📄 trainlog.txt
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----- ------ ----- ----- ------- ------- ------------- b-np 529 515 500 97.09 94.52 95.79 o 725 742 716 96.50 98.76 97.61 i-np 691 688 663 96.37 95.95 96.16 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.65 96.41 96.53 Avg2. 1945 1945 1879 96.61 96.61 96.61 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 515 475 92.23 89.79 91.00 ----- ------ ----- ----- ------- ------- ------------- Avg1. 92.23 89.79 91.00 Avg2. 529 515 475 92.23 89.79 91.00 Current max chunk-based F1: 91.00 (iteration 12) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 13 Log-likelihood = -2874.128684 Norm(log-likelihood gradient vector) = 571.084534 Norm(lambda vector) = 27.459907 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 524 506 96.56 95.65 96.11 o 725 749 720 96.13 99.31 97.69 i-np 691 672 657 97.77 95.08 96.40 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.82 96.68 96.75 Avg2. 1945 1945 1883 96.81 96.81 96.81 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 524 483 92.18 91.30 91.74 ----- ------ ----- ----- ------- ------- ------------- Avg1. 92.18 91.30 91.74 Avg2. 529 524 483 92.18 91.30 91.74 Current max chunk-based F1: 91.74 (iteration 13) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 14 Log-likelihood = -2727.109929 Norm(log-likelihood gradient vector) = 290.827106 Norm(lambda vector) = 28.536432 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 527 509 96.58 96.22 96.40 o 725 749 721 96.26 99.45 97.83 i-np 691 669 658 98.36 95.22 96.76 ----- ------ ----- ----- ------- ------- ------------- Avg1. 97.07 96.96 97.02 Avg2. 1945 1945 1888 97.07 97.07 97.07 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 527 489 92.79 92.44 92.61 ----- ------ ----- ----- ------- ------- ------------- Avg1. 92.79 92.44 92.61 Avg2. 529 527 489 92.79 92.44 92.61 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 15 Log-likelihood = -2610.850678 Norm(log-likelihood gradient vector) = 192.260821 Norm(lambda vector) = 29.748733 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 530 509 96.04 96.22 96.13 o 725 752 721 95.88 99.45 97.63 i-np 691 663 652 98.34 94.36 96.31 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.75 96.67 96.71 Avg2. 1945 1945 1882 96.76 96.76 96.76 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 530 487 91.89 92.06 91.97 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.89 92.06 91.97 Avg2. 529 530 487 91.89 92.06 91.97 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 16 Log-likelihood = -2495.206889 Norm(log-likelihood gradient vector) = 208.792291 Norm(lambda vector) = 31.031253 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 530 508 95.85 96.03 95.94 o 725 755 721 95.50 99.45 97.43 i-np 691 660 647 98.03 93.63 95.78 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.46 96.37 96.41 Avg2. 1945 1945 1876 96.45 96.45 96.45 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 530 481 90.75 90.93 90.84 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.75 90.93 90.84 Avg2. 529 530 481 90.75 90.93 90.84 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 17 Log-likelihood = -2262.306669 Norm(log-likelihood gradient vector) = 190.425904 Norm(lambda vector) = 33.900238 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 521 501 96.16 94.71 95.43 o 725 752 718 95.48 99.03 97.22 i-np 691 672 650 96.73 94.07 95.38 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.12 95.94 96.03 Avg2. 1945 1945 1869 96.09 96.09 96.09 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 521 472 90.60 89.22 89.90 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.60 89.22 89.90 Avg2. 529 521 472 90.60 89.22 89.90 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 18 Log-likelihood = -2111.935104 Norm(log-likelihood gradient vector) = 582.050917 Norm(lambda vector) = 41.183676 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 525 505 96.19 95.46 95.83 o 725 752 719 95.61 99.17 97.36 i-np 691 668 649 97.16 93.92 95.51 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.32 96.19 96.25 Avg2. 1945 1945 1873 96.30 96.30 96.30 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 525 476 90.67 89.98 90.32 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.67 89.98 90.32 Avg2. 529 525 476 90.67 89.98 90.32 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 19 Log-likelihood = -1813.840084 Norm(log-likelihood gradient vector) = 189.367128 Norm(lambda vector) = 42.420653 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 525 506 96.38 95.65 96.02 o 725 749 719 95.99 99.17 97.56 i-np 691 671 652 97.17 94.36 95.74 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.51 96.39 96.45 Avg2. 1945 1945 1877 96.50 96.50 96.50 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 525 476 90.67 89.98 90.32 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.67 89.98 90.32 Avg2. 529 525 476 90.67 89.98 90.32 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 20 Log-likelihood = -1725.977139 Norm(log-likelihood gradient vector) = 122.741876 Norm(lambda vector) = 43.414242 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 523 505 96.56 95.46 96.01 o 725 745 718 96.38 99.03 97.69 i-np 691 677 656 96.90 94.93 95.91 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.61 96.48 96.54 Avg2. 1945 1945 1879 96.61 96.61 96.61 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 523 476 91.01 89.98 90.49 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.01 89.98 90.49 Avg2. 529 523 476 91.01 89.98 90.49 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 21 Log-likelihood = -1604.698049 Norm(log-likelihood gradient vector) = 112.755720 Norm(lambda vector) = 45.683589 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 526 508 96.58 96.03 96.30 o 725 746 721 96.65 99.45 98.03 i-np 691 673 658 97.77 95.22 96.48 ----- ------ ----- ----- ------- ------- ------------- Avg1. 97.00 96.90 96.95 Avg2. 1945 1945 1887 97.02 97.02 97.02 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 526 482 91.63 91.12 91.37 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.63 91.12 91.37 Avg2. 529 526 482 91.63 91.12 91.37 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 22 Log-likelihood = -1483.869995 Norm(log-likelihood gradient vector) = 143.153108 Norm(lambda vector) = 48.646591 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 535 516 96.45 97.54 96.99 o 725 745 722 96.91 99.59 98.23 i-np 691 665 656 98.65 94.93 96.76 ----- ------ ----- ----- ------- ------- ------------- Avg1. 97.34 97.35 97.35 Avg2. 1945 1945 1894 97.38 97.38 97.38 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 535 492 91.96 93.01 92.48 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.96 93.01 92.48 Avg2. 529 535 492 91.96 93.01 92.48 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 23 Log-likelihood = -1331.861185 Norm(log-likelihood gradient vector) = 172.722852 Norm(lambda vector) = 54.533690 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 532 515 96.80 97.35 97.08 o 725 743 722 97.17 99.59 98.37 i-np 691 670 659 98.36 95.37 96.84 ----- ------ ----- ----- ------- ------- ------------- Avg1. 97.45 97.44 97.44 Avg2. 1945 1945 1896 97.48 97.48 97.48 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 532 490 92.11 92.63 92.37 ----- ------ ----- ----- ------- ------- ------------- Avg1. 92.11 92.63 92.37 Avg2. 529 532 490 92.11 92.63 92.37 Current max chunk-based F1: 92.61 (iteration 14) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 24 Log-likelihood = -1212.661370 Norm(log-likelihood gradient vector) = 126.200371 Norm(lambda vector) = 59.788284 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 532 516 96.99 97.54 97.27 o 725 744 723 97.18 99.72 98.43 i-np 691 669 660 98.65 95.51 97.06 ----- ------ ----- ----- ------- ------- ------------- Avg1. 97.61 97.59 97.60 Avg2. 1945 1945 1899 97.63 97.63 97.63 Chunk-based performance evaluation:⌨️ 快捷键说明
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