diff options
Diffstat (limited to 'training/dtrain/examples/parallelized/work')
31 files changed, 447 insertions, 448 deletions
diff --git a/training/dtrain/examples/parallelized/work/out.0.0 b/training/dtrain/examples/parallelized/work/out.0.0 index f394a9b0..9154c906 100644 --- a/training/dtrain/examples/parallelized/work/out.0.0 +++ b/training/dtrain/examples/parallelized/work/out.0.0 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 2577966319 +Seeding random number sequence to 4087834873 dtrain Parameters: @@ -33,33 +33,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = -0.0358 - WordPenalty = +0.099236 - LanguageModel = +0.51874 - LanguageModel_OOV = -0.1512 - PhraseModel_0 = -0.10121 - PhraseModel_1 = -0.25462 - PhraseModel_2 = -0.14282 - PhraseModel_3 = +0.068512 - PhraseModel_4 = -0.78139 - PhraseModel_5 = +0 - PhraseModel_6 = +0.1547 - PassThrough = -0.075 + Glue = +0.257 + WordPenalty = +0.026926 + LanguageModel = +0.67342 + LanguageModel_OOV = -0.046 + PhraseModel_0 = +0.25329 + PhraseModel_1 = +0.20036 + PhraseModel_2 = +0.00060731 + PhraseModel_3 = +0.65578 + PhraseModel_4 = +0.47916 + PhraseModel_5 = +0.004 + PhraseModel_6 = +0.1829 + PassThrough = -0.082 --- - 1best avg score: 0.080513 (+0.080513) - 1best avg model score: 6.1321 (+6.1321) - avg # pairs: 1848.3 - avg # rank err: 1096.7 - avg # margin viol: 751.67 + 1best avg score: 0.04518 (+0.04518) + 1best avg model score: 32.803 (+32.803) + avg # pairs: 1266.3 + avg # rank err: 857 + avg # margin viol: 386.67 k-best loss imp: 100% - non0 feature count: 11 + non0 feature count: 12 avg list sz: 100 - avg f count: 10.6 -(time 0.23 min, 4.7 s/S) + avg f count: 10.853 +(time 0.47 min, 9.3 s/S) Writing weights file to 'work/weights.0.0' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.080513]. -This took 0.23333 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.04518]. +This took 0.46667 min. diff --git a/training/dtrain/examples/parallelized/work/out.0.1 b/training/dtrain/examples/parallelized/work/out.0.1 index d0819a5a..0dbc7bd3 100644 --- a/training/dtrain/examples/parallelized/work/out.0.1 +++ b/training/dtrain/examples/parallelized/work/out.0.1 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 3555678516 +Seeding random number sequence to 2283043509 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = +0.19265 - WordPenalty = +0.0064601 - LanguageModel = +0.63102 - LanguageModel_OOV = -0.58027 - PhraseModel_0 = -0.71998 - PhraseModel_1 = +0.67713 - PhraseModel_2 = +1.2848 - PhraseModel_3 = -0.30726 - PhraseModel_4 = -0.91479 - PhraseModel_5 = +0.026825 - PhraseModel_6 = -0.31892 - PassThrough = -0.51565 + Glue = -0.17905 + WordPenalty = +0.062126 + LanguageModel = +0.66825 + LanguageModel_OOV = -0.15248 + PhraseModel_0 = -0.55811 + PhraseModel_1 = +0.12741 + PhraseModel_2 = +0.60388 + PhraseModel_3 = -0.44464 + PhraseModel_4 = -0.63137 + PhraseModel_5 = -0.0084 + PhraseModel_6 = -0.20165 + PassThrough = -0.23468 --- - 1best avg score: 0.12642 (+0.12642) - 1best avg model score: -30.689 (-30.689) - avg # pairs: 1682.7 - avg # rank err: 807 - avg # margin viol: 872 + 1best avg score: 0.14066 (+0.14066) + 1best avg model score: -37.614 (-37.614) + avg # pairs: 1244.7 + avg # rank err: 728 + avg # margin viol: 516.67 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 12 -(time 0.27 min, 5.3 s/S) + avg f count: 11.507 +(time 0.45 min, 9 s/S) Writing weights file to 'work/weights.0.1' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.12642]. -This took 0.26667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.14066]. +This took 0.45 min. diff --git a/training/dtrain/examples/parallelized/work/out.0.2 b/training/dtrain/examples/parallelized/work/out.0.2 index 62bf8bb9..fcecc7e1 100644 --- a/training/dtrain/examples/parallelized/work/out.0.2 +++ b/training/dtrain/examples/parallelized/work/out.0.2 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 2696902705 +Seeding random number sequence to 3693132895 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = -0.2741 - WordPenalty = +0.1227 - LanguageModel = +0.82597 - LanguageModel_OOV = -0.52135 - PhraseModel_0 = -0.68526 - PhraseModel_1 = +0.27265 - PhraseModel_2 = +0.87438 - PhraseModel_3 = -0.00012234 - PhraseModel_4 = -1.0912 - PhraseModel_5 = +0.0371 - PhraseModel_6 = -0.2855 - PassThrough = -0.4831 + Glue = -0.019275 + WordPenalty = +0.022192 + LanguageModel = +0.40688 + LanguageModel_OOV = -0.36397 + PhraseModel_0 = -0.36273 + PhraseModel_1 = +0.56432 + PhraseModel_2 = +0.85638 + PhraseModel_3 = -0.20222 + PhraseModel_4 = -0.48295 + PhraseModel_5 = +0.03145 + PhraseModel_6 = -0.26092 + PassThrough = -0.38122 --- - 1best avg score: 0.12697 (+0.12697) - 1best avg model score: -1.7396 (-1.7396) - avg # pairs: 1280.3 - avg # rank err: 764.33 - avg # margin viol: 507 + 1best avg score: 0.18982 (+0.18982) + 1best avg model score: 1.7096 (+1.7096) + avg # pairs: 1524.3 + avg # rank err: 813.33 + avg # margin viol: 702.67 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 10.727 -(time 0.28 min, 5.7 s/S) + avg f count: 11.32 +(time 0.53 min, 11 s/S) Writing weights file to 'work/weights.0.2' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.12697]. -This took 0.28333 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.18982]. +This took 0.53333 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.0 b/training/dtrain/examples/parallelized/work/out.1.0 index cc35e676..595dfc94 100644 --- a/training/dtrain/examples/parallelized/work/out.1.0 +++ b/training/dtrain/examples/parallelized/work/out.1.0 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 1336015864 +Seeding random number sequence to 859043351 dtrain Parameters: @@ -33,33 +33,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = -0.2015 - WordPenalty = +0.078303 - LanguageModel = +0.90323 - LanguageModel_OOV = -0.1378 - PhraseModel_0 = -1.3044 - PhraseModel_1 = -0.88246 - PhraseModel_2 = +0.26379 - PhraseModel_3 = -0.79106 - PhraseModel_4 = -1.4702 - PhraseModel_5 = +0.0218 - PhraseModel_6 = -0.5283 - PassThrough = -0.2531 + Glue = -0.3229 + WordPenalty = +0.27969 + LanguageModel = +1.3645 + LanguageModel_OOV = -0.0443 + PhraseModel_0 = -0.19049 + PhraseModel_1 = -0.077698 + PhraseModel_2 = +0.058898 + PhraseModel_3 = +0.017251 + PhraseModel_4 = -1.5474 + PhraseModel_5 = +0 + PhraseModel_6 = -0.1818 + PassThrough = -0.193 --- - 1best avg score: 0.062351 (+0.062351) - 1best avg model score: -47.109 (-47.109) - avg # pairs: 1284 - avg # rank err: 844.33 - avg # margin viol: 216.33 + 1best avg score: 0.070229 (+0.070229) + 1best avg model score: -44.01 (-44.01) + avg # pairs: 1294 + avg # rank err: 878.67 + avg # margin viol: 350.67 k-best loss imp: 100% - non0 feature count: 12 + non0 feature count: 11 avg list sz: 100 - avg f count: 11.883 -(time 0.42 min, 8.3 s/S) + avg f count: 11.487 +(time 0.28 min, 5.7 s/S) Writing weights file to 'work/weights.1.0' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.062351]. -This took 0.41667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.070229]. +This took 0.28333 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.1 b/training/dtrain/examples/parallelized/work/out.1.1 index 3d7a7e66..9346fc82 100644 --- a/training/dtrain/examples/parallelized/work/out.1.1 +++ b/training/dtrain/examples/parallelized/work/out.1.1 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 1673913538 +Seeding random number sequence to 3557309480 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = -0.15575 - WordPenalty = +0.14939 - LanguageModel = +0.95915 - LanguageModel_OOV = -0.42267 - PhraseModel_0 = -0.46337 - PhraseModel_1 = +0.36682 - PhraseModel_2 = +0.79339 - PhraseModel_3 = +0.27497 - PhraseModel_4 = -1.2038 - PhraseModel_5 = +0.061325 - PhraseModel_6 = -0.11143 - PassThrough = -0.45405 + Glue = -0.26425 + WordPenalty = +0.047881 + LanguageModel = +0.78496 + LanguageModel_OOV = -0.49307 + PhraseModel_0 = -0.58703 + PhraseModel_1 = -0.33425 + PhraseModel_2 = +0.20834 + PhraseModel_3 = -0.043346 + PhraseModel_4 = -0.60761 + PhraseModel_5 = +0.123 + PhraseModel_6 = -0.05415 + PassThrough = -0.42167 --- - 1best avg score: 0.057772 (+0.057772) - 1best avg model score: -59.945 (-59.945) - avg # pairs: 1647 - avg # rank err: 878 - avg # margin viol: 564.67 + 1best avg score: 0.085952 (+0.085952) + 1best avg model score: -45.175 (-45.175) + avg # pairs: 1180.7 + avg # rank err: 668.33 + avg # margin viol: 512.33 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 11.973 -(time 0.42 min, 8.3 s/S) + avg f count: 12 +(time 0.27 min, 5.3 s/S) Writing weights file to 'work/weights.1.1' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.057772]. -This took 0.41667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.085952]. +This took 0.26667 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.2 b/training/dtrain/examples/parallelized/work/out.1.2 index ba603651..08f07a75 100644 --- a/training/dtrain/examples/parallelized/work/out.1.2 +++ b/training/dtrain/examples/parallelized/work/out.1.2 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 785956183 +Seeding random number sequence to 56743915 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = -0.2323 - WordPenalty = +0.11501 - LanguageModel = +0.76484 - LanguageModel_OOV = -0.57495 - PhraseModel_0 = -0.64111 - PhraseModel_1 = +0.44772 - PhraseModel_2 = +0.98529 - PhraseModel_3 = +0.022939 - PhraseModel_4 = -1.1029 - PhraseModel_5 = +0.0491 - PhraseModel_6 = -0.315 - PassThrough = -0.5367 + Glue = -0.23608 + WordPenalty = +0.10931 + LanguageModel = +0.81339 + LanguageModel_OOV = -0.33238 + PhraseModel_0 = -0.53685 + PhraseModel_1 = -0.049658 + PhraseModel_2 = +0.40277 + PhraseModel_3 = +0.14601 + PhraseModel_4 = -0.72851 + PhraseModel_5 = +0.03475 + PhraseModel_6 = -0.27192 + PassThrough = -0.34763 --- - 1best avg score: 0.24871 (+0.24871) - 1best avg model score: -3.0138 (-3.0138) - avg # pairs: 1489.7 - avg # rank err: 644.67 - avg # margin viol: 549 + 1best avg score: 0.10073 (+0.10073) + 1best avg model score: -38.422 (-38.422) + avg # pairs: 1505.3 + avg # rank err: 777 + avg # margin viol: 691.67 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 11.187 -(time 0.43 min, 8.7 s/S) + avg f count: 12 +(time 0.35 min, 7 s/S) Writing weights file to 'work/weights.1.2' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.24871]. -This took 0.43333 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.10073]. +This took 0.35 min. diff --git a/training/dtrain/examples/parallelized/work/out.2.0 b/training/dtrain/examples/parallelized/work/out.2.0 index ab38c637..25ef6d4e 100644 --- a/training/dtrain/examples/parallelized/work/out.2.0 +++ b/training/dtrain/examples/parallelized/work/out.2.0 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 3274281797 +Seeding random number sequence to 2662215673 dtrain Parameters: @@ -33,33 +33,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = +0.1295 - WordPenalty = +0.12781 - LanguageModel = +1.1825 - LanguageModel_OOV = -0.1667 - PhraseModel_0 = -0.65167 - PhraseModel_1 = -0.044563 - PhraseModel_2 = +0.49706 - PhraseModel_3 = -0.40367 - PhraseModel_4 = -1.3438 - PhraseModel_5 = +0.0435 - PhraseModel_6 = -0.3743 - PassThrough = -0.0307 + Glue = -0.1259 + WordPenalty = +0.048294 + LanguageModel = +0.36254 + LanguageModel_OOV = -0.1228 + PhraseModel_0 = +0.26357 + PhraseModel_1 = +0.24793 + PhraseModel_2 = +0.0063763 + PhraseModel_3 = -0.18966 + PhraseModel_4 = -0.226 + PhraseModel_5 = +0 + PhraseModel_6 = +0.0743 + PassThrough = -0.1335 --- - 1best avg score: 0.08637 (+0.08637) - 1best avg model score: -42.175 (-42.175) - avg # pairs: 1136.3 - avg # rank err: 720.67 - avg # margin viol: 399.67 + 1best avg score: 0.072836 (+0.072836) + 1best avg model score: -0.56296 (-0.56296) + avg # pairs: 1094.7 + avg # rank err: 658 + avg # margin viol: 436.67 k-best loss imp: 100% - non0 feature count: 12 + non0 feature count: 11 avg list sz: 100 - avg f count: 11.487 -(time 0.22 min, 4.3 s/S) + avg f count: 10.813 +(time 0.13 min, 2.7 s/S) Writing weights file to 'work/weights.2.0' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.08637]. -This took 0.21667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.072836]. +This took 0.13333 min. diff --git a/training/dtrain/examples/parallelized/work/out.2.1 b/training/dtrain/examples/parallelized/work/out.2.1 index f86ec520..8e4efde9 100644 --- a/training/dtrain/examples/parallelized/work/out.2.1 +++ b/training/dtrain/examples/parallelized/work/out.2.1 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 3424877412 +Seeding random number sequence to 3092904479 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = -0.33455 - WordPenalty = +0.10696 - LanguageModel = +1.0621 - LanguageModel_OOV = -0.46617 - PhraseModel_0 = -0.63382 - PhraseModel_1 = +0.33225 - PhraseModel_2 = +0.8501 - PhraseModel_3 = -0.29374 - PhraseModel_4 = -1.0908 - PhraseModel_5 = +0.033425 - PhraseModel_6 = -0.38922 - PassThrough = -0.36385 + Glue = -0.10385 + WordPenalty = +0.038717 + LanguageModel = +0.49413 + LanguageModel_OOV = -0.24887 + PhraseModel_0 = -0.32102 + PhraseModel_1 = +0.34413 + PhraseModel_2 = +0.62366 + PhraseModel_3 = -0.49337 + PhraseModel_4 = -0.77005 + PhraseModel_5 = +0.007 + PhraseModel_6 = -0.05055 + PassThrough = -0.23928 --- - 1best avg score: 0.12089 (+0.12089) - 1best avg model score: -30.902 (-30.902) - avg # pairs: 1852 - avg # rank err: 870.33 - avg # margin viol: 898.67 + 1best avg score: 0.10245 (+0.10245) + 1best avg model score: -20.384 (-20.384) + avg # pairs: 1741.7 + avg # rank err: 953.67 + avg # margin viol: 585.33 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 12 -(time 0.22 min, 4.3 s/S) + avg f count: 11.977 +(time 0.12 min, 2.3 s/S) Writing weights file to 'work/weights.2.1' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.12089]. -This took 0.21667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.10245]. +This took 0.11667 min. diff --git a/training/dtrain/examples/parallelized/work/out.2.2 b/training/dtrain/examples/parallelized/work/out.2.2 index 823129c0..e0ca2110 100644 --- a/training/dtrain/examples/parallelized/work/out.2.2 +++ b/training/dtrain/examples/parallelized/work/out.2.2 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 3087490723 +Seeding random number sequence to 2803362953 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 3 WEIGHTS - Glue = -0.3464 - WordPenalty = +0.18737 - LanguageModel = +1.5794 - LanguageModel_OOV = -0.48725 - PhraseModel_0 = -1.0015 - PhraseModel_1 = -0.51734 - PhraseModel_2 = +0.40486 - PhraseModel_3 = -0.013031 - PhraseModel_4 = -1.1546 - PhraseModel_5 = +0.0371 - PhraseModel_6 = -0.1892 - PassThrough = -0.449 + Glue = -0.32907 + WordPenalty = +0.049596 + LanguageModel = +0.33496 + LanguageModel_OOV = -0.44357 + PhraseModel_0 = -0.3068 + PhraseModel_1 = +0.59376 + PhraseModel_2 = +0.86416 + PhraseModel_3 = -0.21072 + PhraseModel_4 = -0.65734 + PhraseModel_5 = +0.03475 + PhraseModel_6 = -0.10653 + PassThrough = -0.46082 --- - 1best avg score: 0.17557 (+0.17557) - 1best avg model score: -15.133 (-15.133) - avg # pairs: 1644.7 - avg # rank err: 830.33 - avg # margin viol: 766.33 + 1best avg score: 0.25055 (+0.25055) + 1best avg model score: -1.4459 (-1.4459) + avg # pairs: 1689 + avg # rank err: 755.67 + avg # margin viol: 829.33 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 11.267 -(time 0.23 min, 4.7 s/S) + avg f count: 10.53 +(time 0.13 min, 2.7 s/S) Writing weights file to 'work/weights.2.2' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.17557]. -This took 0.23333 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.25055]. +This took 0.13333 min. diff --git a/training/dtrain/examples/parallelized/work/out.3.0 b/training/dtrain/examples/parallelized/work/out.3.0 index 2d8dea27..3c074f04 100644 --- a/training/dtrain/examples/parallelized/work/out.3.0 +++ b/training/dtrain/examples/parallelized/work/out.3.0 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 164953210 +Seeding random number sequence to 316107185 dtrain Parameters: @@ -33,20 +33,20 @@ Parameters: Iteration #1 of 1. 1 WEIGHTS - Glue = -0.11 - WordPenalty = +0.21975 - LanguageModel = +1.7397 - LanguageModel_OOV = -0.037 - PhraseModel_0 = -0.34702 - PhraseModel_1 = +0.11602 - PhraseModel_2 = +0.3951 - PhraseModel_3 = +0.37857 - PhraseModel_4 = -1.0319 - PhraseModel_5 = +0.042 - PhraseModel_6 = -0.253 - PassThrough = -0.111 + Glue = +0.046 + WordPenalty = +0.17328 + LanguageModel = +1.1667 + LanguageModel_OOV = +0.066 + PhraseModel_0 = -1.1694 + PhraseModel_1 = -0.9883 + PhraseModel_2 = +0.036205 + PhraseModel_3 = -0.77387 + PhraseModel_4 = -1.5019 + PhraseModel_5 = +0.024 + PhraseModel_6 = -0.514 + PassThrough = +0.031 --- - 1best avg score: 0.034204 (+0.034204) + 1best avg score: 0.032916 (+0.032916) 1best avg model score: 0 (+0) avg # pairs: 900 avg # rank err: 900 @@ -54,12 +54,12 @@ WEIGHTS k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 10.8 -(time 0.12 min, 7 s/S) + avg f count: 11.72 +(time 0.23 min, 14 s/S) Writing weights file to 'work/weights.3.0' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.034204]. -This took 0.11667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.032916]. +This took 0.23333 min. diff --git a/training/dtrain/examples/parallelized/work/out.3.1 b/training/dtrain/examples/parallelized/work/out.3.1 index a1eeb64b..241d3455 100644 --- a/training/dtrain/examples/parallelized/work/out.3.1 +++ b/training/dtrain/examples/parallelized/work/out.3.1 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 2079701870 +Seeding random number sequence to 353677750 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 1 WEIGHTS - Glue = -0.63235 - WordPenalty = +0.10761 - LanguageModel = +1.4703 - LanguageModel_OOV = -0.45548 - PhraseModel_0 = -0.34858 - PhraseModel_1 = +0.050651 - PhraseModel_2 = +0.32137 - PhraseModel_3 = +0.31848 - PhraseModel_4 = -0.96702 - PhraseModel_5 = +0.026825 - PhraseModel_6 = -0.30802 - PassThrough = -0.43805 + Glue = -0.08475 + WordPenalty = +0.11151 + LanguageModel = +1.0635 + LanguageModel_OOV = -0.11468 + PhraseModel_0 = -0.062922 + PhraseModel_1 = +0.0035552 + PhraseModel_2 = +0.039692 + PhraseModel_3 = +0.080265 + PhraseModel_4 = -0.57787 + PhraseModel_5 = +0.0174 + PhraseModel_6 = -0.17095 + PassThrough = -0.18248 --- - 1best avg score: 0.078383 (+0.078383) - 1best avg model score: -68.182 (-68.182) + 1best avg score: 0.16117 (+0.16117) + 1best avg model score: -67.89 (-67.89) avg # pairs: 1411 - avg # rank err: 599 - avg # margin viol: 801 + avg # rank err: 460 + avg # margin viol: 951 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 avg f count: 12 -(time 0.12 min, 7 s/S) +(time 0.22 min, 13 s/S) Writing weights file to 'work/weights.3.1' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.078383]. -This took 0.11667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.16117]. +This took 0.21667 min. diff --git a/training/dtrain/examples/parallelized/work/out.3.2 b/training/dtrain/examples/parallelized/work/out.3.2 index a0c0e509..b995daf5 100644 --- a/training/dtrain/examples/parallelized/work/out.3.2 +++ b/training/dtrain/examples/parallelized/work/out.3.2 @@ -3,7 +3,7 @@ Loading the LM will be faster if you build a binary file. Reading ../standard/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 **************************************************************************************************** -Seeding random number sequence to 3524794953 +Seeding random number sequence to 3001145976 dtrain Parameters: @@ -34,33 +34,33 @@ Parameters: Iteration #1 of 1. 1 WEIGHTS - Glue = -0.2581 - WordPenalty = +0.091647 - LanguageModel = +0.77537 - LanguageModel_OOV = -0.57165 - PhraseModel_0 = -0.5794 - PhraseModel_1 = +0.46929 - PhraseModel_2 = +0.95471 - PhraseModel_3 = +0.12107 - PhraseModel_4 = -1.0053 - PhraseModel_5 = +0.0371 - PhraseModel_6 = -0.3253 - PassThrough = -0.5334 + Glue = -0.13247 + WordPenalty = +0.053592 + LanguageModel = +0.72105 + LanguageModel_OOV = -0.30827 + PhraseModel_0 = -0.37053 + PhraseModel_1 = +0.17551 + PhraseModel_2 = +0.5 + PhraseModel_3 = -0.1459 + PhraseModel_4 = -0.59563 + PhraseModel_5 = +0.03475 + PhraseModel_6 = -0.11143 + PassThrough = -0.32553 --- - 1best avg score: 0.10945 (+0.10945) - 1best avg model score: -23.077 (-23.077) - avg # pairs: 1545 - avg # rank err: 987 - avg # margin viol: 558 + 1best avg score: 0.12501 (+0.12501) + 1best avg model score: -62.128 (-62.128) + avg # pairs: 979 + avg # rank err: 539 + avg # margin viol: 440 k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 avg f count: 12 -(time 0.12 min, 7 s/S) +(time 0.22 min, 13 s/S) Writing weights file to 'work/weights.3.2' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.10945]. -This took 0.11667 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.12501]. +This took 0.21667 min. diff --git a/training/dtrain/examples/parallelized/work/shard.0.0.in b/training/dtrain/examples/parallelized/work/shard.0.0.in index fb8c2cd6..d1b48321 100644 --- a/training/dtrain/examples/parallelized/work/shard.0.0.in +++ b/training/dtrain/examples/parallelized/work/shard.0.0.in @@ -1,3 +1,3 @@ -<seg grammar="grammar/grammar.out.1.gz" id="1">ein gemeinsames merkmal aller extremen rechten in europa ist ihr rassismus und die tatsache , daß sie das einwanderungsproblem als politischen hebel benutzen .</seg> ||| a common feature of europe 's extreme right is its racism and use of the immigration issue as a political wedge . -<seg grammar="grammar/grammar.out.6.gz" id="6">das aber wird es nicht , wie die geschichte des rassismus in amerika deutlich zeigt .</seg> ||| it will not , as america 's racial history clearly shows . +<seg grammar="grammar/grammar.out.8.gz" id="8">der erste schritt , um mit der rassenfrage umzugehen ist , ursache und folgen rassistischer feindseligkeiten zu verstehen , auch dann , wenn das bedeutet , unangenehme tatsachen aufzudecken .</seg> ||| the first step to address racial politics is to understand the origin and consequences of racial animosity , even if it means uncovering unpleasant truths . <seg grammar="grammar/grammar.out.5.gz" id="5">die großen parteien der rechten und der linken mitte haben sich dem problem gestellt , in dem sie den kopf in den sand gesteckt und allen aussichten zuwider gehofft haben , es möge bald verschwinden .</seg> ||| mainstream parties of the center left and center right have confronted this prospect by hiding their heads in the ground , hoping against hope that the problem will disappear . +<seg grammar="grammar/grammar.out.2.gz" id="2">der lega nord in italien , der vlaams block in den niederlanden , die anhänger von le pens nationaler front in frankreich , sind beispiele für parteien oder bewegungen , die sich um das gemeinsame thema : ablehnung der zuwanderung gebildet haben und um forderung nach einer vereinfachten politik , um sie zu regeln .</seg> ||| the lega nord in italy , the vlaams blok in the netherlands , the supporters of le pen 's national front in france , are all examples of parties or movements formed on the common theme of aversion to immigrants and promotion of simplistic policies to control them . diff --git a/training/dtrain/examples/parallelized/work/shard.1.0.in b/training/dtrain/examples/parallelized/work/shard.1.0.in index c28d1502..a63f05bd 100644 --- a/training/dtrain/examples/parallelized/work/shard.1.0.in +++ b/training/dtrain/examples/parallelized/work/shard.1.0.in @@ -1,3 +1,3 @@ -<seg grammar="grammar/grammar.out.7.gz" id="7">die beziehungen zwischen den rassen standen in den usa über jahrzehnte - und tun das noch heute - im zentrum der politischen debatte . das ging so weit , daß rassentrennung genauso wichtig wie das einkommen wurde , - wenn nicht sogar noch wichtiger - um politische zuneigungen und einstellungen zu bestimmen .</seg> ||| race relations in the us have been for decades - and remain - at the center of political debate , to the point that racial cleavages are as important as income , if not more , as determinants of political preferences and attitudes . -<seg grammar="grammar/grammar.out.0.gz" id="0">europas nach rassen geteiltes haus</seg> ||| europe 's divided racial house -<seg grammar="grammar/grammar.out.2.gz" id="2">der lega nord in italien , der vlaams block in den niederlanden , die anhänger von le pens nationaler front in frankreich , sind beispiele für parteien oder bewegungen , die sich um das gemeinsame thema : ablehnung der zuwanderung gebildet haben und um forderung nach einer vereinfachten politik , um sie zu regeln .</seg> ||| the lega nord in italy , the vlaams blok in the netherlands , the supporters of le pen 's national front in france , are all examples of parties or movements formed on the common theme of aversion to immigrants and promotion of simplistic policies to control them . +<seg grammar="grammar/grammar.out.4.gz" id="4">eine alternde einheimische bevölkerung und immer offenere grenzen vermehren die rassistische zersplitterung in den europäischen ländern .</seg> ||| an aging population at home and ever more open borders imply increasing racial fragmentation in european countries . +<seg grammar="grammar/grammar.out.9.gz" id="9">genau das haben in den usa eine große anzahl an forschungsvorhaben in wirtschaft , soziologie , psychologie und politikwissenschaft geleistet . diese forschungen zeigten , daß menschen unterschiedlicher rasse einander deutlich weniger vertrauen .</seg> ||| this is precisely what a large amount of research in economics , sociology , psychology and political science has done for the us . +<seg grammar="grammar/grammar.out.3.gz" id="3">während individuen wie jörg haidar und jean @-@ marie le pen kommen und ( leider nicht zu bald ) wieder gehen mögen , wird die rassenfrage aus der europäischer politik nicht so bald verschwinden .</seg> ||| while individuals like jorg haidar and jean @-@ marie le pen may come and ( never to soon ) go , the race question will not disappear from european politics anytime soon . diff --git a/training/dtrain/examples/parallelized/work/shard.2.0.in b/training/dtrain/examples/parallelized/work/shard.2.0.in index 85f68e20..fe542b40 100644 --- a/training/dtrain/examples/parallelized/work/shard.2.0.in +++ b/training/dtrain/examples/parallelized/work/shard.2.0.in @@ -1,3 +1,3 @@ -<seg grammar="grammar/grammar.out.4.gz" id="4">eine alternde einheimische bevölkerung und immer offenere grenzen vermehren die rassistische zersplitterung in den europäischen ländern .</seg> ||| an aging population at home and ever more open borders imply increasing racial fragmentation in european countries . -<seg grammar="grammar/grammar.out.3.gz" id="3">während individuen wie jörg haidar und jean @-@ marie le pen kommen und ( leider nicht zu bald ) wieder gehen mögen , wird die rassenfrage aus der europäischer politik nicht so bald verschwinden .</seg> ||| while individuals like jorg haidar and jean @-@ marie le pen may come and ( never to soon ) go , the race question will not disappear from european politics anytime soon . -<seg grammar="grammar/grammar.out.8.gz" id="8">der erste schritt , um mit der rassenfrage umzugehen ist , ursache und folgen rassistischer feindseligkeiten zu verstehen , auch dann , wenn das bedeutet , unangenehme tatsachen aufzudecken .</seg> ||| the first step to address racial politics is to understand the origin and consequences of racial animosity , even if it means uncovering unpleasant truths . +<seg grammar="grammar/grammar.out.1.gz" id="1">ein gemeinsames merkmal aller extremen rechten in europa ist ihr rassismus und die tatsache , daß sie das einwanderungsproblem als politischen hebel benutzen .</seg> ||| a common feature of europe 's extreme right is its racism and use of the immigration issue as a political wedge . +<seg grammar="grammar/grammar.out.0.gz" id="0">europas nach rassen geteiltes haus</seg> ||| europe 's divided racial house +<seg grammar="grammar/grammar.out.6.gz" id="6">das aber wird es nicht , wie die geschichte des rassismus in amerika deutlich zeigt .</seg> ||| it will not , as america 's racial history clearly shows . diff --git a/training/dtrain/examples/parallelized/work/shard.3.0.in b/training/dtrain/examples/parallelized/work/shard.3.0.in index f7cbb3e3..4a8fa5b1 100644 --- a/training/dtrain/examples/parallelized/work/shard.3.0.in +++ b/training/dtrain/examples/parallelized/work/shard.3.0.in @@ -1 +1 @@ -<seg grammar="grammar/grammar.out.9.gz" id="9">genau das haben in den usa eine große anzahl an forschungsvorhaben in wirtschaft , soziologie , psychologie und politikwissenschaft geleistet . diese forschungen zeigten , daß menschen unterschiedlicher rasse einander deutlich weniger vertrauen .</seg> ||| this is precisely what a large amount of research in economics , sociology , psychology and political science has done for the us . +<seg grammar="grammar/grammar.out.7.gz" id="7">die beziehungen zwischen den rassen standen in den usa über jahrzehnte - und tun das noch heute - im zentrum der politischen debatte . das ging so weit , daß rassentrennung genauso wichtig wie das einkommen wurde , - wenn nicht sogar noch wichtiger - um politische zuneigungen und einstellungen zu bestimmen .</seg> ||| race relations in the us have been for decades - and remain - at the center of political debate , to the point that racial cleavages are as important as income , if not more , as determinants of political preferences and attitudes . diff --git a/training/dtrain/examples/parallelized/work/weights.0 b/training/dtrain/examples/parallelized/work/weights.0 index aa494afb..c560fdbd 100644 --- a/training/dtrain/examples/parallelized/work/weights.0 +++ b/training/dtrain/examples/parallelized/work/weights.0 @@ -1,12 +1,12 @@ -PhraseModel_4 -1.1568444011426948 -LanguageModel 1.0860459962466693 -PhraseModel_0 -0.6010837860294569 -PhraseModel_3 -0.18690910705225725 -PhraseModel_1 -0.26640412994377044 -PhraseModel_6 -0.25022499999999803 -PhraseModel_2 0.2532838373219909 -PassThrough -0.1174500000000002 -WordPenalty 0.1312763645173042 -LanguageModel_OOV -0.12317500000000006 -Glue -0.05444999999999971 -PhraseModel_5 0.026825000000000078 +PhraseModel_4 -0.6990170657294328 +LanguageModel 0.891784887346263 +PhraseModel_0 -0.2107507586515428 +PhraseModel_1 -0.15442709655871997 +PhraseModel_3 -0.07262514338204715 +PhraseModel_6 -0.10965000000000148 +Glue -0.03644999999999783 +WordPenalty 0.13204723722268177 +PassThrough -0.09437500000000089 +LanguageModel_OOV -0.036775000000000564 +PhraseModel_2 0.025521702385571707 +PhraseModel_5 0.006999999999999977 diff --git a/training/dtrain/examples/parallelized/work/weights.0.0 b/training/dtrain/examples/parallelized/work/weights.0.0 index 541321af..91eedc7b 100644 --- a/training/dtrain/examples/parallelized/work/weights.0.0 +++ b/training/dtrain/examples/parallelized/work/weights.0.0 @@ -1,11 +1,12 @@ -LanguageModel_OOV -0.15119999999999936 -PassThrough -0.075000000000000872 -Glue -0.035799999999999721 -PhraseModel_1 -0.25461850237866285 -WordPenalty 0.099236289114895807 -PhraseModel_0 -0.101213892033636 -PhraseModel_2 -0.14281771543359051 -PhraseModel_3 0.068512482804492139 -PhraseModel_4 -0.78138944075452532 -PhraseModel_6 0.15469999999999931 -LanguageModel 0.51873837981298221 +PassThrough -0.082000000000001058 +Glue 0.25700000000000267 +LanguageModel_OOV -0.046000000000000034 +LanguageModel 0.67341721152744249 +PhraseModel_6 0.18290000000000028 +PhraseModel_5 0.0039999999999999975 +PhraseModel_4 0.47916377173928498 +PhraseModel_3 0.65577926367715722 +PhraseModel_2 0.00060731048591637909 +PhraseModel_0 0.25329462707903372 +WordPenalty 0.026926257878001431 +PhraseModel_1 0.20035945197369062 diff --git a/training/dtrain/examples/parallelized/work/weights.0.1 b/training/dtrain/examples/parallelized/work/weights.0.1 index c983747e..6fcc9999 100644 --- a/training/dtrain/examples/parallelized/work/weights.0.1 +++ b/training/dtrain/examples/parallelized/work/weights.0.1 @@ -1,12 +1,12 @@ -PassThrough -0.51564999999999106 -Glue 0.19265000000000118 -WordPenalty 0.0064601304183101293 -LanguageModel 0.63101690103206198 -LanguageModel_OOV -0.58027499999998244 -PhraseModel_0 -0.7199776484358319 -PhraseModel_1 0.67713208716270057 -PhraseModel_2 1.2847869050798759 -PhraseModel_3 -0.30726076030314797 -PhraseModel_4 -0.9147907962255597 -PhraseModel_5 0.026825000000000078 -PhraseModel_6 -0.31892499999999002 +PassThrough -0.2346750000000028 +Glue -0.17904999999999763 +WordPenalty 0.062125825636256168 +LanguageModel 0.66824625053667575 +LanguageModel_OOV -0.15247500000000355 +PhraseModel_0 -0.5581144363944085 +PhraseModel_1 0.12740874153205478 +PhraseModel_2 0.6038779278708799 +PhraseModel_3 -0.44463820299544454 +PhraseModel_4 -0.63136538282212662 +PhraseModel_5 -0.0084000000000000324 +PhraseModel_6 -0.20164999999999911 diff --git a/training/dtrain/examples/parallelized/work/weights.0.2 b/training/dtrain/examples/parallelized/work/weights.0.2 index 86795230..5668915d 100644 --- a/training/dtrain/examples/parallelized/work/weights.0.2 +++ b/training/dtrain/examples/parallelized/work/weights.0.2 @@ -1,12 +1,12 @@ -PassThrough -0.48309999999998859 -Glue -0.27409999999999729 -WordPenalty 0.12269904849971774 -LanguageModel 0.82596659132167016 -LanguageModel_OOV -0.5213499999999861 -PhraseModel_0 -0.68525899286050596 -PhraseModel_1 0.27265146052517253 -PhraseModel_2 0.87438450673072043 -PhraseModel_3 -0.00012233626643227101 -PhraseModel_4 -1.0911805651205244 -PhraseModel_5 0.037100000000000292 -PhraseModel_6 -0.28549999999999121 +PassThrough -0.38122499999999337 +Glue -0.019274999999998679 +WordPenalty 0.022192448025253487 +LanguageModel 0.4068780855136106 +LanguageModel_OOV -0.363974999999992 +PhraseModel_0 -0.36273429313029715 +PhraseModel_1 0.56431752511029298 +PhraseModel_2 0.85638010019687694 +PhraseModel_3 -0.20222345248738063 +PhraseModel_4 -0.48295466434310252 +PhraseModel_5 0.031450000000000339 +PhraseModel_6 -0.26092499999998625 diff --git a/training/dtrain/examples/parallelized/work/weights.1 b/training/dtrain/examples/parallelized/work/weights.1 index 520b575e..f52e07b8 100644 --- a/training/dtrain/examples/parallelized/work/weights.1 +++ b/training/dtrain/examples/parallelized/work/weights.1 @@ -1,12 +1,12 @@ -LanguageModel 1.0306413574382605 -PhraseModel_4 -1.0441183310270499 -PhraseModel_2 0.8124104300969892 -PhraseModel_0 -0.5414354190041899 -LanguageModel_OOV -0.48114999999999053 -PassThrough -0.442899999999993 -PhraseModel_1 0.3567134472577971 -Glue -0.2324999999999999 -PhraseModel_6 -0.2818999999999916 -PhraseModel_3 -0.001886958694580998 -WordPenalty 0.09260244090382065 -PhraseModel_5 0.03710000000000029 +LanguageModel 0.7527067666152598 +PhraseModel_4 -0.6467221787583058 +PhraseModel_2 0.36889175522051015 +PhraseModel_0 -0.38227173053779245 +PhraseModel_3 -0.2252732111174934 +LanguageModel_OOV -0.25227499999999975 +PassThrough -0.2695250000000011 +PhraseModel_1 0.03521067244127414 +Glue -0.1579749999999981 +PhraseModel_6 -0.11932500000000047 +WordPenalty 0.0650573133891042 +PhraseModel_5 0.03475000000000043 diff --git a/training/dtrain/examples/parallelized/work/weights.1.0 b/training/dtrain/examples/parallelized/work/weights.1.0 index 68f4eaf2..31e08d81 100644 --- a/training/dtrain/examples/parallelized/work/weights.1.0 +++ b/training/dtrain/examples/parallelized/work/weights.1.0 @@ -1,12 +1,11 @@ -PhraseModel_4 -1.4702479045005545 -PhraseModel_3 -0.79105519577534078 -PhraseModel_6 -0.52829999999999666 -PhraseModel_5 0.021799999999999924 -LanguageModel 0.90323355461358656 -PhraseModel_2 0.26378844109522476 -PassThrough -0.25310000000000021 -Glue -0.20149999999999982 -PhraseModel_1 -0.88245610760574056 -WordPenalty 0.078303295087152405 -PhraseModel_0 -1.3044311246859424 -LanguageModel_OOV -0.13780000000000128 +LanguageModel_OOV -0.044300000000000235 +PassThrough -0.19300000000000087 +PhraseModel_6 -0.18180000000000701 +LanguageModel 1.3644969337716422 +PhraseModel_3 0.017250706134911725 +PhraseModel_4 -1.5473728273858063 +Glue -0.32289999999999447 +PhraseModel_1 -0.077697953502182365 +WordPenalty 0.27968564634568688 +PhraseModel_0 -0.19048660891012237 +PhraseModel_2 0.05889844333199834 diff --git a/training/dtrain/examples/parallelized/work/weights.1.1 b/training/dtrain/examples/parallelized/work/weights.1.1 index 02926c54..544ff462 100644 --- a/training/dtrain/examples/parallelized/work/weights.1.1 +++ b/training/dtrain/examples/parallelized/work/weights.1.1 @@ -1,12 +1,12 @@ -PassThrough -0.45404999999998186 -Glue -0.15574999999999967 -WordPenalty 0.14938644441267146 -LanguageModel 0.95914771145227362 -LanguageModel_OOV -0.42267499999998259 -PhraseModel_0 -0.4633667196239511 -PhraseModel_1 0.36681570131202201 -PhraseModel_2 0.7933894810149833 -PhraseModel_3 0.27497076611523918 -PhraseModel_4 -1.2038459762138427 -PhraseModel_5 0.061325000000000914 -PhraseModel_6 -0.11142500000000027 +PassThrough -0.42167499999999858 +Glue -0.26424999999999721 +WordPenalty 0.04788096662983269 +LanguageModel 0.78495517342352483 +LanguageModel_OOV -0.49307499999999477 +PhraseModel_0 -0.58703462849498356 +PhraseModel_1 -0.33425278954714266 +PhraseModel_2 0.20834221229630179 +PhraseModel_3 -0.043345645640208569 +PhraseModel_4 -0.60760531115816907 +PhraseModel_5 0.12300000000000186 +PhraseModel_6 -0.054150000000001031 diff --git a/training/dtrain/examples/parallelized/work/weights.1.2 b/training/dtrain/examples/parallelized/work/weights.1.2 index 79a104b3..ac3284b9 100644 --- a/training/dtrain/examples/parallelized/work/weights.1.2 +++ b/training/dtrain/examples/parallelized/work/weights.1.2 @@ -1,12 +1,12 @@ -PassThrough -0.53669999999998386 -Glue -0.23230000000000336 -WordPenalty 0.1150120361700277 -LanguageModel 0.76483587762340066 -LanguageModel_OOV -0.57494999999998042 -PhraseModel_0 -0.64110548780098009 -PhraseModel_1 0.44772095653729937 -PhraseModel_2 0.98529136452571298 -PhraseModel_3 0.022939428768845804 -PhraseModel_4 -1.1028511897295128 -PhraseModel_5 0.049100000000000636 -PhraseModel_6 -0.31499999999998796 +PassThrough -0.34762500000000224 +Glue -0.23607500000000026 +WordPenalty 0.10931192109504413 +LanguageModel 0.81339027211983694 +LanguageModel_OOV -0.33237500000000098 +PhraseModel_0 -0.53685104648974269 +PhraseModel_1 -0.049657790506137042 +PhraseModel_2 0.40277066454544108 +PhraseModel_3 0.14600791389785803 +PhraseModel_4 -0.72850673041349101 +PhraseModel_5 0.034750000000000433 +PhraseModel_6 -0.27192499999999448 diff --git a/training/dtrain/examples/parallelized/work/weights.2 b/training/dtrain/examples/parallelized/work/weights.2 index 9c7f5f2a..dedaf165 100644 --- a/training/dtrain/examples/parallelized/work/weights.2 +++ b/training/dtrain/examples/parallelized/work/weights.2 @@ -1,12 +1,12 @@ -PhraseModel_4 -1.0884784363200164 -LanguageModel 0.9863954661653327 -PhraseModel_2 0.8048100209655031 -PhraseModel_0 -0.7268058343336511 -LanguageModel_OOV -0.5387999999999846 -PassThrough -0.5005499999999877 -PhraseModel_1 0.16807904188863734 -PhraseModel_6 -0.2787499999999906 -Glue -0.2777249999999977 -WordPenalty 0.12918089364212418 -PhraseModel_3 0.03271485277712574 -PhraseModel_5 0.04010000000000038 +PhraseModel_2 0.6558266927225778 +PhraseModel_4 -0.6161090299356294 +LanguageModel 0.5690697644415413 +PhraseModel_1 0.32098232482479416 +PhraseModel_0 -0.39422813904895143 +PassThrough -0.37879999999999764 +LanguageModel_OOV -0.3620499999999963 +Glue -0.1792249999999967 +PhraseModel_6 -0.18769999999999526 +PhraseModel_3 -0.10321074877850786 +WordPenalty 0.05867318450512617 +PhraseModel_5 0.03392500000000041 diff --git a/training/dtrain/examples/parallelized/work/weights.2.0 b/training/dtrain/examples/parallelized/work/weights.2.0 index 7c7e097d..f7ece54d 100644 --- a/training/dtrain/examples/parallelized/work/weights.2.0 +++ b/training/dtrain/examples/parallelized/work/weights.2.0 @@ -1,12 +1,11 @@ -LanguageModel_OOV -0.16669999999999968 -PassThrough -0.030699999999999096 -PhraseModel_5 0.043500000000000219 -PhraseModel_6 -0.37429999999999497 -LanguageModel 1.1825232395261447 -PhraseModel_3 -0.40366624719458399 -PhraseModel_4 -1.3438482384390973 -Glue 0.12950000000000114 -PhraseModel_1 -0.044563165462829533 -WordPenalty 0.12781286602412198 -PhraseModel_0 -0.65166852874668157 -PhraseModel_2 0.49706380871834238 +LanguageModel_OOV -0.12280000000000209 +PassThrough -0.13350000000000165 +Glue -0.1259000000000001 +PhraseModel_1 0.24792740418949952 +WordPenalty 0.048293546387642321 +PhraseModel_0 0.26356693580129958 +PhraseModel_2 0.0063762787517740458 +PhraseModel_3 -0.18966358382769741 +PhraseModel_4 -0.22599681869670471 +PhraseModel_6 0.074299999999999047 +LanguageModel 0.3625416478537038 diff --git a/training/dtrain/examples/parallelized/work/weights.2.1 b/training/dtrain/examples/parallelized/work/weights.2.1 index 11714ec1..0946609d 100644 --- a/training/dtrain/examples/parallelized/work/weights.2.1 +++ b/training/dtrain/examples/parallelized/work/weights.2.1 @@ -1,12 +1,12 @@ -PassThrough -0.36384999999999734 -Glue -0.33455000000000329 -WordPenalty 0.10695587353072468 -LanguageModel 1.0621291481802193 -LanguageModel_OOV -0.46617499999999584 -PhraseModel_0 -0.63382056132769171 -PhraseModel_1 0.33225469649984996 -PhraseModel_2 0.85009991348010649 -PhraseModel_3 -0.29374143412758763 -PhraseModel_4 -1.0908181449386518 -PhraseModel_5 0.033425000000000114 -PhraseModel_6 -0.38922499999998272 +PassThrough -0.23927500000000015 +Glue -0.10384999999999919 +WordPenalty 0.038717353061671053 +LanguageModel 0.49412782572695274 +LanguageModel_OOV -0.24887499999999915 +PhraseModel_0 -0.32101572713801541 +PhraseModel_1 0.34413149733472631 +PhraseModel_2 0.62365535622061474 +PhraseModel_3 -0.49337445280658987 +PhraseModel_4 -0.77004673375347765 +PhraseModel_5 0.0069999999999999767 +PhraseModel_6 -0.05055000000000108 diff --git a/training/dtrain/examples/parallelized/work/weights.2.2 b/training/dtrain/examples/parallelized/work/weights.2.2 index 4651c771..b766fc75 100644 --- a/training/dtrain/examples/parallelized/work/weights.2.2 +++ b/training/dtrain/examples/parallelized/work/weights.2.2 @@ -1,12 +1,12 @@ -PassThrough -0.44899999999999302 -Glue -0.34639999999999227 -WordPenalty 0.18736549685511736 -LanguageModel 1.579413019617276 -LanguageModel_OOV -0.48724999999999041 -PhraseModel_0 -1.0014593871340565 -PhraseModel_1 -0.5173431118302918 -PhraseModel_2 0.40485682070199475 -PhraseModel_3 -0.013031148291449997 -PhraseModel_4 -1.1546267627331184 -PhraseModel_5 0.037100000000000292 -PhraseModel_6 -0.18919999999999634 +PassThrough -0.46082499999999499 +Glue -0.32907499999998979 +WordPenalty 0.049596429833348527 +LanguageModel 0.33496341201347335 +LanguageModel_OOV -0.44357499999999361 +PhraseModel_0 -0.30679883980783829 +PhraseModel_1 0.5937585900939707 +PhraseModel_2 0.86415970329021152 +PhraseModel_3 -0.21072279838022553 +PhraseModel_4 -0.65734339854224544 +PhraseModel_5 0.034750000000000433 +PhraseModel_6 -0.10652500000000011 diff --git a/training/dtrain/examples/parallelized/work/weights.3.0 b/training/dtrain/examples/parallelized/work/weights.3.0 index 37bd01a2..403ffbb3 100644 --- a/training/dtrain/examples/parallelized/work/weights.3.0 +++ b/training/dtrain/examples/parallelized/work/weights.3.0 @@ -1,12 +1,12 @@ -LanguageModel_OOV -0.036999999999999908 -PassThrough -0.11100000000000057 -Glue -0.11000000000000044 -PhraseModel_1 0.11602125567215119 -WordPenalty 0.2197530078430466 -PhraseModel_0 -0.34702159865156773 -PhraseModel_2 0.39510081490798676 -PhraseModel_3 0.37857253195640361 -PhraseModel_4 -1.0318920208766025 -PhraseModel_5 0.042000000000000176 -PhraseModel_6 -0.25299999999999973 -LanguageModel 1.7396888110339634 +PhraseModel_4 -1.501862388574505 +PhraseModel_3 -0.77386695951256013 +PhraseModel_6 -0.51399999999999824 +PhraseModel_5 0.02399999999999991 +LanguageModel 1.1666837562322641 +PhraseModel_2 0.036204776972598059 +PassThrough 0.030999999999999975 +Glue 0.046000000000000582 +PhraseModel_1 -0.98829728889588764 +WordPenalty 0.1732834982793964 +PhraseModel_0 -1.1693779885763822 +LanguageModel_OOV 0.066000000000000086 diff --git a/training/dtrain/examples/parallelized/work/weights.3.1 b/training/dtrain/examples/parallelized/work/weights.3.1 index 21096c45..c171d586 100644 --- a/training/dtrain/examples/parallelized/work/weights.3.1 +++ b/training/dtrain/examples/parallelized/work/weights.3.1 @@ -1,12 +1,12 @@ -PassThrough -0.43805000000000188 -Glue -0.63234999999999786 -WordPenalty 0.10760731525357638 -LanguageModel 1.4702716690884872 -LanguageModel_OOV -0.45547500000000124 -PhraseModel_0 -0.34857674662928467 -PhraseModel_1 0.050651304056615561 -PhraseModel_2 0.32136542081299119 -PhraseModel_3 0.31848359353717243 -PhraseModel_4 -0.96701840673014472 -PhraseModel_5 0.026825000000000078 -PhraseModel_6 -0.30802499999999322 +PassThrough -0.18247500000000313 +Glue -0.084749999999998368 +WordPenalty 0.11150510822865688 +LanguageModel 1.063497816773886 +LanguageModel_OOV -0.1146750000000015 +PhraseModel_0 -0.062922130123762257 +PhraseModel_1 0.0035552404454581212 +PhraseModel_2 0.039691524494244249 +PhraseModel_3 0.080265456972269417 +PhraseModel_4 -0.57787128729945014 +PhraseModel_5 0.017399999999999922 +PhraseModel_6 -0.17095000000000066 diff --git a/training/dtrain/examples/parallelized/work/weights.3.2 b/training/dtrain/examples/parallelized/work/weights.3.2 index 7593e794..3ff0411d 100644 --- a/training/dtrain/examples/parallelized/work/weights.3.2 +++ b/training/dtrain/examples/parallelized/work/weights.3.2 @@ -1,12 +1,12 @@ -PassThrough -0.53339999999998544 -Glue -0.25809999999999805 -WordPenalty 0.091646993043633926 -LanguageModel 0.77536637609898384 -LanguageModel_OOV -0.57164999999998134 -PhraseModel_0 -0.57939946953906185 -PhraseModel_1 0.46928686232236927 -PhraseModel_2 0.95470739190358411 -PhraseModel_3 0.12107346689753942 -PhraseModel_4 -1.0052552276969096 -PhraseModel_5 0.037100000000000292 -PhraseModel_6 -0.32529999999998682 +PassThrough -0.32552500000000006 +Glue -0.13247499999999815 +WordPenalty 0.053591939066858545 +LanguageModel 0.72104728811924446 +LanguageModel_OOV -0.30827499999999869 +PhraseModel_0 -0.37052837676792744 +PhraseModel_1 0.17551097460105014 +PhraseModel_2 0.49999630285778179 +PhraseModel_3 -0.14590465814428336 +PhraseModel_4 -0.59563132644367889 +PhraseModel_5 0.034750000000000433 +PhraseModel_6 -0.11142500000000025 |