 | Run 2508 |
| Creator | internal |
| Program | crfpp |
| Dataset | ner-conll2003-deu |
| Task type | SequenceTagging |
| Created | 1y122d ago |
Done! 
| 26m28s | |
| 1478M | |
| SequenceTagging | |
| 1.000 | |
| 0.998 | |
| 0.934 | |
| 0.457 | |
Log file
===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train CRF++: Yet Another CRF Tool Kit Copyright (C) 2005-2009 Taku Kudo, All rights reserved. reading training data: 100.. 200.. 300.. 400.. 500.. 600.. 700.. 800.. 900.. 1000.. 1100.. 1200.. 1300.. 1400.. 1500.. 1600.. 1700.. 1800.. 1900.. 2000.. 2100.. 2200.. 2300.. 2400.. 2500.. 2600.. 2700.. 2800.. 2900.. 3000.. 3100.. 3200.. 3300.. 3400.. 3500.. 3600.. 3700.. 3800.. 3900.. 4000.. 4100.. 4200.. 4300.. 4400.. 4500.. 4600.. 4700.. 4800.. 4900.. 5000.. 5100.. 5200.. 5300.. 5400.. 5500.. 5600.. 5700.. 5800.. 5900.. 6000.. 6100.. 6200.. 6300.. 6400.. 6500.. 6600.. 6700.. 6800.. 6900.. 7000.. 7100.. 7200.. 7300.. 7400.. 7500.. 7600.. 7700.. 7800.. 7900.. 8000.. 8100.. 8200.. 8300.. 8400.. 8500.. 8600.. 8700.. 8800.. 8900.. 9000.. 9100.. 9200.. 9300.. 9400.. 9500.. 9600.. 9700.. 9800.. 9900.. 10000.. 10100.. 10200.. 10300.. 10400.. 10500.. 10600.. 10700.. 10800.. 10900.. 11000.. 11100.. 11200.. 11300.. 11400.. 11500.. 11600.. 11700.. 11800.. 11900.. 12000.. 12100.. 12200.. 12300.. 12400.. 12500.. 12600.. 12700.. Done!13.20 s Number of sentences: 12705 Number of features: 12434166 Number of thread(s): 1 Freq: 1 eta: 0.00010 C: 1.00000 shrinking size: 20 iter=0 terr=0.97897 serr=0.99953 act=12434166 obj=455888.94420 diff=1.00000 iter=1 terr=0.08089 serr=0.50350 act=12434166 obj=276827.03066 diff=0.39278 iter=2 terr=0.08089 serr=0.50350 act=12434166 obj=136036.76601 diff=0.50859 iter=3 terr=0.08089 serr=0.50350 act=12434166 obj=116481.42487 diff=0.14375 iter=4 terr=0.08089 serr=0.50350 act=12434166 obj=149465.64497 diff=0.28317 iter=5 terr=0.08089 serr=0.50350 act=12434166 obj=87344.78995 diff=0.41562 iter=6 terr=0.35546 serr=0.92656 act=12434166 obj=280175.47747 diff=2.20770 iter=7 terr=0.08089 serr=0.50350 act=12434166 obj=83675.17824 diff=0.70135 iter=8 terr=0.08089 serr=0.50350 act=12434166 obj=79353.84808 diff=0.05164 iter=9 terr=0.08089 serr=0.50350 act=12434166 obj=75286.22371 diff=0.05126 iter=10 terr=0.08081 serr=0.50641 act=12434166 obj=67871.77824 diff=0.09848 iter=11 terr=0.07967 serr=0.50586 act=12434166 obj=59612.46315 diff=0.12169 iter=12 terr=0.08111 serr=0.50870 act=12434166 obj=56297.67572 diff=0.05561 iter=13 terr=0.07690 serr=0.50893 act=12434166 obj=48611.34635 diff=0.13653 iter=14 terr=0.07803 serr=0.51389 act=12434166 obj=47392.21688 diff=0.02508 iter=15 terr=0.07246 serr=0.49414 act=12434166 obj=44058.80372 diff=0.07034 iter=16 terr=0.07150 serr=0.48571 act=12434166 obj=43426.76770 diff=0.01435 iter=17 terr=0.07025 serr=0.47997 act=12434166 obj=42076.95147 diff=0.03108 iter=18 terr=0.06643 serr=0.45651 act=12434166 obj=39009.85539 diff=0.07289 iter=19 terr=0.06359 serr=0.44305 act=12434166 obj=36386.54802 diff=0.06725 iter=20 terr=0.05741 serr=0.41543 act=12434166 obj=33626.19331 diff=0.07586 iter=21 terr=0.05519 serr=0.40905 act=12434166 obj=31803.43287 diff=0.05421 iter=22 terr=0.06388 serr=0.44156 act=12434166 obj=36543.19856 diff=0.14903 iter=23 terr=0.05370 serr=0.40425 act=12434166 obj=31115.03480 diff=0.14854 iter=24 terr=0.05350 serr=0.40795 act=12434166 obj=29864.20493 diff=0.04020 iter=25 terr=0.05224 serr=0.40268 act=12434166 obj=29040.39844 diff=0.02759 iter=26 terr=0.04965 serr=0.38819 act=12434166 obj=27699.79682 diff=0.04616 iter=27 terr=0.04628 serr=0.36993 act=12434166 obj=26263.98202 diff=0.05183 iter=28 terr=0.04227 serr=0.34687 act=12434166 obj=24344.90213 diff=0.07307 iter=29 terr=0.05310 serr=0.38607 act=12434166 obj=32556.75757 diff=0.33731 iter=30 terr=0.04210 serr=0.34420 act=12434166 obj=23930.88729 diff=0.26495 iter=31 terr=0.03755 serr=0.32161 act=12434166 obj=22407.53379 diff=0.06366 iter=32 terr=0.03564 serr=0.31436 act=12434166 obj=21688.32130 diff=0.03210 iter=33 terr=0.03327 serr=0.30012 act=12434166 obj=20698.72655 diff=0.04563 iter=34 terr=0.03185 serr=0.29075 act=12434166 obj=19767.08566 diff=0.04501 iter=35 terr=0.03009 serr=0.27824 act=12434166 obj=18896.81647 diff=0.04403 iter=36 terr=0.02946 serr=0.27485 act=12434166 obj=18296.74245 diff=0.03176 iter=37 terr=0.02745 serr=0.26029 act=12434166 obj=17618.45638 diff=0.03707 iter=38 terr=0.02287 serr=0.22700 act=12434166 obj=16362.90419 diff=0.07126 iter=39 terr=0.02052 serr=0.20913 act=12434166 obj=15760.14576 diff=0.03684 iter=40 terr=0.01761 serr=0.18599 act=12434166 obj=14848.97022 diff=0.05782 iter=41 terr=0.01544 serr=0.17080 act=12434166 obj=14079.01407 diff=0.05185 iter=42 terr=0.01352 serr=0.15427 act=12434166 obj=13289.05718 diff=0.05611 iter=43 terr=0.01208 serr=0.13963 act=12434166 obj=13022.81424 diff=0.02003 iter=44 terr=0.01322 serr=0.15136 act=12434166 obj=12787.63406 diff=0.01806 iter=45 terr=0.01268 serr=0.14679 act=12434166 obj=12695.15931 diff=0.00723 iter=46 terr=0.01170 serr=0.13806 act=12434166 obj=12501.71181 diff=0.01524 iter=47 terr=0.01165 serr=0.13750 act=12434166 obj=12388.40470 diff=0.00906 iter=48 terr=0.01086 serr=0.13184 act=12434166 obj=11991.19086 diff=0.03206 iter=49 terr=0.01116 serr=0.13506 act=12434166 obj=11952.67561 diff=0.00321 iter=50 terr=0.01066 serr=0.13026 act=12434166 obj=11708.21923 diff=0.02045 iter=51 terr=0.00970 serr=0.12113 act=12434166 obj=11414.73552 diff=0.02507 iter=52 terr=0.00965 serr=0.12050 act=12434166 obj=11177.04530 diff=0.02082 iter=53 terr=0.00629 serr=0.08249 act=12434166 obj=11262.94072 diff=0.00768 iter=54 terr=0.00796 serr=0.10248 act=12434166 obj=11065.15881 diff=0.01756 iter=55 terr=0.00782 serr=0.10122 act=12434166 obj=10905.21825 diff=0.01445 iter=56 terr=0.00748 serr=0.09626 act=12434166 obj=10718.10312 diff=0.01716 iter=57 terr=0.00698 serr=0.09044 act=12434166 obj=10518.06116 diff=0.01866 iter=58 terr=0.00586 serr=0.07824 act=12434166 obj=10142.96915 diff=0.03566 iter=59 terr=0.00733 serr=0.09185 act=12434166 obj=10524.78869 diff=0.03764 iter=60 terr=0.00557 serr=0.07532 act=12434166 obj=9977.01785 diff=0.05205 iter=61 terr=0.00404 serr=0.05478 act=12434166 obj=9724.70996 diff=0.02529 iter=62 terr=0.00476 serr=0.06179 act=12434166 obj=9585.99001 diff=0.01426 iter=63 terr=0.00313 serr=0.04140 act=12434166 obj=9508.75331 diff=0.00806 iter=64 terr=0.00339 serr=0.04557 act=12434166 obj=9461.79554 diff=0.00494 iter=65 terr=0.00371 serr=0.05053 act=12434166 obj=9403.50495 diff=0.00616 iter=66 terr=0.00379 serr=0.05250 act=12434166 obj=9329.07691 diff=0.00791 iter=67 terr=0.00372 serr=0.05171 act=12434166 obj=9224.16267 diff=0.01125 iter=68 terr=0.00308 serr=0.04329 act=12434166 obj=9065.72272 diff=0.01718 iter=69 terr=0.00343 serr=0.04589 act=12434166 obj=8990.79335 diff=0.00827 iter=70 terr=0.00190 serr=0.02519 act=12434166 obj=8859.64969 diff=0.01459 iter=71 terr=0.00184 serr=0.02534 act=12434166 obj=8770.13857 diff=0.01010 iter=72 terr=0.00168 serr=0.02322 act=12434166 obj=8694.86596 diff=0.00858 iter=73 terr=0.00144 serr=0.01999 act=12434166 obj=8634.60757 diff=0.00693 iter=74 terr=0.00237 serr=0.03337 act=12434166 obj=8974.15765 diff=0.03932 iter=75 terr=0.00141 serr=0.01952 act=12434166 obj=8596.94592 diff=0.04203 iter=76 terr=0.00133 serr=0.01787 act=12434166 obj=8550.18262 diff=0.00544 iter=77 terr=0.00111 serr=0.01543 act=12434166 obj=8453.98656 diff=0.01125 iter=78 terr=0.00126 serr=0.01362 act=12434166 obj=9996.80799 diff=0.18250 iter=79 terr=0.00094 serr=0.01283 act=12434166 obj=8430.84993 diff=0.15665 iter=80 terr=0.00089 serr=0.01220 act=12434166 obj=8396.75717 diff=0.00404 iter=81 terr=0.00084 serr=0.01181 act=12434166 obj=8278.70654 diff=0.01406 iter=82 terr=0.00080 serr=0.01118 act=12434166 obj=8193.05216 diff=0.01035 iter=83 terr=0.00084 serr=0.01165 act=12434166 obj=8116.41060 diff=0.00935 iter=84 terr=0.00077 serr=0.01047 act=12434166 obj=8096.93252 diff=0.00240 iter=85 terr=0.00073 serr=0.01039 act=12434166 obj=8030.66730 diff=0.00818 iter=86 terr=0.00062 serr=0.00882 act=12434166 obj=8012.17171 diff=0.00230 iter=87 terr=0.00062 serr=0.00882 act=12434166 obj=7992.50416 diff=0.00245 iter=88 terr=0.00061 serr=0.00874 act=12434166 obj=7967.10390 diff=0.00318 iter=89 terr=0.00057 serr=0.00834 act=12434166 obj=7941.51816 diff=0.00321 iter=90 terr=0.00053 serr=0.00771 act=12434166 obj=7894.00483 diff=0.00598 iter=91 terr=0.00053 serr=0.00763 act=12434166 obj=7852.42204 diff=0.00527 iter=92 terr=0.00045 serr=0.00622 act=12434166 obj=7853.00126 diff=0.00007 iter=93 terr=0.00043 serr=0.00614 act=12434166 obj=7827.97400 diff=0.00319 iter=94 terr=0.00045 serr=0.00638 act=12434166 obj=7797.67137 diff=0.00387 iter=95 terr=0.00041 serr=0.00567 act=12434166 obj=7800.82879 diff=0.00040 iter=96 terr=0.00045 serr=0.00630 act=12434166 obj=7787.70159 diff=0.00168 iter=97 terr=0.00043 serr=0.00606 act=12434166 obj=7769.50495 diff=0.00234 iter=98 terr=0.00041 serr=0.00575 act=12434166 obj=7748.87361 diff=0.00266 iter=99 terr=0.00038 serr=0.00512 act=12434166 obj=7718.68878 diff=0.00390 iter=100 terr=0.00035 serr=0.00472 act=12434166 obj=7690.41761 diff=0.00366 iter=101 terr=0.00034 serr=0.00441 act=12434166 obj=7682.35350 diff=0.00105 iter=102 terr=0.00031 serr=0.00409 act=12434166 obj=7652.94543 diff=0.00383 iter=103 terr=0.00031 serr=0.00425 act=12434166 obj=7642.69051 diff=0.00134 iter=104 terr=0.00031 serr=0.00425 act=12434166 obj=7635.05400 diff=0.00100 iter=105 terr=0.00028 serr=0.00386 act=12434166 obj=7619.33018 diff=0.00206 iter=106 terr=0.00027 serr=0.00362 act=12434166 obj=7592.23000 diff=0.00356 iter=107 terr=0.00025 serr=0.00323 act=12434166 obj=7564.60435 diff=0.00364 iter=108 terr=0.00032 serr=0.00433 act=12434166 obj=7616.72317 diff=0.00689 iter=109 terr=0.00026 serr=0.00338 act=12434166 obj=7558.17167 diff=0.00769 iter=110 terr=0.00025 serr=0.00315 act=12434166 obj=7545.68989 diff=0.00165 iter=111 terr=0.00023 serr=0.00283 act=12434166 obj=7536.95306 diff=0.00116 iter=112 terr=0.00023 serr=0.00291 act=12434166 obj=7521.67340 diff=0.00203 iter=113 terr=0.00022 serr=0.00275 act=12434166 obj=7513.68321 diff=0.00106 iter=114 terr=0.00024 serr=0.00299 act=12434166 obj=7506.80547 diff=0.00092 iter=115 terr=0.00024 serr=0.00299 act=12434166 obj=7503.80899 diff=0.00040 iter=116 terr=0.00024 serr=0.00299 act=12434166 obj=7497.21435 diff=0.00088 iter=117 terr=0.00027 serr=0.00338 act=12434166 obj=7489.19495 diff=0.00107 iter=118 terr=0.00026 serr=0.00338 act=12434166 obj=7485.35847 diff=0.00051 iter=119 terr=0.00026 serr=0.00331 act=12434166 obj=7478.95463 diff=0.00086 iter=120 terr=0.00023 serr=0.00283 act=12434166 obj=7475.69459 diff=0.00044 iter=121 terr=0.00023 serr=0.00283 act=12434166 obj=7472.60482 diff=0.00041 iter=122 terr=0.00023 serr=0.00283 act=12434166 obj=7465.21324 diff=0.00099 iter=123 terr=0.00024 serr=0.00299 act=12434166 obj=7507.03838 diff=0.00560 iter=124 terr=0.00023 serr=0.00291 act=12434166 obj=7462.23380 diff=0.00597 iter=125 terr=0.00023 serr=0.00291 act=12434166 obj=7459.23941 diff=0.00040 iter=126 terr=0.00023 serr=0.00283 act=12434166 obj=7452.54976 diff=0.00090 iter=127 terr=0.00023 serr=0.00275 act=12434166 obj=7459.63358 diff=0.00095 iter=128 terr=0.00023 serr=0.00283 act=12434166 obj=7450.79469 diff=0.00118 iter=129 terr=0.00023 serr=0.00283 act=12434166 obj=7449.52529 diff=0.00017 iter=130 terr=0.00023 serr=0.00275 act=12434166 obj=7444.90981 diff=0.00062 iter=131 terr=0.00023 serr=0.00275 act=12434166 obj=7442.29257 diff=0.00035 iter=132 terr=0.00023 serr=0.00283 act=12434166 obj=7440.33072 diff=0.00026 iter=133 terr=0.00023 serr=0.00283 act=12434166 obj=7436.67963 diff=0.00049 iter=134 terr=0.00023 serr=0.00275 act=12434166 obj=7434.86224 diff=0.00024 iter=135 terr=0.00023 serr=0.00275 act=12434166 obj=7433.04429 diff=0.00024 iter=136 terr=0.00023 serr=0.00275 act=12434166 obj=7431.96413 diff=0.00015 iter=137 terr=0.00023 serr=0.00275 act=12434166 obj=7430.10300 diff=0.00025 iter=138 terr=0.00022 serr=0.00260 act=12434166 obj=7428.52048 diff=0.00021 iter=139 terr=0.00021 serr=0.00252 act=12434166 obj=7425.58958 diff=0.00039 iter=140 terr=0.00022 serr=0.00268 act=12434166 obj=7424.35142 diff=0.00017 iter=141 terr=0.00022 serr=0.00260 act=12434166 obj=7422.76589 diff=0.00021 iter=142 terr=0.00022 serr=0.00260 act=12434166 obj=7422.33675 diff=0.00006 iter=143 terr=0.00022 serr=0.00260 act=12434166 obj=7421.54218 diff=0.00011 iter=144 terr=0.00022 serr=0.00260 act=12434166 obj=7419.94357 diff=0.00022 iter=145 terr=0.00022 serr=0.00260 act=12434166 obj=7418.52705 diff=0.00019 iter=146 terr=0.00022 serr=0.00260 act=12434166 obj=7417.01193 diff=0.00020 iter=147 terr=0.00022 serr=0.00268 act=12434166 obj=7418.04052 diff=0.00014 iter=148 terr=0.00022 serr=0.00260 act=12434166 obj=7416.26432 diff=0.00024 iter=149 terr=0.00022 serr=0.00260 act=12434166 obj=7415.16088 diff=0.00015 iter=150 terr=0.00022 serr=0.00260 act=12434166 obj=7414.40442 diff=0.00010 iter=151 terr=0.00022 serr=0.00260 act=12434166 obj=7413.71880 diff=0.00009 iter=152 terr=0.00022 serr=0.00260 act=12434166 obj=7412.53776 diff=0.00016 iter=153 terr=0.00021 serr=0.00252 act=12434166 obj=7411.14207 diff=0.00019 iter=154 terr=0.00021 serr=0.00260 act=12434166 obj=7417.26143 diff=0.00083 iter=155 terr=0.00021 serr=0.00252 act=12434166 obj=7410.72906 diff=0.00088 iter=156 terr=0.00021 serr=0.00252 act=12434166 obj=7409.94841 diff=0.00011 iter=157 terr=0.00021 serr=0.00252 act=12434166 obj=7409.15935 diff=0.00011 iter=158 terr=0.00022 serr=0.00260 act=12434166 obj=7408.48869 diff=0.00009 iter=159 terr=0.00022 serr=0.00260 act=12434166 obj=7408.15752 diff=0.00004 iter=160 terr=0.00022 serr=0.00260 act=12434166 obj=7406.95362 diff=0.00016 iter=161 terr=0.00022 serr=0.00260 act=12434166 obj=7406.08689 diff=0.00012 iter=162 terr=0.00021 serr=0.00260 act=12434166 obj=7405.53950 diff=0.00007 iter=163 terr=0.00022 serr=0.00268 act=12434166 obj=7404.60398 diff=0.00013 iter=164 terr=0.00022 serr=0.00268 act=12434166 obj=7404.24468 diff=0.00005 iter=165 terr=0.00022 serr=0.00268 act=12434166 obj=7403.90988 diff=0.00005 iter=166 terr=0.00022 serr=0.00268 act=12434166 obj=7403.35660 diff=0.00007 Done!1591.68 s === END program1: ./run learn ../dataset2/train --- OK [1614s] ===== MAIN: predict/evaluate on train data ===== === START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in === END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [2s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [5s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [7s] ===== MAIN: predict/evaluate on test data ===== === START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in === END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 27m11.785s user 26m58.677s sys 0m4.292s
Run specification 
Results
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