📄 trainlog.txt_second-ordercrfs
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Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 37 Log-likelihood = -604.858986 Norm(log-likelihood gradient vector) = 43.884980 Norm(lambda vector) = 75.023551 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 535 513 95.89 96.98 96.43 o 725 752 724 96.28 99.86 98.04 i-np 691 658 649 98.63 93.92 96.22 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.93 96.92 96.93 Avg2. 1945 1945 1886 96.97 96.97 96.97 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 535 491 91.78 92.82 92.29 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.78 92.82 92.29 Avg2. 529 535 491 91.78 92.82 92.29 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 38 Log-likelihood = -570.632265 Norm(log-likelihood gradient vector) = 100.070121 Norm(lambda vector) = 77.750232 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 535 513 95.89 96.98 96.43 o 725 750 724 96.53 99.86 98.17 i-np 691 660 651 98.64 94.21 96.37 ----- ------ ----- ----- ------- ------- ------------- Avg1. 97.02 97.02 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 529 535 491 91.78 92.82 92.29 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.78 92.82 92.29 Avg2. 529 535 491 91.78 92.82 92.29 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 39 Log-likelihood = -534.737334 Norm(log-likelihood gradient vector) = 50.459825 Norm(lambda vector) = 80.568114 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 534 512 95.88 96.79 96.33 o 725 749 723 96.53 99.72 98.10 i-np 691 662 651 98.34 94.21 96.23 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.92 96.91 96.91 Avg2. 1945 1945 1886 96.97 96.97 96.97 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 534 489 91.57 92.44 92.00 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.57 92.44 92.00 Avg2. 529 534 489 91.57 92.44 92.00 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 40 Log-likelihood = -510.291837 Norm(log-likelihood gradient vector) = 35.333466 Norm(lambda vector) = 82.517685 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 535 512 95.70 96.79 96.24 o 725 749 723 96.53 99.72 98.10 i-np 691 661 650 98.34 94.07 96.15 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.86 96.86 96.86 Avg2. 1945 1945 1885 96.92 96.92 96.92 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 535 488 91.21 92.25 91.73 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.21 92.25 91.73 Avg2. 529 535 488 91.21 92.25 91.73 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 41 Log-likelihood = -482.098069 Norm(log-likelihood gradient vector) = 44.143191 Norm(lambda vector) = 84.617361 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 536 512 95.52 96.79 96.15 o 725 750 724 96.53 99.86 98.17 i-np 691 659 649 98.48 93.92 96.15 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.85 96.86 96.85 Avg2. 1945 1945 1885 96.92 96.92 96.92 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 536 487 90.86 92.06 91.46 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.86 92.06 91.46 Avg2. 529 536 487 90.86 92.06 91.46 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 42 Log-likelihood = -444.667969 Norm(log-likelihood gradient vector) = 47.538958 Norm(lambda vector) = 86.970268 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 535 510 95.33 96.41 95.86 o 725 748 722 96.52 99.59 98.03 i-np 691 662 649 98.04 93.92 95.93 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.63 96.64 96.63 Avg2. 1945 1945 1881 96.71 96.71 96.71 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 535 484 90.47 91.49 90.98 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.47 91.49 90.98 Avg2. 529 535 484 90.47 91.49 90.98 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 43 Log-likelihood = -399.478439 Norm(log-likelihood gradient vector) = 60.772111 Norm(lambda vector) = 90.582019 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 543 513 94.48 96.98 95.71 o 725 751 723 96.27 99.72 97.97 i-np 691 651 644 98.92 93.20 95.98 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.56 96.63 96.59 Avg2. 1945 1945 1880 96.66 96.66 96.66 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 543 489 90.06 92.44 91.23 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.06 92.44 91.23 Avg2. 529 543 489 90.06 92.44 91.23 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 44 Log-likelihood = -388.520884 Norm(log-likelihood gradient vector) = 120.279815 Norm(lambda vector) = 95.579563 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 538 510 94.80 96.41 95.60 o 725 749 721 96.26 99.45 97.83 i-np 691 658 646 98.18 93.49 95.77 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.41 96.45 96.43 Avg2. 1945 1945 1877 96.50 96.50 96.50 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 538 485 90.15 91.68 90.91 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.15 91.68 90.91 Avg2. 529 538 485 90.15 91.68 90.91 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 45 Log-likelihood = -379.851055 Norm(log-likelihood gradient vector) = 50.479547 Norm(lambda vector) = 93.049675 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 539 510 94.62 96.41 95.51 o 725 749 721 96.26 99.45 97.83 i-np 691 657 645 98.17 93.34 95.70 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.35 96.40 96.38 Avg2. 1945 1945 1876 96.45 96.45 96.45 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 539 484 89.80 91.49 90.64 ----- ------ ----- ----- ------- ------- ------------- Avg1. 89.80 91.49 90.64 Avg2. 529 539 484 89.80 91.49 90.64 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 46 Log-likelihood = -370.605261 Norm(log-likelihood gradient vector) = 31.917102 Norm(lambda vector) = 93.553564 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 537 509 94.79 96.22 95.50 o 725 748 721 96.39 99.45 97.90 i-np 691 660 646 97.88 93.49 95.63 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.35 96.39 96.37 Avg2. 1945 1945 1876 96.45 96.45 96.45 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 537 481 89.57 90.93 90.24 ----- ------ ----- ----- ------- ------- ------------- Avg1. 89.57 90.93 90.24 Avg2. 529 537 481 89.57 90.93 90.24 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 4 secondsIteration: 47 Log-likelihood = -364.241403 Norm(log-likelihood gradient vector) = 24.772455 Norm(lambda vector) = 93.167307 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 536 509 94.96 96.22 95.59 o 725 747 721 96.52 99.45 97.96 i-np 691 662 648 97.89 93.78 95.79 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.46 96.48 96.47 Avg2. 1945 1945 1878 96.56 96.56 96.56 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 536 481 89.74 90.93 90.33 ----- ------ ----- ----- ------- ------- ------------- Avg1. 89.74 90.93 90.33 Avg2. 529 536 481 89.74 90.93 90.33 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 48 Log-likelihood = -359.131350 Norm(log-likelihood gradient vector) = 27.887901 Norm(lambda vector) = 93.044991 Iteration elapsed: 3 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 532 508 95.49 96.03 95.76 o 725 744 720 96.77 99.31 98.03 i-np 691 669 653 97.61 94.50 96.03 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.62 96.61 96.62 Avg2. 1945 1945 1881 96.71 96.71 96.71 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 532 482 90.60 91.12 90.86 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.60 91.12 90.86 Avg2. 529 532 482 90.60 91.12 90.86 Current max chunk-based F1: 93.13 (iteration 31) Training iteration elapsed (including testing & evaluation time): 3 secondsIteration: 49 Log-likelihood = -347.267440 Norm(log-likelihood gradient vector) = 27.161328 Norm(lambda vector) = 94.038434 Iteration elapsed: 3 seconds Label-based performance evaluation:⌨️ 快捷键说明
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