- a/dtrain/test/example/expected-output +++ b/dtrain/test/example/expected-output @@ -1,31 +1,20 @@ 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 +Seeding random number sequence to 2912000813 dtrain Parameters: k 100 N 4 - T 3 + T 2 scorer 'stupid_bleu' sample from 'kbest' filter 'uniq' - learning rate 0.0001 + learning rate 1 gamma 0 loss margin 0 pairs 'XYX' @@ -33,93 +22,68 @@ Parameters: 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 3. +Iteration #1 of 2. . 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 + 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: 0.16872 (+0.16872) - 1best avg model score: -1.8276 (-1.8276) - avg # pairs: 1121.1 - avg # rank err: 555.6 + 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: 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 + non0 feature count: 528 + avg list sz: 85.7 + avg f count: 102.75 (time 0.083 min, 0.5 s/S) -Iteration #3 of 3. +Iteration #2 of 2. . 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 + 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: 0.2962 (+0.11225) - 1best avg model score: -36.274 (-39.479) - avg # pairs: 1109.6 - avg # rank err: 515.9 + 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: 741 - avg list sz: 77 - avg f count: 88.982 -(time 0.083 min, 0.5 s/S) + 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: 3 [SCORE 'stupid_bleu'=0.2962]. -This took 0.26667 min. +Best iteration: 2 [SCORE 'stupid_bleu'=0.277]. +This took 0.15 min. |