diff options
Diffstat (limited to 'training/dtrain/examples')
49 files changed, 711 insertions, 514 deletions
diff --git a/training/dtrain/examples/parallelized/README b/training/dtrain/examples/parallelized/README index 89715105..c4addd81 100644 --- a/training/dtrain/examples/parallelized/README +++ b/training/dtrain/examples/parallelized/README @@ -1,5 +1,5 @@ run for example - ../../parallelize.rb ./dtrain.ini 4 false 2 2 ./in ./refs + ../../parallelize.rb -c dtrain.ini -s 4 -e 3 -d ../../dtrain -p 2 -i in -final weights will be in the file work/weights.3 +final weights will be in the file work/weights.2 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/dtrain.ini b/training/dtrain/examples/parallelized/dtrain.ini index 0b0932d6..9fc205a3 100644 --- a/training/dtrain/examples/parallelized/dtrain.ini +++ b/training/dtrain/examples/parallelized/dtrain.ini @@ -1,14 +1,7 @@ k=100 N=4 learning_rate=0.0001 -gamma=0 -loss_margin=1.0 -epochs=1 -scorer=stupid_bleu -sample_from=kbest -filter=uniq -pair_sampling=XYX -hi_lo=0.1 -select_weights=last -print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough +error_margin=1.0 +iterations=1 decoder_config=cdec.ini +print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough 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..77749404 100644 --- a/training/dtrain/examples/parallelized/work/out.0.0 +++ b/training/dtrain/examples/parallelized/work/out.0.0 @@ -1,62 +1,43 @@ - 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 - dtrain Parameters: k 100 N 4 T 1 - 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' - max pairs 4294967295 - cdec cfg 'cdec.ini' + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' input 'work/shard.0.0.in' - refs 'work/shard.0.0.refs' output 'work/weights.0.0' -(a dot represents 10 inputs) +(a dot per input) 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 = +0.3404 + WordPenalty = -0.017632 + LanguageModel = +0.72958 + LanguageModel_OOV = -0.235 + PhraseModel_0 = -0.43721 + PhraseModel_1 = +1.01 + PhraseModel_2 = +1.3525 + PhraseModel_3 = -0.25541 + PhraseModel_4 = -0.78115 + PhraseModel_5 = +0 + PhraseModel_6 = -0.3681 + PassThrough = -0.3304 --- - 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: 0.19474 (+0.19474) + 1best avg model score: 0.52232 + avg # pairs: 2513 + non-0 feature count: 11 avg list sz: 100 - avg f count: 11.32 -(time 0.35 min, 4.2 s/S) - -Writing weights file to 'work/weights.0.0' ... -done + avg f count: 11.42 +(time 0.32 min, 6 s/S) --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.17521]. -This took 0.35 min. +Best iteration: 1 [GOLD = 0.19474]. +This took 0.31667 min. diff --git a/training/dtrain/examples/parallelized/work/out.0.1 b/training/dtrain/examples/parallelized/work/out.0.1 index 8bc7ea9c..d0dee623 100644 --- a/training/dtrain/examples/parallelized/work/out.0.1 +++ b/training/dtrain/examples/parallelized/work/out.0.1 @@ -1,63 +1,44 @@ - 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 - dtrain Parameters: k 100 N 4 T 1 - 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' - max pairs 4294967295 - cdec cfg 'cdec.ini' + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' input 'work/shard.0.0.in' - refs 'work/shard.0.0.refs' output 'work/weights.0.1' weights in 'work/weights.0' -(a dot represents 10 inputs) +(a dot per input) 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 = -0.40908 + WordPenalty = +0.12967 + LanguageModel = +0.39892 + LanguageModel_OOV = -0.6314 + PhraseModel_0 = -0.63992 + PhraseModel_1 = +0.74198 + PhraseModel_2 = +1.3096 + PhraseModel_3 = -0.1216 + PhraseModel_4 = -1.2274 + PhraseModel_5 = +0.02435 + PhraseModel_6 = -0.21093 + PassThrough = -0.66155 --- - 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 - non0 feature count: 12 + 1best avg score: 0.15735 (+0.15735) + 1best avg model score: 46.831 + avg # pairs: 2132.3 + non-0 feature count: 12 avg list sz: 100 - avg f count: 10.496 -(time 0.35 min, 4.2 s/S) - -Writing weights file to 'work/weights.0.1' ... -done + avg f count: 10.64 +(time 0.38 min, 7 s/S) --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.26638]. -This took 0.35 min. +Best iteration: 1 [GOLD = 0.15735]. +This took 0.38333 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..9c4b110b --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.0.2 @@ -0,0 +1,44 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.0.0.in' + output 'work/weights.0.2' + weights in 'work/weights.1' +(a dot per input) +Iteration #1 of 1. + .... 3 +WEIGHTS + Glue = -0.44422 + WordPenalty = +0.1032 + LanguageModel = +0.66474 + LanguageModel_OOV = -0.62252 + PhraseModel_0 = -0.59993 + PhraseModel_1 = +0.78992 + PhraseModel_2 = +1.3149 + PhraseModel_3 = +0.21434 + PhraseModel_4 = -1.0174 + PhraseModel_5 = +0.02435 + PhraseModel_6 = -0.18452 + PassThrough = -0.65268 + --- + 1best avg score: 0.24722 (+0.24722) + 1best avg model score: 61.971 + avg # pairs: 2017.7 + non-0 feature count: 12 + avg list sz: 100 + avg f count: 10.42 +(time 0.3 min, 6 s/S) + +--- +Best iteration: 1 [GOLD = 0.24722]. +This took 0.3 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.0 b/training/dtrain/examples/parallelized/work/out.1.0 index 65d1e7dc..3dc4dca6 100644 --- a/training/dtrain/examples/parallelized/work/out.1.0 +++ b/training/dtrain/examples/parallelized/work/out.1.0 @@ -1,62 +1,43 @@ - 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 - dtrain Parameters: k 100 N 4 T 1 - 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' - max pairs 4294967295 - cdec cfg 'cdec.ini' + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' input 'work/shard.1.0.in' - refs 'work/shard.1.0.refs' output 'work/weights.1.0' -(a dot represents 10 inputs) +(a dot per input) 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 = -0.2722 + WordPenalty = +0.05433 + LanguageModel = +0.69948 + LanguageModel_OOV = -0.2641 + PhraseModel_0 = -1.4208 + PhraseModel_1 = -1.563 + PhraseModel_2 = -0.21051 + PhraseModel_3 = -0.17764 + PhraseModel_4 = -1.6583 + PhraseModel_5 = +0.0794 + PhraseModel_6 = +0.1528 + PassThrough = -0.2367 --- - 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: 0.071329 (+0.071329) + 1best avg model score: -41.362 + avg # pairs: 1862.3 + non-0 feature count: 12 avg list sz: 100 - avg f count: 11.814 -(time 0.43 min, 5.2 s/S) - -Writing weights file to 'work/weights.1.0' ... -done + avg f count: 11.847 +(time 0.28 min, 5 s/S) --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.10863]. -This took 0.43333 min. +Best iteration: 1 [GOLD = 0.071329]. +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 f479fbbc..79ac35dc 100644 --- a/training/dtrain/examples/parallelized/work/out.1.1 +++ b/training/dtrain/examples/parallelized/work/out.1.1 @@ -1,63 +1,44 @@ - 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 - dtrain Parameters: k 100 N 4 T 1 - 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' - max pairs 4294967295 - cdec cfg 'cdec.ini' + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' input 'work/shard.1.0.in' - refs 'work/shard.1.0.refs' output 'work/weights.1.1' weights in 'work/weights.0' -(a dot represents 10 inputs) +(a dot per input) 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 = -0.20488 + WordPenalty = -0.0091745 + LanguageModel = +0.79433 + LanguageModel_OOV = -0.4309 + PhraseModel_0 = -0.56242 + PhraseModel_1 = +0.85363 + PhraseModel_2 = +1.3458 + PhraseModel_3 = -0.13095 + PhraseModel_4 = -0.94762 + PhraseModel_5 = +0.02435 + PhraseModel_6 = -0.16003 + PassThrough = -0.46105 --- - 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 - non0 feature count: 12 + 1best avg score: 0.13017 (+0.13017) + 1best avg model score: 14.53 + avg # pairs: 1968 + non-0 feature count: 12 avg list sz: 100 - avg f count: 11.224 -(time 0.45 min, 5.4 s/S) - -Writing weights file to 'work/weights.1.1' ... -done + avg f count: 11 +(time 0.33 min, 6 s/S) --- -Best iteration: 1 [SCORE 'stupid_bleu'=0.13169]. -This took 0.45 min. +Best iteration: 1 [GOLD = 0.13017]. +This took 0.33333 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..8c4f8b03 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.1.2 @@ -0,0 +1,44 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.1.0.in' + output 'work/weights.1.2' + weights in 'work/weights.1' +(a dot per input) +Iteration #1 of 1. + .... 3 +WEIGHTS + Glue = -0.49853 + WordPenalty = +0.07636 + LanguageModel = +1.3183 + LanguageModel_OOV = -0.60902 + PhraseModel_0 = -0.22481 + PhraseModel_1 = +0.86369 + PhraseModel_2 = +1.0747 + PhraseModel_3 = +0.18002 + PhraseModel_4 = -0.84661 + PhraseModel_5 = +0.02435 + PhraseModel_6 = +0.11247 + PassThrough = -0.63918 + --- + 1best avg score: 0.15478 (+0.15478) + 1best avg model score: -7.2154 + avg # pairs: 1776 + non-0 feature count: 12 + avg list sz: 100 + avg f count: 11.327 +(time 0.27 min, 5 s/S) + +--- +Best iteration: 1 [GOLD = 0.15478]. +This took 0.26667 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..07c85963 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.2.0 @@ -0,0 +1,43 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.2.0.in' + output 'work/weights.2.0' +(a dot per input) +Iteration #1 of 1. + .... 3 +WEIGHTS + Glue = -0.2109 + WordPenalty = +0.14922 + LanguageModel = +0.79686 + LanguageModel_OOV = -0.6627 + PhraseModel_0 = +0.37999 + PhraseModel_1 = +0.69213 + PhraseModel_2 = +0.3422 + PhraseModel_3 = +1.1426 + PhraseModel_4 = -0.55413 + PhraseModel_5 = +0 + PhraseModel_6 = +0.0676 + PassThrough = -0.6343 + --- + 1best avg score: 0.072374 (+0.072374) + 1best avg model score: -27.384 + avg # pairs: 2582 + non-0 feature count: 11 + avg list sz: 100 + avg f count: 11.54 +(time 0.32 min, 6 s/S) + +--- +Best iteration: 1 [GOLD = 0.072374]. +This took 0.31667 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..c54bb1b1 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.2.1 @@ -0,0 +1,44 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.2.0.in' + output 'work/weights.2.1' + weights in 'work/weights.0' +(a dot per input) +Iteration #1 of 1. + .... 3 +WEIGHTS + Glue = -0.76608 + WordPenalty = +0.15938 + LanguageModel = +1.5897 + LanguageModel_OOV = -0.521 + PhraseModel_0 = -0.58348 + PhraseModel_1 = +0.29828 + PhraseModel_2 = +0.78493 + PhraseModel_3 = +0.083222 + PhraseModel_4 = -0.93843 + PhraseModel_5 = +0.02435 + PhraseModel_6 = -0.27382 + PassThrough = -0.55115 + --- + 1best avg score: 0.12881 (+0.12881) + 1best avg model score: -9.6731 + avg # pairs: 2020.3 + non-0 feature count: 12 + avg list sz: 100 + avg f count: 12 +(time 0.32 min, 6 s/S) + +--- +Best iteration: 1 [GOLD = 0.12881]. +This took 0.31667 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..f5d6229f --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.2.2 @@ -0,0 +1,44 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.2.0.in' + output 'work/weights.2.2' + weights in 'work/weights.1' +(a dot per input) +Iteration #1 of 1. + .... 3 +WEIGHTS + Glue = -0.90863 + WordPenalty = +0.10819 + LanguageModel = +0.5239 + LanguageModel_OOV = -0.41623 + PhraseModel_0 = -0.86868 + PhraseModel_1 = +0.40784 + PhraseModel_2 = +1.1793 + PhraseModel_3 = -0.24698 + PhraseModel_4 = -1.2353 + PhraseModel_5 = +0.03375 + PhraseModel_6 = -0.17883 + PassThrough = -0.44638 + --- + 1best avg score: 0.12788 (+0.12788) + 1best avg model score: 41.302 + avg # pairs: 2246.3 + non-0 feature count: 12 + avg list sz: 100 + avg f count: 10.98 +(time 0.35 min, 7 s/S) + +--- +Best iteration: 1 [GOLD = 0.12788]. +This took 0.35 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..fa499523 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.3.0 @@ -0,0 +1,43 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.3.0.in' + output 'work/weights.3.0' +(a dot per input) +Iteration #1 of 1. + .. 1 +WEIGHTS + Glue = -0.09 + WordPenalty = +0.32442 + LanguageModel = +2.5769 + LanguageModel_OOV = -0.009 + PhraseModel_0 = -0.58972 + PhraseModel_1 = +0.063691 + PhraseModel_2 = +0.5366 + PhraseModel_3 = +0.12867 + PhraseModel_4 = -1.9801 + PhraseModel_5 = +0.018 + PhraseModel_6 = -0.486 + PassThrough = -0.09 + --- + 1best avg score: 0.034204 (+0.034204) + 1best avg model score: 0 + avg # pairs: 1700 + non-0 feature count: 12 + avg list sz: 100 + avg f count: 10.8 +(time 0.1 min, 6 s/S) + +--- +Best iteration: 1 [GOLD = 0.034204]. +This took 0.1 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..c4b3aa3c --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.3.1 @@ -0,0 +1,44 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.3.0.in' + output 'work/weights.3.1' + weights in 'work/weights.0' +(a dot per input) +Iteration #1 of 1. + .. 1 +WEIGHTS + Glue = +0.31832 + WordPenalty = +0.11139 + LanguageModel = +0.95438 + LanguageModel_OOV = -0.0608 + PhraseModel_0 = -0.98113 + PhraseModel_1 = -0.090531 + PhraseModel_2 = +0.79088 + PhraseModel_3 = -0.57623 + PhraseModel_4 = -1.4382 + PhraseModel_5 = +0.02435 + PhraseModel_6 = -0.10812 + PassThrough = -0.09095 + --- + 1best avg score: 0.084989 (+0.084989) + 1best avg model score: -52.323 + avg # pairs: 2487 + non-0 feature count: 12 + avg list sz: 100 + avg f count: 12 +(time 0.1 min, 6 s/S) + +--- +Best iteration: 1 [GOLD = 0.084989]. +This took 0.1 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..eb27dac2 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/out.3.2 @@ -0,0 +1,44 @@ +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 +**************************************************************************************************** +dtrain +Parameters: + k 100 + N 4 + T 1 + learning rate 0.0001 + error margin 1 + l1 reg 0 + decoder conf 'cdec.ini' + input 'work/shard.3.0.in' + output 'work/weights.3.2' + weights in 'work/weights.1' +(a dot per input) +Iteration #1 of 1. + .. 1 +WEIGHTS + Glue = -0.12993 + WordPenalty = +0.13651 + LanguageModel = +0.58946 + LanguageModel_OOV = -0.48362 + PhraseModel_0 = -0.81262 + PhraseModel_1 = +0.44273 + PhraseModel_2 = +1.1733 + PhraseModel_3 = -0.1826 + PhraseModel_4 = -1.2213 + PhraseModel_5 = +0.02435 + PhraseModel_6 = -0.18823 + PassThrough = -0.51378 + --- + 1best avg score: 0.12674 (+0.12674) + 1best avg model score: -7.2878 + avg # pairs: 1769 + non-0 feature count: 12 + avg list sz: 100 + avg f count: 12 +(time 0.1 min, 6 s/S) + +--- +Best iteration: 1 [GOLD = 0.12674]. +This took 0.1 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..a0ef6f54 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.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 . 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..05f0273b 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.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 . 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..0528d357 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/shard.2.0.in @@ -0,0 +1,3 @@ +<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 . 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..816269cd 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 +LanguageModel 1.200704259340465 +PhraseModel_4 -1.2434381298299035 +PhraseModel_1 0.050697726409824076 +PhraseModel_0 -0.516923312932941 +PhraseModel_2 0.5051987092783867 +PhraseModel_3 0.20955092377784057 +PassThrough -0.32285 +LanguageModel_OOV -0.29269999999999996 +PhraseModel_6 -0.158425 +Glue -0.05817500000000002 +WordPenalty 0.12758486142112804 +PhraseModel_5 0.02435 diff --git a/training/dtrain/examples/parallelized/work/weights.0.0 b/training/dtrain/examples/parallelized/work/weights.0.0 index c9370b18..be386c62 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 +WordPenalty -0.017632355965271129 +LanguageModel 0.72957628464102753 +LanguageModel_OOV -0.23499999999999999 +PhraseModel_0 -0.43720953659541578 +PhraseModel_1 1.0100170838129212 +PhraseModel_2 1.3524984123857073 +PhraseModel_3 -0.25541132249775761 +PhraseModel_4 -0.78115161368856911 +PhraseModel_6 -0.36810000000000004 +Glue 0.34040000000000004 +PassThrough -0.33040000000000003 diff --git a/training/dtrain/examples/parallelized/work/weights.0.1 b/training/dtrain/examples/parallelized/work/weights.0.1 index 8fad3de8..d4c77d07 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 -0.33284999999999004 -PhraseModel_5 -0.026900000000000257 +WordPenalty 0.12966947493426365 +LanguageModel 0.3989224621154368 +LanguageModel_OOV -0.63139999999999996 +PhraseModel_0 -0.63991953012355962 +PhraseModel_1 0.74197897612368646 +PhraseModel_2 1.3096163833051435 +PhraseModel_3 -0.12160001974680773 +PhraseModel_4 -1.2274031286515816 +PhraseModel_5 0.02435 +PhraseModel_6 -0.210925 +Glue -0.40907500000000002 +PassThrough -0.66155000000000008 diff --git a/training/dtrain/examples/parallelized/work/weights.0.2 b/training/dtrain/examples/parallelized/work/weights.0.2 new file mode 100644 index 00000000..8ce1449b --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.0.2 @@ -0,0 +1,12 @@ +WordPenalty 0.10319922626226019 +LanguageModel 0.6647396869692952 +LanguageModel_OOV -0.622525 +PhraseModel_0 -0.59993441316076157 +PhraseModel_1 0.78991513935858193 +PhraseModel_2 1.3148638774685031 +PhraseModel_3 0.2143393571820455 +PhraseModel_4 -1.0173894637028262 +PhraseModel_5 0.02435 +PhraseModel_6 -0.18452499999999999 +Glue -0.44422499999999998 +PassThrough -0.65267500000000012 diff --git a/training/dtrain/examples/parallelized/work/weights.1 b/training/dtrain/examples/parallelized/work/weights.1 index 03058a16..2a00be2e 100644 --- a/training/dtrain/examples/parallelized/work/weights.1 +++ b/training/dtrain/examples/parallelized/work/weights.1 @@ -1,12 +1,12 @@ -PhraseModel_2 0.8365578543552836 -PhraseModel_4 -0.5900840266009169 -PhraseModel_1 0.5312000609786991 -PhraseModel_0 -0.3872342271319619 -PhraseModel_3 -0.3728279676912084 -Glue -0.2938500000000036 -PhraseModel_6 -0.2803499999999967 -PassThrough -0.25014999999999626 -LanguageModel_OOV -0.21754999999999702 -LanguageModel 0.07306061161169894 -WordPenalty 0.09576193325966899 -PhraseModel_5 -0.026900000000000257 +PhraseModel_4 -1.1379250444170055 +PhraseModel_2 1.0578050661336098 +LanguageModel 0.9343385461706668 +PhraseModel_0 -0.6917392152965985 +PhraseModel_1 0.4508371141128957 +PassThrough -0.4411750000000001 +Glue -0.265425 +LanguageModel_OOV -0.411025 +PhraseModel_3 -0.186390082624459 +PhraseModel_6 -0.188225 +WordPenalty 0.09781397468665984 +PhraseModel_5 0.02435 diff --git a/training/dtrain/examples/parallelized/work/weights.1.0 b/training/dtrain/examples/parallelized/work/weights.1.0 index 6a6a65c1..cdcf959e 100644 --- a/training/dtrain/examples/parallelized/work/weights.1.0 +++ b/training/dtrain/examples/parallelized/work/weights.1.0 @@ -1,11 +1,12 @@ -WordPenalty 0.20064405063930751 -LanguageModel 0.9530439901597807 -LanguageModel_OOV -0.26400000000000112 -Glue -0.38150000000000084 -PhraseModel_0 -0.22362384322085468 -PhraseModel_1 0.12253609968953538 -PhraseModel_2 0.26328345736266612 -PhraseModel_3 0.38018406503151553 -PhraseModel_4 -0.48654149460854373 -PhraseModel_6 -0.36449999999999722 -PassThrough -0.22160000000000085 +WordPenalty 0.05433023968609621 +LanguageModel 0.69947965605855011 +LanguageModel_OOV -0.2641 +PhraseModel_0 -1.4207505705360111 +PhraseModel_1 -1.563047680441811 +PhraseModel_2 -0.21050528366541305 +PhraseModel_3 -0.17764037275860439 +PhraseModel_4 -1.6583462458159566 +PhraseModel_5 0.079399999999999998 +PhraseModel_6 0.15280000000000002 +Glue -0.27220000000000005 +PassThrough -0.23670000000000002 diff --git a/training/dtrain/examples/parallelized/work/weights.1.1 b/training/dtrain/examples/parallelized/work/weights.1.1 index f56ea4a2..c1bb2cf0 100644 --- a/training/dtrain/examples/parallelized/work/weights.1.1 +++ b/training/dtrain/examples/parallelized/work/weights.1.1 @@ -1,12 +1,12 @@ -WordPenalty 0.1109188106780935 -LanguageModel 0.17269294375442074 -LanguageModel_OOV -0.13485000000000266 -Glue -0.3178000000000088 -PhraseModel_2 0.75311275661967159 -PhraseModel_1 0.38789263503268989 -PhraseModel_4 -0.58816805170415531 -PhraseModel_3 -0.38163156335114284 -PhraseModel_6 -0.27314999999999739 -PhraseModel_0 -0.45370601223484697 -PassThrough -0.16745000000000249 -PhraseModel_5 -0.026900000000000257 +WordPenalty -0.0091744709302067785 +LanguageModel 0.79433413663506514 +LanguageModel_OOV -0.43090000000000001 +PhraseModel_0 -0.56242499947237046 +PhraseModel_1 0.85362516703032698 +PhraseModel_2 1.3457900890481096 +PhraseModel_3 -0.13095079554478939 +PhraseModel_4 -0.94761908497413061 +PhraseModel_5 0.02435 +PhraseModel_6 -0.160025 +Glue -0.20487500000000003 +PassThrough -0.46105000000000007 diff --git a/training/dtrain/examples/parallelized/work/weights.1.2 b/training/dtrain/examples/parallelized/work/weights.1.2 new file mode 100644 index 00000000..c9598a04 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.1.2 @@ -0,0 +1,12 @@ +WordPenalty 0.076359827280638559 +LanguageModel 1.3183380272921175 +LanguageModel_OOV -0.60902499999999993 +PhraseModel_0 -0.2248075206657828 +PhraseModel_1 0.86368802571834491 +PhraseModel_2 1.0746702462261808 +PhraseModel_3 0.18002263643876637 +PhraseModel_4 -0.84660750337519441 +PhraseModel_5 0.02435 +PhraseModel_6 0.11247499999999999 +Glue -0.49852500000000005 +PassThrough -0.63917500000000005 diff --git a/training/dtrain/examples/parallelized/work/weights.2 b/training/dtrain/examples/parallelized/work/weights.2 new file mode 100644 index 00000000..310973ec --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.2 @@ -0,0 +1,12 @@ +PhraseModel_2 1.185520780812669 +PhraseModel_4 -1.0801541070647134 +LanguageModel 0.7741099486587568 +PhraseModel_0 -0.6265095873268189 +PhraseModel_1 0.6260421233840029 +PassThrough -0.5630000000000002 +Glue -0.495325 +LanguageModel_OOV -0.53285 +PhraseModel_3 -0.008805626854390465 +PhraseModel_6 -0.10977500000000001 +WordPenalty 0.1060655698428214 +PhraseModel_5 0.026699999999999998 diff --git a/training/dtrain/examples/parallelized/work/weights.2.0 b/training/dtrain/examples/parallelized/work/weights.2.0 new file mode 100644 index 00000000..3e87fed4 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.2.0 @@ -0,0 +1,11 @@ +WordPenalty 0.14922358398195767 +LanguageModel 0.79685677298009394 +LanguageModel_OOV -0.66270000000000007 +PhraseModel_0 0.37998874905310187 +PhraseModel_1 0.69213063228111271 +PhraseModel_2 0.34219807728516061 +PhraseModel_3 1.1425846772648622 +PhraseModel_4 -0.55412548521619742 +PhraseModel_6 0.067599999999999993 +Glue -0.21090000000000003 +PassThrough -0.63429999999999997 diff --git a/training/dtrain/examples/parallelized/work/weights.2.1 b/training/dtrain/examples/parallelized/work/weights.2.1 new file mode 100644 index 00000000..d129dc49 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.2.1 @@ -0,0 +1,12 @@ +WordPenalty 0.1593752174964457 +LanguageModel 1.5897162231676281 +LanguageModel_OOV -0.52100000000000002 +PhraseModel_0 -0.5834836741748588 +PhraseModel_1 0.29827543837280185 +PhraseModel_2 0.78493316593562568 +PhraseModel_3 0.083221832554333464 +PhraseModel_4 -0.93843312963279457 +PhraseModel_5 0.02435 +PhraseModel_6 -0.27382499999999999 +Glue -0.76607500000000006 +PassThrough -0.55115000000000003 diff --git a/training/dtrain/examples/parallelized/work/weights.2.2 b/training/dtrain/examples/parallelized/work/weights.2.2 new file mode 100644 index 00000000..bcc83b44 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.2.2 @@ -0,0 +1,12 @@ +WordPenalty 0.10819361280414735 +LanguageModel 0.52389743342585859 +LanguageModel_OOV -0.41622500000000001 +PhraseModel_0 -0.86867995703334211 +PhraseModel_1 0.40783818771767943 +PhraseModel_2 1.1792706530114188 +PhraseModel_3 -0.2469805689928464 +PhraseModel_4 -1.2352895858909159 +PhraseModel_5 0.033750000000000002 +PhraseModel_6 -0.17882500000000001 +Glue -0.90862500000000002 +PassThrough -0.44637500000000013 diff --git a/training/dtrain/examples/parallelized/work/weights.3.0 b/training/dtrain/examples/parallelized/work/weights.3.0 new file mode 100644 index 00000000..e3586048 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.3.0 @@ -0,0 +1,12 @@ +WordPenalty 0.32441797798172944 +LanguageModel 2.5769043236821889 +LanguageModel_OOV -0.0090000000000000011 +PhraseModel_0 -0.58972189365343919 +PhraseModel_1 0.063690869987073351 +PhraseModel_2 0.53660363110809217 +PhraseModel_3 0.12867071310286207 +PhraseModel_4 -1.9801291745988916 +PhraseModel_5 0.018000000000000002 +PhraseModel_6 -0.48600000000000004 +Glue -0.090000000000000011 +PassThrough -0.090000000000000011 diff --git a/training/dtrain/examples/parallelized/work/weights.3.1 b/training/dtrain/examples/parallelized/work/weights.3.1 new file mode 100644 index 00000000..b27687d3 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.3.1 @@ -0,0 +1,12 @@ +WordPenalty 0.11138567724613679 +LanguageModel 0.95438136276453733 +LanguageModel_OOV -0.060799999999999937 +PhraseModel_0 -0.98112865741560529 +PhraseModel_1 -0.090531125075232435 +PhraseModel_2 0.79088062624556033 +PhraseModel_3 -0.57623134776057228 +PhraseModel_4 -1.4382448344095151 +PhraseModel_5 0.02435 +PhraseModel_6 -0.108125 +Glue 0.31832499999999997 +PassThrough -0.090950000000000003 diff --git a/training/dtrain/examples/parallelized/work/weights.3.2 b/training/dtrain/examples/parallelized/work/weights.3.2 new file mode 100644 index 00000000..ccb591a2 --- /dev/null +++ b/training/dtrain/examples/parallelized/work/weights.3.2 @@ -0,0 +1,12 @@ +WordPenalty 0.13650961302423945 +LanguageModel 0.58946464694775647 +LanguageModel_OOV -0.48362499999999997 +PhraseModel_0 -0.81261645844738917 +PhraseModel_1 0.44272714074140529 +PhraseModel_2 1.1732783465445731 +PhraseModel_3 -0.18260393204552733 +PhraseModel_4 -1.2213298752899167 +PhraseModel_5 0.02435 +PhraseModel_6 -0.188225 +Glue -0.12992500000000001 +PassThrough -0.51377500000000009 diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini index a515db02..f2698007 100644 --- a/training/dtrain/examples/standard/dtrain.ini +++ b/training/dtrain/examples/standard/dtrain.ini @@ -1,27 +1,10 @@ -#input=./nc-wmt11.de.gz -#refs=./nc-wmt11.en.gz -bitext=./nc-wmt11.gz +bitext=./nc-wmt11.gz # input bitext output=- # a weights file (add .gz for gzip compression) or STDOUT '-' -select_weights=avg # output average (over epochs) weight vector decoder_config=./cdec.ini # config for cdec -# weights for these features will be printed on each iteration +iterations=3 # run over input 3 times +k=100 # use 100best lists +N=4 # optimize (approx.) BLEU4 +learning_rate=0.1 # learning rate +error_margin=1.0 # margin for margin perceptron print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough -# newer version of the grammar extractor use different feature names: -#print_weights= EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV -stop_after=10 # stop epoch after 10 inputs -# interesting stuff -epochs=3 # run over input 3 times -k=100 # use 100best lists -N=4 # optimize (approx) BLEU4 -scorer=fixed_stupid_bleu # use 'stupid' BLEU+1 -learning_rate=0.1 # learning rate, don't care if gamma=0 (perceptron) and loss_margin=0 (not margin perceptron) -gamma=0 # use SVM reg -sample_from=kbest # use kbest lists (as opposed to forest) -filter=uniq # only unique entries in kbest (surface form) -pair_sampling=XYX # -hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here -pair_threshold=0 # minimum distance in BLEU (here: > 0) -loss_margin=0 # update if correctly ranked, but within this margin -repeat=1 # repeat training on a kbest list 1 times -#batch=true # batch tuning, update after accumulating over all sentences and all kbest lists diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output deleted file mode 100644 index 2460cfbb..00000000 --- a/training/dtrain/examples/standard/expected-output +++ /dev/null @@ -1,123 +0,0 @@ - cdec cfg './cdec.ini' -Loading the LM will be faster if you build a binary file. -Reading ./nc-wmt11.en.srilm.gz -----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 -**************************************************************************************************** - Example feature: Shape_S00000_T00000 -T=1 I=1 D=1 -Seeding random number sequence to 2327685089 - -dtrain -Parameters: - k 100 - N 4 - T 3 - batch 0 - scorer 'fixed_stupid_bleu' - sample from 'kbest' - filter 'uniq' - learning rate 0.1 - gamma 0 - loss margin 0 - faster perceptron 1 - pairs 'XYX' - hi lo 0.1 - pair threshold 0 - select weights 'avg' - l1 reg 0 'none' - pclr no - max pairs 4294967295 - repeat 1 - cdec cfg './cdec.ini' - input './nc-wmt11.gz' - output '-' - stop_after 10 -(a dot represents 10 inputs) -Iteration #1 of 3. - . 10 -Stopping after 10 input sentences. -WEIGHTS - Glue = +6.9 - WordPenalty = -46.426 - LanguageModel = +535.12 - LanguageModel_OOV = -123.5 - PhraseModel_0 = -160.73 - PhraseModel_1 = -350.13 - PhraseModel_2 = -187.81 - PhraseModel_3 = +172.04 - PhraseModel_4 = +0.90108 - PhraseModel_5 = +21.6 - PhraseModel_6 = +67.2 - PassThrough = -149.7 - --- - 1best avg score: 0.23327 (+0.23327) - 1best avg model score: -9084.9 (-9084.9) - avg # pairs: 780.7 - avg # rank err: 0 (meaningless) - avg # margin viol: 0 - k-best loss imp: 100% - non0 feature count: 1389 - avg list sz: 91.3 - avg f count: 146.2 -(time 0.37 min, 2.2 s/S) - -Iteration #2 of 3. - . 10 -WEIGHTS - Glue = -43 - WordPenalty = -22.019 - LanguageModel = +591.53 - LanguageModel_OOV = -252.1 - PhraseModel_0 = -120.21 - PhraseModel_1 = -43.589 - PhraseModel_2 = +73.53 - PhraseModel_3 = +113.7 - PhraseModel_4 = -223.81 - PhraseModel_5 = +64 - PhraseModel_6 = +54.8 - PassThrough = -331.1 - --- - 1best avg score: 0.29568 (+0.062413) - 1best avg model score: -15879 (-6794.1) - avg # pairs: 566.1 - avg # rank err: 0 (meaningless) - avg # margin viol: 0 - k-best loss imp: 100% - non0 feature count: 1931 - avg list sz: 91.3 - avg f count: 139.89 -(time 0.33 min, 2 s/S) - -Iteration #3 of 3. - . 10 -WEIGHTS - Glue = -44.3 - WordPenalty = -131.85 - LanguageModel = +230.91 - LanguageModel_OOV = -285.4 - PhraseModel_0 = -194.27 - PhraseModel_1 = -294.83 - PhraseModel_2 = -92.043 - PhraseModel_3 = -140.24 - PhraseModel_4 = +85.613 - PhraseModel_5 = +238.1 - PhraseModel_6 = +158.7 - PassThrough = -359.6 - --- - 1best avg score: 0.37375 (+0.078067) - 1best avg model score: -14519 (+1359.7) - avg # pairs: 545.4 - avg # rank err: 0 (meaningless) - avg # margin viol: 0 - k-best loss imp: 100% - non0 feature count: 2218 - avg list sz: 91.3 - avg f count: 137.77 -(time 0.35 min, 2.1 s/S) - -Writing weights file to '-' ... -done - ---- -Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.37375]. -This took 1.05 min. diff --git a/training/dtrain/examples/standard/expected-output.gz b/training/dtrain/examples/standard/expected-output.gz Binary files differnew file mode 100644 index 00000000..43e6b21a --- /dev/null +++ b/training/dtrain/examples/standard/expected-output.gz diff --git a/training/dtrain/examples/standard/nc-wmt11.de.gz b/training/dtrain/examples/standard/nc-wmt11.de.gz Binary files differdeleted file mode 100644 index 0741fd92..00000000 --- a/training/dtrain/examples/standard/nc-wmt11.de.gz +++ /dev/null diff --git a/training/dtrain/examples/standard/nc-wmt11.en.gz b/training/dtrain/examples/standard/nc-wmt11.en.gz Binary files differdeleted file mode 100644 index 1c0bd401..00000000 --- a/training/dtrain/examples/standard/nc-wmt11.en.gz +++ /dev/null diff --git a/training/dtrain/examples/toy/dtrain.ini b/training/dtrain/examples/toy/dtrain.ini index ef956df7..378224b8 100644 --- a/training/dtrain/examples/toy/dtrain.ini +++ b/training/dtrain/examples/toy/dtrain.ini @@ -1,13 +1,8 @@ decoder_config=cdec.ini -input=src -refs=tgt -output=- -print_weights=logp shell_rule house_rule small_rule little_rule PassThrough PassThrough_1 PassThrough_2 PassThrough_3 PassThrough_4 PassThrough_5 PassThrough_6 +bitext=in +output=weights k=4 N=4 -epochs=2 -scorer=bleu -sample_from=kbest -filter=uniq -pair_sampling=all +iterations=2 learning_rate=1 +print_weights=logp shell_rule house_rule small_rule little_rule PassThrough PassThrough_1 PassThrough_2 PassThrough_3 PassThrough_4 PassThrough_5 PassThrough_6 diff --git a/training/dtrain/examples/toy/expected-output b/training/dtrain/examples/toy/expected-output index 1da2aadd..8c758d00 100644 --- a/training/dtrain/examples/toy/expected-output +++ b/training/dtrain/examples/toy/expected-output @@ -1,77 +1,63 @@ -Warning: hi_lo only works with pair_sampling XYX. - cdec cfg 'cdec.ini' -Seeding random number sequence to 1664825829 - dtrain Parameters: k 4 N 4 T 2 - scorer 'bleu' - sample from 'kbest' - filter 'uniq' learning rate 1 - gamma 0 - loss margin 0 - pairs 'all' - pair threshold 0 - select weights 'last' - l1 reg 0 'none' - max pairs 4294967295 - cdec cfg 'cdec.ini' - input 'src' - refs 'tgt' - output '-' -(a dot represents 10 inputs) + error margin 0 + l1 reg 0 + decoder conf 'cdec.ini' + input 'in' + output 'weights' +(a dot per input) Iteration #1 of 2. - 2 + ... 2 WEIGHTS logp = +0 - shell_rule = -1 - house_rule = +2 - small_rule = -2 + shell_rule = +0 + house_rule = +3 + small_rule = +0 little_rule = +3 - PassThrough = -5 + PassThrough = -15 + PassThrough_1 = +0 + PassThrough_2 = +0 + PassThrough_3 = +0 + PassThrough_4 = +0 + PassThrough_5 = +0 + PassThrough_6 = +0 --- - 1best avg score: 0.5 (+0.5) - 1best avg model score: 2.5 (+2.5) - avg # pairs: 4 - avg # rank err: 1.5 - avg # margin viol: 0 - non0 feature count: 6 + 1best avg score: 0.40937 (+0.40937) + 1best avg model score: 3 + avg # pairs: 2.5 + non-0 feature count: 4 avg list sz: 4 avg f count: 2.875 (time 0 min, 0 s/S) Iteration #2 of 2. - 2 + ... 2 WEIGHTS logp = +0 - shell_rule = -1 - house_rule = +2 - small_rule = -2 + shell_rule = +0 + house_rule = +3 + small_rule = +0 little_rule = +3 - PassThrough = -5 + PassThrough = -15 + PassThrough_1 = +0 + PassThrough_2 = +0 + PassThrough_3 = +0 + PassThrough_4 = +0 + PassThrough_5 = +0 + PassThrough_6 = +0 --- - 1best avg score: 1 (+0.5) - 1best avg model score: 5 (+2.5) - avg # pairs: 5 - avg # rank err: 0 - avg # margin viol: 0 - non0 feature count: 6 + 1best avg score: 0.81873 (+0.40937) + 1best avg model score: 6 + avg # pairs: 0 + non-0 feature count: 4 avg list sz: 4 avg f count: 3 (time 0 min, 0 s/S) -Writing weights file to '-' ... -house_rule 2 -little_rule 3 -Glue -4 -PassThrough -5 -small_rule -2 -shell_rule -1 -done - --- -Best iteration: 2 [SCORE 'bleu'=1]. +Best iteration: 2 [GOLD = 0.81873]. This took 0 min. diff --git a/training/dtrain/examples/toy/in b/training/dtrain/examples/toy/in new file mode 100644 index 00000000..5d70795d --- /dev/null +++ b/training/dtrain/examples/toy/in @@ -0,0 +1,2 @@ +ich sah ein kleines haus ||| i saw a little house +ich fand ein kleines haus ||| i found a little house diff --git a/training/dtrain/examples/toy/src b/training/dtrain/examples/toy/src deleted file mode 100644 index 87e39ef2..00000000 --- a/training/dtrain/examples/toy/src +++ /dev/null @@ -1,2 +0,0 @@ -ich sah ein kleines haus -ich fand ein kleines haus diff --git a/training/dtrain/examples/toy/tgt b/training/dtrain/examples/toy/tgt deleted file mode 100644 index 174926b3..00000000 --- a/training/dtrain/examples/toy/tgt +++ /dev/null @@ -1,2 +0,0 @@ -i saw a little house -i found a little house diff --git a/training/dtrain/examples/toy/weights b/training/dtrain/examples/toy/weights new file mode 100644 index 00000000..f6f32772 --- /dev/null +++ b/training/dtrain/examples/toy/weights @@ -0,0 +1,4 @@ +house_rule 3 +little_rule 3 +Glue -12 +PassThrough -15 |