diff options
36 files changed, 844 insertions, 218 deletions
diff --git a/training/dtrain/examples/parallelized/cdec.ini b/training/dtrain/examples/parallelized/cdec.ini index 5773029a..733b1653 100644 --- a/training/dtrain/examples/parallelized/cdec.ini +++ b/training/dtrain/examples/parallelized/cdec.ini @@ -4,7 +4,7 @@ intersection_strategy=cube_pruning cubepruning_pop_limit=200 scfg_max_span_limit=15 feature_function=WordPenalty -feature_function=KLanguageModel ../standard//nc-wmt11.en.srilm.gz +feature_function=KLanguageModel ../standard/nc-wmt11.en.srilm.gz #feature_function=ArityPenalty #feature_function=CMR2008ReorderingFeatures #feature_function=Dwarf diff --git a/training/dtrain/examples/parallelized/in b/training/dtrain/examples/parallelized/in index 51d01fe7..82555908 100644 --- a/training/dtrain/examples/parallelized/in +++ b/training/dtrain/examples/parallelized/in @@ -1,10 +1,10 @@ -<seg grammar="grammar/grammar.out.0.gz" id="0">europas nach rassen geteiltes haus</seg> -<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> -<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> -<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> -<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> -<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> -<seg grammar="grammar/grammar.out.6.gz" id="6">das aber wird es nicht , wie die geschichte des rassismus in amerika deutlich zeigt .</seg> -<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> -<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> -<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> +<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.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.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.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.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.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.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.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.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.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 . diff --git a/training/dtrain/examples/parallelized/refs b/training/dtrain/examples/parallelized/refs deleted file mode 100644 index 632e27b0..00000000 --- a/training/dtrain/examples/parallelized/refs +++ /dev/null @@ -1,10 +0,0 @@ -europe 's divided racial house -a common feature of europe 's extreme right is its racism and use of the immigration issue as a political wedge . -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 . -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 . -an aging population at home and ever more open borders imply increasing racial fragmentation in european countries . -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 . -it will not , as america 's racial history clearly shows . -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 . -the first step to address racial politics is to understand the origin and consequences of racial animosity , even if it means uncovering unpleasant truths . -this is precisely what a large amount of research in economics , sociology , psychology and political science has done for the us . diff --git a/training/dtrain/examples/parallelized/work/out.0.0 b/training/dtrain/examples/parallelized/work/out.0.0 index c559dd4d..f394a9b0 100644 --- a/training/dtrain/examples/parallelized/work/out.0.0 +++ b/training/dtrain/examples/parallelized/work/out.0.0 @@ -1,15 +1,16 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../standard//nc-wmt11.en.srilm.gz +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 405292278 +Seeding random number sequence to 2577966319 dtrain Parameters: k 100 N 4 T 1 + batch 0 scorer 'stupid_bleu' sample from 'kbest' filter 'uniq' @@ -22,41 +23,43 @@ Parameters: pair threshold 0 select weights 'last' l1 reg 0 'none' + pclr no max pairs 4294967295 + repeat 1 cdec cfg 'cdec.ini' - input 'work/shard.0.0.in' - refs 'work/shard.0.0.refs' + input '' output 'work/weights.0.0' (a dot represents 10 inputs) Iteration #1 of 1. - 5 + 3 WEIGHTS - Glue = +0.2663 - WordPenalty = -0.0079042 - LanguageModel = +0.44782 - LanguageModel_OOV = -0.0401 - PhraseModel_0 = -0.193 - PhraseModel_1 = +0.71321 - PhraseModel_2 = +0.85196 - PhraseModel_3 = -0.43986 - PhraseModel_4 = -0.44803 - PhraseModel_5 = -0.0538 - PhraseModel_6 = -0.1788 - PassThrough = -0.1477 + 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 --- - 1best avg score: 0.17521 (+0.17521) - 1best avg model score: 21.556 (+21.556) - avg # pairs: 1671.2 - avg # rank err: 1118.6 - avg # margin viol: 552.6 - non0 feature count: 12 + 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 + k-best loss imp: 100% + non0 feature count: 11 avg list sz: 100 - avg f count: 11.32 -(time 0.35 min, 4.2 s/S) + avg f count: 10.6 +(time 0.23 min, 4.7 s/S) Writing weights file to 'work/weights.0.0' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.17521]. -This took 0.35 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.080513]. +This took 0.23333 min. diff --git a/training/dtrain/examples/parallelized/work/out.0.1 b/training/dtrain/examples/parallelized/work/out.0.1 index 8bc7ea9c..d0819a5a 100644 --- a/training/dtrain/examples/parallelized/work/out.0.1 +++ b/training/dtrain/examples/parallelized/work/out.0.1 @@ -1,15 +1,16 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../standard//nc-wmt11.en.srilm.gz +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 43859692 +Seeding random number sequence to 3555678516 dtrain Parameters: k 100 N 4 T 1 + batch 0 scorer 'stupid_bleu' sample from 'kbest' filter 'uniq' @@ -22,42 +23,44 @@ Parameters: pair threshold 0 select weights 'last' l1 reg 0 'none' + pclr no max pairs 4294967295 + repeat 1 cdec cfg 'cdec.ini' - input 'work/shard.0.0.in' - refs 'work/shard.0.0.refs' + input '' output 'work/weights.0.1' weights in 'work/weights.0' (a dot represents 10 inputs) Iteration #1 of 1. - 5 + 3 WEIGHTS - Glue = -0.2699 - WordPenalty = +0.080605 - LanguageModel = -0.026572 - LanguageModel_OOV = -0.30025 - PhraseModel_0 = -0.32076 - PhraseModel_1 = +0.67451 - PhraseModel_2 = +0.92 - PhraseModel_3 = -0.36402 - PhraseModel_4 = -0.592 - PhraseModel_5 = -0.0269 - PhraseModel_6 = -0.28755 - PassThrough = -0.33285 + 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 --- - 1best avg score: 0.26638 (+0.26638) - 1best avg model score: 53.197 (+53.197) - avg # pairs: 2028.6 - avg # rank err: 998.2 - avg # margin viol: 918.8 + 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 + k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 10.496 -(time 0.35 min, 4.2 s/S) + avg f count: 12 +(time 0.27 min, 5.3 s/S) Writing weights file to 'work/weights.0.1' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.26638]. -This took 0.35 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.12642]. +This took 0.26667 min. diff --git a/training/dtrain/examples/parallelized/work/out.0.2 b/training/dtrain/examples/parallelized/work/out.0.2 new file mode 100644 index 00000000..62bf8bb9 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.0.2 @@ -0,0 +1,66 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.0.2' + weights in 'work/weights.1' +(a dot represents 10 inputs) +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 + --- + 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 + 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) + +Writing weights file to 'work/weights.0.2' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.12697]. +This took 0.28333 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.0 b/training/dtrain/examples/parallelized/work/out.1.0 index 65d1e7dc..cc35e676 100644 --- a/training/dtrain/examples/parallelized/work/out.1.0 +++ b/training/dtrain/examples/parallelized/work/out.1.0 @@ -1,15 +1,16 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../standard//nc-wmt11.en.srilm.gz +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 4126799437 +Seeding random number sequence to 1336015864 dtrain Parameters: k 100 N 4 T 1 + batch 0 scorer 'stupid_bleu' sample from 'kbest' filter 'uniq' @@ -22,41 +23,43 @@ Parameters: pair threshold 0 select weights 'last' l1 reg 0 'none' + pclr no max pairs 4294967295 + repeat 1 cdec cfg 'cdec.ini' - input 'work/shard.1.0.in' - refs 'work/shard.1.0.refs' + input '' output 'work/weights.1.0' (a dot represents 10 inputs) Iteration #1 of 1. - 5 + 3 WEIGHTS - Glue = -0.3815 - WordPenalty = +0.20064 - LanguageModel = +0.95304 - LanguageModel_OOV = -0.264 - PhraseModel_0 = -0.22362 - PhraseModel_1 = +0.12254 - PhraseModel_2 = +0.26328 - PhraseModel_3 = +0.38018 - PhraseModel_4 = -0.48654 - PhraseModel_5 = +0 - PhraseModel_6 = -0.3645 - PassThrough = -0.2216 + 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 --- - 1best avg score: 0.10863 (+0.10863) - 1best avg model score: -4.9841 (-4.9841) - avg # pairs: 1345.4 - avg # rank err: 822.4 - avg # margin viol: 501 - non0 feature count: 11 + 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 + k-best loss imp: 100% + non0 feature count: 12 avg list sz: 100 - avg f count: 11.814 -(time 0.43 min, 5.2 s/S) + avg f count: 11.883 +(time 0.42 min, 8.3 s/S) Writing weights file to 'work/weights.1.0' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.10863]. -This took 0.43333 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.062351]. +This took 0.41667 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.1 b/training/dtrain/examples/parallelized/work/out.1.1 index f479fbbc..3d7a7e66 100644 --- a/training/dtrain/examples/parallelized/work/out.1.1 +++ b/training/dtrain/examples/parallelized/work/out.1.1 @@ -1,15 +1,16 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../standard//nc-wmt11.en.srilm.gz +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 2112412848 +Seeding random number sequence to 1673913538 dtrain Parameters: k 100 N 4 T 1 + batch 0 scorer 'stupid_bleu' sample from 'kbest' filter 'uniq' @@ -22,42 +23,44 @@ Parameters: pair threshold 0 select weights 'last' l1 reg 0 'none' + pclr no max pairs 4294967295 + repeat 1 cdec cfg 'cdec.ini' - input 'work/shard.1.0.in' - refs 'work/shard.1.0.refs' + input '' output 'work/weights.1.1' weights in 'work/weights.0' (a dot represents 10 inputs) Iteration #1 of 1. - 5 + 3 WEIGHTS - Glue = -0.3178 - WordPenalty = +0.11092 - LanguageModel = +0.17269 - LanguageModel_OOV = -0.13485 - PhraseModel_0 = -0.45371 - PhraseModel_1 = +0.38789 - PhraseModel_2 = +0.75311 - PhraseModel_3 = -0.38163 - PhraseModel_4 = -0.58817 - PhraseModel_5 = -0.0269 - PhraseModel_6 = -0.27315 - PassThrough = -0.16745 + 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 --- - 1best avg score: 0.13169 (+0.13169) - 1best avg model score: 24.226 (+24.226) - avg # pairs: 1951.2 - avg # rank err: 985.4 - avg # margin viol: 951 + 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 + k-best loss imp: 100% non0 feature count: 12 avg list sz: 100 - avg f count: 11.224 -(time 0.45 min, 5.4 s/S) + avg f count: 11.973 +(time 0.42 min, 8.3 s/S) Writing weights file to 'work/weights.1.1' ... done --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.13169]. -This took 0.45 min. +Best iteration: 1 [SCORE 'stupid_bleu'=0.057772]. +This took 0.41667 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.2 b/training/dtrain/examples/parallelized/work/out.1.2 new file mode 100644 index 00000000..ba603651 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.1.2 @@ -0,0 +1,66 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.1.2' + weights in 'work/weights.1' +(a dot represents 10 inputs) +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 + --- + 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 + 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) + +Writing weights file to 'work/weights.1.2' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.24871]. +This took 0.43333 min. diff --git a/training/dtrain/examples/parallelized/work/out.2.0 b/training/dtrain/examples/parallelized/work/out.2.0 new file mode 100644 index 00000000..ab38c637 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.2.0 @@ -0,0 +1,65 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.2.0' +(a dot represents 10 inputs) +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 + --- + 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 + k-best loss imp: 100% + non0 feature count: 12 + avg list sz: 100 + avg f count: 11.487 +(time 0.22 min, 4.3 s/S) + +Writing weights file to 'work/weights.2.0' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.08637]. +This took 0.21667 min. diff --git a/training/dtrain/examples/parallelized/work/out.2.1 b/training/dtrain/examples/parallelized/work/out.2.1 new file mode 100644 index 00000000..f86ec520 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.2.1 @@ -0,0 +1,66 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.2.1' + weights in 'work/weights.0' +(a dot represents 10 inputs) +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 + --- + 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 + 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) + +Writing weights file to 'work/weights.2.1' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.12089]. +This took 0.21667 min. diff --git a/training/dtrain/examples/parallelized/work/out.2.2 b/training/dtrain/examples/parallelized/work/out.2.2 new file mode 100644 index 00000000..823129c0 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.2.2 @@ -0,0 +1,66 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.2.2' + weights in 'work/weights.1' +(a dot represents 10 inputs) +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 + --- + 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 + 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) + +Writing weights file to 'work/weights.2.2' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.17557]. +This took 0.23333 min. diff --git a/training/dtrain/examples/parallelized/work/out.3.0 b/training/dtrain/examples/parallelized/work/out.3.0 new file mode 100644 index 00000000..2d8dea27 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.3.0 @@ -0,0 +1,65 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.3.0' +(a dot represents 10 inputs) +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 + --- + 1best avg score: 0.034204 (+0.034204) + 1best avg model score: 0 (+0) + avg # pairs: 900 + avg # rank err: 900 + avg # margin viol: 0 + 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) + +Writing weights file to 'work/weights.3.0' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.034204]. +This took 0.11667 min. diff --git a/training/dtrain/examples/parallelized/work/out.3.1 b/training/dtrain/examples/parallelized/work/out.3.1 new file mode 100644 index 00000000..a1eeb64b --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.3.1 @@ -0,0 +1,66 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.3.1' + weights in 'work/weights.0' +(a dot represents 10 inputs) +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 + --- + 1best avg score: 0.078383 (+0.078383) + 1best avg model score: -68.182 (-68.182) + avg # pairs: 1411 + avg # rank err: 599 + avg # margin viol: 801 + k-best loss imp: 100% + non0 feature count: 12 + avg list sz: 100 + avg f count: 12 +(time 0.12 min, 7 s/S) + +Writing weights file to 'work/weights.3.1' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.078383]. +This took 0.11667 min. diff --git a/training/dtrain/examples/parallelized/work/out.3.2 b/training/dtrain/examples/parallelized/work/out.3.2 new file mode 100644 index 00000000..a0c0e509 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.3.2 @@ -0,0 +1,66 @@ + cdec cfg 'cdec.ini' +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 + +dtrain +Parameters: + k 100 + N 4 + T 1 + batch 0 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 1 + faster perceptron 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'last' + l1 reg 0 'none' + pclr no + max pairs 4294967295 + repeat 1 + cdec cfg 'cdec.ini' + input '' + output 'work/weights.3.2' + weights in 'work/weights.1' +(a dot represents 10 inputs) +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 + --- + 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 + k-best loss imp: 100% + non0 feature count: 12 + avg list sz: 100 + avg f count: 12 +(time 0.12 min, 7 s/S) + +Writing weights file to 'work/weights.3.2' ... +done + +--- +Best iteration: 1 [SCORE 'stupid_bleu'=0.10945]. +This took 0.11667 min. diff --git a/training/dtrain/examples/parallelized/work/shard.0.0.in b/training/dtrain/examples/parallelized/work/shard.0.0.in index 92f9c78e..fb8c2cd6 100644 --- a/training/dtrain/examples/parallelized/work/shard.0.0.in +++ b/training/dtrain/examples/parallelized/work/shard.0.0.in @@ -1,5 +1,3 @@ -<seg grammar="grammar/grammar.out.0.gz" id="0">europas nach rassen geteiltes haus</seg> -<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> -<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> -<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> -<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> +<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.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 . diff --git a/training/dtrain/examples/parallelized/work/shard.0.0.refs b/training/dtrain/examples/parallelized/work/shard.0.0.refs deleted file mode 100644 index bef68fee..00000000 --- a/training/dtrain/examples/parallelized/work/shard.0.0.refs +++ /dev/null @@ -1,5 +0,0 @@ -europe 's divided racial house -a common feature of europe 's extreme right is its racism and use of the immigration issue as a political wedge . -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 . -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 . -an aging population at home and ever more open borders imply increasing racial fragmentation in european countries . diff --git a/training/dtrain/examples/parallelized/work/shard.1.0.in b/training/dtrain/examples/parallelized/work/shard.1.0.in index b7695ce7..c28d1502 100644 --- a/training/dtrain/examples/parallelized/work/shard.1.0.in +++ b/training/dtrain/examples/parallelized/work/shard.1.0.in @@ -1,5 +1,3 @@ -<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> -<seg grammar="grammar/grammar.out.6.gz" id="6">das aber wird es nicht , wie die geschichte des rassismus in amerika deutlich zeigt .</seg> -<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> -<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> -<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> +<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 . diff --git a/training/dtrain/examples/parallelized/work/shard.1.0.refs b/training/dtrain/examples/parallelized/work/shard.1.0.refs deleted file mode 100644 index 6076f6d5..00000000 --- a/training/dtrain/examples/parallelized/work/shard.1.0.refs +++ /dev/null @@ -1,5 +0,0 @@ -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 . -it will not , as america 's racial history clearly shows . -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 . -the first step to address racial politics is to understand the origin and consequences of racial animosity , even if it means uncovering unpleasant truths . -this is precisely what a large amount of research in economics , sociology , psychology and political science has done for the us . diff --git a/training/dtrain/examples/parallelized/work/shard.2.0.in b/training/dtrain/examples/parallelized/work/shard.2.0.in new file mode 100644 index 00000000..85f68e20 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/shard.2.0.in @@ -0,0 +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 . diff --git a/training/dtrain/examples/parallelized/work/shard.3.0.in b/training/dtrain/examples/parallelized/work/shard.3.0.in new file mode 100644 index 00000000..f7cbb3e3 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/shard.3.0.in @@ -0,0 +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 . diff --git a/training/dtrain/examples/parallelized/work/weights.0 b/training/dtrain/examples/parallelized/work/weights.0 index ddd595a8..aa494afb 100644 --- a/training/dtrain/examples/parallelized/work/weights.0 +++ b/training/dtrain/examples/parallelized/work/weights.0 @@ -1,12 +1,12 @@ -LanguageModel 0.7004298992212881 -PhraseModel_2 0.5576194336478857 -PhraseModel_1 0.41787318415343155 -PhraseModel_4 -0.46728502545635164 -PhraseModel_3 -0.029839521598455515 -Glue -0.05760000000000068 -PhraseModel_6 -0.2716499999999978 -PhraseModel_0 -0.20831031065605327 -LanguageModel_OOV -0.15205000000000077 -PassThrough -0.1846500000000006 -WordPenalty 0.09636994553433414 -PhraseModel_5 -0.026900000000000257 +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 diff --git a/training/dtrain/examples/parallelized/work/weights.0.0 b/training/dtrain/examples/parallelized/work/weights.0.0 index c9370b18..541321af 100644 --- a/training/dtrain/examples/parallelized/work/weights.0.0 +++ b/training/dtrain/examples/parallelized/work/weights.0.0 @@ -1,12 +1,11 @@ -WordPenalty -0.0079041595706392243 -LanguageModel 0.44781580828279532 -LanguageModel_OOV -0.04010000000000042 -Glue 0.26629999999999948 -PhraseModel_0 -0.19299677809125185 -PhraseModel_1 0.71321026861732773 -PhraseModel_2 0.85195540993310537 -PhraseModel_3 -0.43986310822842656 -PhraseModel_4 -0.44802855630415955 -PhraseModel_5 -0.053800000000000514 -PhraseModel_6 -0.17879999999999835 -PassThrough -0.14770000000000036 +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 diff --git a/training/dtrain/examples/parallelized/work/weights.0.1 b/training/dtrain/examples/parallelized/work/weights.0.1 index 8fad3de8..c983747e 100644 --- a/training/dtrain/examples/parallelized/work/weights.0.1 +++ b/training/dtrain/examples/parallelized/work/weights.0.1 @@ -1,12 +1,12 @@ -WordPenalty 0.080605055841244472 -LanguageModel -0.026571720531022844 -LanguageModel_OOV -0.30024999999999141 -Glue -0.26989999999999842 -PhraseModel_2 0.92000295209089566 -PhraseModel_1 0.67450748692470841 -PhraseModel_4 -0.5920000014976784 -PhraseModel_3 -0.36402437203127397 -PhraseModel_6 -0.28754999999999603 -PhraseModel_0 -0.32076244202907672 -PassThrough 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