Starting cxg2 and twitter for ara (8000, 27693) 8000 {'C': 0.01, 'loss': 'squared_hinge'} (77690, 27693) 77690 fit_time [64.55056453 65.72509003 43.95628309 53.51638579 57.71557355 55.68115401 49.55521154 60.50441241 54.74861431 56.9448576 ] score_time [0.05437613 0.05229092 0.05861354 0.05506349 0.05771232 0.05762005 0.05727005 0.05667639 0.05188322 0.05872464] test_precision_weighted [0.97928949 0.98022293 0.97545461 0.9745533 0.97925087 0.97837042 0.98064036 0.97582132 0.9789372 0.9756153 ] train_precision_weighted [0.99898477 0.99898463 0.99901339 0.99915635 0.99897053 0.99907058 0.99901336 0.99902792 0.99911363 0.99899914] test_recall_weighted [0.97928461 0.98018273 0.97542144 0.97451737 0.97915058 0.97837281 0.98055877 0.97579503 0.97888503 0.97553753] train_recall_weighted [0.99898452 0.99898454 0.99901314 0.99915618 0.99897025 0.99907039 0.9990132 0.9990275 0.99911331 0.9989989 ] test_f1_weighted [0.97927873 0.98018806 0.97541346 0.97450481 0.97917612 0.97836623 0.98058131 0.97579797 0.97890344 0.97556635] train_f1_weighted [0.99898459 0.99898455 0.9990132 0.9991562 0.99897027 0.99907044 0.99901324 0.9990276 0.99911339 0.99899896] Starting cxg2 and cc for ara (7000, 27693) 7000 {'C': 0.01, 'loss': 'squared_hinge'} (83427, 27693) 83427 fit_time [13.41482806 24.69719362 27.52523565 35.42868328 23.64864349 24.4990449 28.07714629 29.47051239 23.46794295 26.96810532] score_time [0.08362317 0.09558964 0.09162068 0.07820296 0.09157276 0.09162617 0.09062409 0.08946252 0.09099984 0.08950186] test_precision_weighted [0.99628852 0.99629171 0.99688354 0.99580419 0.99653407 0.99700574 0.99616505 0.99640668 0.99760463 0.99628689] train_precision_weighted [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] test_recall_weighted [0.99628431 0.99628431 0.99688362 0.99580487 0.99652403 0.99700348 0.99616445 0.99640374 0.99760249 0.99628386] train_recall_weighted [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] test_f1_weighted [0.99628271 0.99628395 0.99688338 0.99580264 0.99652023 0.99700344 0.9961634 0.99640202 0.99760162 0.99628307] train_f1_weighted [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] Starting cxg2 and twitter for deu (4000, 21170) 4000 {'C': 0.01, 'loss': 'squared_hinge'} (41686, 21170) 41686 fit_time [4.01490068 4.16471887 4.50689173 4.57523012 4.67104363 4.23566866 4.22205424 2.69601941 4.15187955 4.37676096] score_time [0.02101231 0.02071047 0.0208416 0.0200913 0.01925063 0.02080679 0.02028489 0.01836824 0.01858854 0.01866627] test_precision_weighted [0.95548613 0.96054856 0.95615413 0.95940301 0.96078696 0.95234491 0.959812 0.96178748 0.96126568 0.9591259 ] train_precision_weighted [0.99502078 0.99547523 0.9949145 0.99499775 0.99510126 0.99494497 0.99523707 0.99515619 0.99499687 0.99475873] test_recall_weighted [0.95563549 0.96066203 0.95610458 0.9594627 0.96090189 0.9524952 0.95993282 0.96185221 0.96137236 0.95921305] train_recall_weighted [0.99501546 0.99546872 0.99490897 0.99498894 0.99509556 0.99493576 0.99522896 0.995149 0.99498907 0.99474919] test_f1_weighted [0.95547523 0.96052369 0.95577196 0.95924129 0.96078401 0.9522506 0.95981205 0.96166759 0.96123267 0.95901146] train_f1_weighted [0.99501022 0.99546391 0.99490351 0.99498286 0.99509035 0.99492952 0.99522343 0.99514353 0.99498321 0.99474254] Starting cxg2 and cc for deu (16000, 21170) 16000 {'C': 0.01, 'loss': 'squared_hinge'} (191933, 21170) 191933 fit_time [1669.73730803 1664.87291265 1401.52578235 1560.90886068 1579.35159421 1308.29891992 1582.35248566 1559.94025254 1564.12139034 1630.35610938] score_time [0.21136093 0.21204114 0.21211934 0.22428012 0.22101188 0.21073031 0.21055412 0.21812034 0.21442008 0.21360373] test_precision_weighted [0.96102452 0.96240293 0.96305227 0.96270232 0.96228468 0.96255245 0.96441713 0.962261 0.96192984 0.96291033] train_precision_weighted [0.9940974 0.99414402 0.99432938 0.99400951 0.99427058 0.99412083 0.99399887 0.99407972 0.99424812 0.99407959] test_recall_weighted [0.96108362 0.96248828 0.96311347 0.96269668 0.96233198 0.96264458 0.96441411 0.96227791 0.96201344 0.96295138] train_recall_weighted [0.99410089 0.99414724 0.99433249 0.99401409 0.9942746 0.99412408 0.99400255 0.99408359 0.99425154 0.99408366] test_f1_weighted [0.96098803 0.96242717 0.96305449 0.96266728 0.96225865 0.96253244 0.9643789 0.96224673 0.96193524 0.96289296] train_f1_weighted [0.99409701 0.99414371 0.9943287 0.99400913 0.99427055 0.99412007 0.99399841 0.9940789 0.99424749 0.99407929] Starting cxg2 and twitter for eng (28000, 22628) 28000 {'C': 0.01, 'loss': 'squared_hinge'} (367932, 22628) 367932 fit_time [452.01722074 444.3831861 444.17092514 448.81175637 440.84906244 441.14397717 438.42810583 444.31140947 450.92097545 441.82863498] score_time [0.71198082 0.72047138 0.7064929 0.70756435 0.73957419 0.71401358 0.72834873 0.66843677 0.64823842 0.67233181] test_precision_weighted [0.92045826 0.91891702 0.9218177 0.92159673 0.92188806 0.92105364 0.91815907 0.92203949 0.92144679 0.92020709] train_precision_weighted [0.97319696 0.97307228 0.97303821 0.97306719 0.97315258 0.97306281 0.97335608 0.97299647 0.97311754 0.97315298] test_recall_weighted [0.92047616 0.91887264 0.92183508 0.92161766 0.92178073 0.9211263 0.91810942 0.92210475 0.92128724 0.92025224] train_recall_weighted [0.97316527 0.97304145 0.97300521 0.97303541 0.97311695 0.97303549 0.97331634 0.97296302 0.97309597 0.97312625] test_f1_weighted [0.92024874 0.91869579 0.92159989 0.92139681 0.92168832 0.92090295 0.917901 0.92188461 0.92120285 0.92006795] train_f1_weighted [0.97308583 0.97295578 0.97292445 0.97295697 0.9730373 0.97295674 0.9732418 0.97288465 0.97301305 0.97304576] Starting cxg2 and cc for eng (28000, 22628) 28000 {'C': 0.01, 'loss': 'squared_hinge'} (389063, 22628) 389063 fit_time [817.22686458 859.40181375 838.1535151 929.82348728 786.37804532 919.11459732 910.43978548 831.82859182 940.64636374 927.20827651] score_time [0.85381174 0.79919314 0.81759167 0.80375123 0.85098553 0.79991031 0.7872622 0.81081629 0.76955509 0.76948333] test_precision_weighted [0.96205072 0.96052518 0.96194588 0.96174947 0.96155878 0.9604945 0.96198502 0.96212432 0.96065453 0.96240519] train_precision_weighted [0.99624928 0.99618963 0.99621512 0.9961834 0.99618041 0.99625769 0.996163 0.99633158 0.99626324 0.99630873] test_recall_weighted [0.96190917 0.96044414 0.96183206 0.96167686 0.96136843 0.96044312 0.96185678 0.96206241 0.96052023 0.96231944] train_recall_weighted [0.99624739 0.99618741 0.99621312 0.99618171 0.99617886 0.99625597 0.99616172 0.99633022 0.99626168 0.99630737] test_f1_weighted [0.96196031 0.96047009 0.96186763 0.96169258 0.96142022 0.960437 0.96189663 0.96207932 0.96055475 0.96234359] train_f1_weighted [0.99624774 0.99618796 0.99621363 0.99618201 0.99617901 0.99625628 0.9961618 0.99633038 0.99626195 0.99630768] Starting cxg2 and twitter for fra (5000, 21747) 5000 {'C': 0.01, 'loss': 'squared_hinge'} (65656, 21747) 65656 fit_time [10.61974549 10.42445803 10.67706752 10.58165812 10.42569351 7.77576566 10.24376059 10.50162268 10.32045603 10.21116471] score_time [0.06910777 0.06885338 0.06657171 0.06672931 0.06573319 0.05874705 0.06583691 0.06644511 0.06267715 0.06421423] test_precision_weighted [0.98127952 0.98505762 0.98399661 0.98566387 0.98384692 0.98338743 0.98186829 0.98580426 0.98323522 0.98459626] train_precision_weighted [0.99900192 0.99896814 0.99888427 0.99886686 0.99871433 0.99891775 0.99879912 0.99884978 0.99881627 0.99888366] test_recall_weighted [0.98126999 0.9850769 0.98401096 0.98568383 0.98385623 0.98339933 0.98187357 0.98583181 0.98324193 0.98461304] train_recall_weighted [0.99900151 0.99896766 0.99888304 0.99886614 0.99871383 0.99891691 0.99879846 0.99884925 0.99881541 0.9988831 ] test_f1_weighted [0.98122315 0.9850193 0.98397837 0.98563621 0.98384548 0.98333978 0.98182409 0.9857838 0.98320476 0.98457801] train_f1_weighted [0.99900108 0.99896719 0.99888235 0.99886554 0.99871316 0.99891632 0.99879783 0.99884868 0.99881473 0.99888255] Starting cxg2 and cc for fra (21000, 21747) 21000 {'C': 0.01, 'loss': 'squared_hinge'} (257964, 21747) 257964 fit_time [595.88708377 560.64686036 549.49936604 639.73811913 595.27377558 524.62417531 572.8971827 583.80732918 602.90420079 537.79433632] score_time [0.55283546 0.53261423 0.56315923 0.52550983 0.52732038 0.53148985 0.56038094 0.55306602 0.52791166 0.5519321 ] test_precision_weighted [0.95759575 0.95865972 0.95714481 0.9554688 0.9589241 0.9580476 0.95712246 0.95795676 0.95790922 0.95836047] train_precision_weighted [0.99673888 0.99669605 0.99670381 0.99668685 0.99651988 0.99648025 0.99667868 0.9966694 0.99659731 0.99662308] test_recall_weighted [0.95755814 0.95860465 0.95701217 0.95534364 0.95879206 0.95797798 0.95712347 0.95778088 0.95785842 0.95832364] train_recall_weighted [0.99673507 0.99669199 0.99670064 0.99668342 0.99651545 0.99647669 0.99667484 0.99666624 0.99659301 0.99661886] test_f1_weighted [0.95751004 0.95855003 0.95696456 0.95531385 0.95874238 0.95794888 0.95707606 0.95774599 0.95778418 0.95827364] train_f1_weighted [0.99673465 0.99669161 0.99670043 0.99668284 0.99651489 0.99647592 0.99667428 0.9966653 0.99659274 0.99661866] Starting cxg2 and twitter for rus (4000, 15126) 4000 {'C': 0.01, 'loss': 'squared_hinge'} (50823, 15126) 50823 fit_time [6.28783512 6.39717412 6.08253956 6.1466701 6.61533952 6.06623602 6.03670001 6.2445538 6.12506652 5.99974537] score_time [0.03373814 0.03256083 0.0329752 0.03209686 0.03130984 0.03177929 0.0309937 0.03012013 0.02915263 0.02958179] test_precision_weighted [0.94047344 0.94682767 0.94557615 0.93800086 0.94564573 0.94050812 0.93912319 0.93439523 0.94041903 0.93805966] train_precision_weighted [0.98554782 0.98445606 0.98520113 0.98567224 0.98512691 0.98495454 0.98551621 0.98557792 0.9854815 0.98595877] test_recall_weighted [0.940783 0.9470785 0.94589809 0.93802872 0.94589809 0.940783 0.93939394 0.93465853 0.94075969 0.93839795] train_recall_weighted [0.98552689 0.98443376 0.98517709 0.98565807 0.9851115 0.9849366 0.98548348 0.98554939 0.98546194 0.9859429 ] test_f1_weighted [0.94057921 0.9468063 0.94551702 0.93753622 0.94557871 0.94036329 0.93909123 0.93427208 0.9403713 0.93794748] train_f1_weighted [0.98547055 0.98437002 0.98511458 0.98560713 0.98505443 0.98487377 0.98542277 0.9854873 0.9854037 0.98589014] Starting cxg2 and cc for rus (36000, 15126) 36000 {'C': 0.01, 'loss': 'squared_hinge'} (483894, 15126) 483894 fit_time [1273.59774828 991.04914546 967.03274798 895.07490158 953.89825106 1307.54697728 1077.62626886 778.02810621 1109.26978278 1163.85374045] score_time [0.72158313 0.72114849 0.72911429 0.74810815 0.73581481 0.70770431 0.73906946 0.72501135 0.73552871 0.73061442] test_precision_weighted [0.94595916 0.94799623 0.94503643 0.94486618 0.94551944 0.94540715 0.94621329 0.94626673 0.94786269 0.94670915] train_precision_weighted [0.98210556 0.98211898 0.98217356 0.9821907 0.9820642 0.98216021 0.98200308 0.98196833 0.98196438 0.98211648] test_recall_weighted [0.94581861 0.94790354 0.94486578 0.94474179 0.94542374 0.94515396 0.94610234 0.94624589 0.94773282 0.94651345] train_recall_weighted [0.9820873 0.98209656 0.98215856 0.98217004 0.98204834 0.98214253 0.98198647 0.98194748 0.98194293 0.98210366] test_f1_weighted [0.94586245 0.94791374 0.94490343 0.94476258 0.9454395 0.94522762 0.9461263 0.94622916 0.94775027 0.94656921] train_f1_weighted [0.9820892 0.98210031 0.98215983 0.98217325 0.98204937 0.98214505 0.98198719 0.98195096 0.98194599 0.98210337] Starting cxg2 and twitter for por (4000, 8120) 4000 {'C': 1.0, 'loss': 'hinge'} (48250, 8120) 48250 fit_time [1.80282307 1.64151335 2.03454208 1.81755424 1.91253519 1.72677541 1.68401885 1.74501753 1.567487 1.89450979] score_time [0.01863074 0.01770473 0.0184741 0.01891851 0.01823354 0.01760912 0.01885176 0.0173831 0.01741242 0.01741505] test_precision_weighted [0.99585551 0.99564708 0.99564708 0.99461118 0.99689162 0.99564729 0.9966869 0.99627248 0.99689095 0.99647715] train_precision_weighted [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] test_recall_weighted [0.99585492 0.99564767 0.99564767 0.9946114 0.99689119 0.99564767 0.99668394 0.99626943 0.99689119 0.99647668] train_recall_weighted [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] test_f1_weighted [0.99585358 0.99564697 0.99564697 0.99460965 0.99689136 0.99564743 0.9966825 0.99627023 0.99689102 0.99647687] train_f1_weighted [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] Starting cxg2 and cc for por (6000, 8120) 6000 {'C': 0.01, 'loss': 'squared_hinge'} (85646, 8120) 85646 fit_time [145.79878712 68.65693665 142.11033559 111.2191143 138.76104259 136.39193845 139.53454757 130.83088422 113.49615383 127.81073046] score_time [0.04349613 0.04423904 0.04508018 0.0469265 0.04698801 0.04735136 0.04636955 0.04676557 0.04782224 0.04671979] test_precision_weighted [0.99369112 0.99333825 0.99321842 0.99040013 0.99321628 0.9928741 0.9922763 0.99075496 0.99263091 0.99204321] train_precision_weighted [0.9986763 0.99863732 0.99870218 0.99875411 0.99874123 0.99872826 0.99881909 0.99878016 0.99863741 0.99871533] test_recall_weighted [0.99369601 0.99334501 0.99322825 0.99042615 0.99322825 0.99287716 0.99229332 0.99077534 0.99264362 0.99205979] train_recall_weighted [0.9986767 0.9986378 0.99870266 0.99875456 0.99874158 0.99872863 0.99881944 0.99878052 0.99863781 0.99871565] test_f1_weighted [0.99369189 0.99333867 0.99322195 0.99039801 0.99322013 0.99287475 0.99227654 0.99075686 0.9926335 0.99204941] train_f1_weighted [0.99867633 0.99863732 0.99870223 0.99875406 0.99874111 0.99872824 0.99881909 0.99878022 0.99863733 0.99871531] Starting cxg2 and twitter for spa (32000, 14846) 32000 {'C': 0.01, 'loss': 'squared_hinge'} (433102, 14846) 433102 fit_time [953.87870765 930.62650919 902.14967394 912.24331975 869.97351408 984.11593771 930.06290293 931.74510765 937.25608087 919.22076845] score_time [0.78971004 0.78284287 0.81262827 0.80185008 0.7472496 0.74386883 0.81096768 0.80749464 0.74118519 0.79088116] test_precision_weighted [0.93665224 0.9401309 0.93844706 0.93724075 0.93842466 0.93613357 0.93754186 0.93941575 0.93862681 0.93740913] train_precision_weighted [0.97530057 0.97536455 0.9754468 0.97543037 0.97552797 0.97566661 0.97535894 0.97549557 0.97543505 0.9756878 ] test_recall_weighted [0.93662557 0.94015515 0.93842353 0.93726767 0.93842211 0.93611175 0.93756638 0.93938904 0.93860115 0.93735279] train_recall_weighted [0.97528657 0.9753457 0.97543293 0.97541246 0.97551508 0.97564855 0.97534069 0.97547929 0.97542041 0.97566419] test_f1_weighted [0.9365491 0.94008226 0.9383796 0.93719983 0.93836306 0.93606067 0.93751621 0.93934938 0.93854399 0.93730419] train_f1_weighted [0.97526386 0.97532314 0.97541083 0.97538925 0.97549263 0.97562733 0.9753177 0.97545652 0.97539747 0.97564236] Starting cxg2 and cc for spa (34000, 14846) 34000 {'C': 0.01, 'loss': 'squared_hinge'} (500465, 14846) 500465 fit_time [8764.28743362 8914.56190729 8680.51322532 8875.97545385 8822.25314426 8581.77474546 8541.03435588 8647.32895446 8349.87638283 8939.4011519 ] score_time [1.01781201 0.98601818 1.01653075 1.02102494 1.00153065 1.01256061 1.02052999 1.00735092 1.03051305 0.98468328] test_precision_weighted [0.95076066 0.95242481 0.95248062 0.95236132 0.95166049 0.95174335 0.954175 0.95325016 0.95313347 0.95245332] train_precision_weighted [0.98842045 0.98842149 0.98842656 0.98849151 0.98852196 0.98861454 0.98832992 0.98847946 0.98842603 0.98849905] test_recall_weighted [0.95080624 0.95248466 0.95250465 0.9523448 0.95172538 0.95170443 0.95420213 0.95320305 0.95314311 0.95248371] train_recall_weighted [0.98842409 0.98842409 0.98843075 0.98849291 0.98852621 0.98861949 0.98833531 0.98848406 0.98843077 0.98850404] test_f1_weighted [0.95075136 0.95242699 0.95244191 0.95232119 0.95163336 0.95167119 0.95415486 0.95319736 0.95310596 0.95241989] train_f1_weighted [0.988417 0.98841669 0.98842381 0.98848713 0.98851892 0.98861249 0.98832723 0.98847663 0.98842344 0.98849626]