📄 trainlog.txt_first-ordercrfs
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Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 37 Log-likelihood = -48.452183 Norm(log-likelihood gradient vector) = 4.542221 Norm(lambda vector) = 81.014945 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 527 504 95.64 95.27 95.45 o 725 748 716 95.72 98.76 97.22 i-np 691 670 648 96.72 93.78 95.22 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.02 95.94 95.98 Avg2. 1945 1945 1868 96.04 96.04 96.04 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 527 477 90.51 90.17 90.34 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.51 90.17 90.34 Avg2. 529 527 477 90.51 90.17 90.34 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 1 secondsIteration: 38 Log-likelihood = -48.713755 Norm(log-likelihood gradient vector) = 13.168265 Norm(lambda vector) = 81.462937 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 526 504 95.82 95.27 95.55 o 725 747 716 95.85 98.76 97.28 i-np 691 672 650 96.73 94.07 95.38 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.13 96.03 96.08 Avg2. 1945 1945 1870 96.14 96.14 96.14 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 526 478 90.87 90.36 90.62 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.87 90.36 90.62 Avg2. 529 526 478 90.87 90.36 90.62 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 39 Log-likelihood = -47.304424 Norm(log-likelihood gradient vector) = 6.223975 Norm(lambda vector) = 81.203148 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 527 505 95.83 95.46 95.64 o 725 747 716 95.85 98.76 97.28 i-np 691 671 650 96.87 94.07 95.45 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.18 96.10 96.14 Avg2. 1945 1945 1871 96.20 96.20 96.20 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 527 479 90.89 90.55 90.72 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.89 90.55 90.72 Avg2. 529 527 479 90.89 90.55 90.72 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 40 Log-likelihood = -45.938296 Norm(log-likelihood gradient vector) = 6.086881 Norm(lambda vector) = 79.984784 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 527 505 95.83 95.46 95.64 o 725 748 716 95.72 98.76 97.22 i-np 691 670 650 97.01 94.07 95.52 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.19 96.10 96.14 Avg2. 1945 1945 1871 96.20 96.20 96.20 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 527 480 91.08 90.74 90.91 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.08 90.74 90.91 Avg2. 529 527 480 91.08 90.74 90.91 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 41 Log-likelihood = -44.866614 Norm(log-likelihood gradient vector) = 2.881313 Norm(lambda vector) = 80.073707 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 529 507 95.84 95.84 95.84 o 725 750 718 95.73 99.03 97.36 i-np 691 666 650 97.60 94.07 95.80 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.39 96.31 96.35 Avg2. 1945 1945 1875 96.40 96.40 96.40 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 529 484 91.49 91.49 91.49 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.49 91.49 91.49 Avg2. 529 529 484 91.49 91.49 91.49 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 1 secondsIteration: 42 Log-likelihood = -44.306156 Norm(log-likelihood gradient vector) = 2.385597 Norm(lambda vector) = 80.390625 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 528 506 95.83 95.65 95.74 o 725 751 718 95.61 99.03 97.29 i-np 691 666 650 97.60 94.07 95.80 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.35 96.25 96.30 Avg2. 1945 1945 1874 96.35 96.35 96.35 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 528 483 91.48 91.30 91.39 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.48 91.30 91.39 Avg2. 529 528 483 91.48 91.30 91.39 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 43 Log-likelihood = -43.256016 Norm(log-likelihood gradient vector) = 4.932125 Norm(lambda vector) = 79.072884 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 528 507 96.02 95.84 95.93 o 725 750 718 95.73 99.03 97.36 i-np 691 667 651 97.60 94.21 95.88 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.45 96.36 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 529 528 484 91.67 91.49 91.58 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.67 91.49 91.58 Avg2. 529 528 484 91.67 91.49 91.58 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 44 Log-likelihood = -42.478084 Norm(log-likelihood gradient vector) = 4.465583 Norm(lambda vector) = 78.052156 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 529 508 96.03 96.03 96.03 o 725 750 719 95.87 99.17 97.49 i-np 691 666 651 97.75 94.21 95.95 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.55 96.47 96.51 Avg2. 1945 1945 1878 96.56 96.56 96.56 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 529 486 91.87 91.87 91.87 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.87 91.87 91.87 Avg2. 529 529 486 91.87 91.87 91.87 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 1 secondsIteration: 45 Log-likelihood = -41.763273 Norm(log-likelihood gradient vector) = 2.615761 Norm(lambda vector) = 77.633419 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 528 506 95.83 95.65 95.74 o 725 750 718 95.73 99.03 97.36 i-np 691 667 650 97.45 94.07 95.73 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.34 96.25 96.30 Avg2. 1945 1945 1874 96.35 96.35 96.35 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 528 483 91.48 91.30 91.39 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.48 91.30 91.39 Avg2. 529 528 483 91.48 91.30 91.39 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 46 Log-likelihood = -41.026387 Norm(log-likelihood gradient vector) = 2.275486 Norm(lambda vector) = 76.554756 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 528 506 95.83 95.65 95.74 o 725 750 718 95.73 99.03 97.36 i-np 691 667 650 97.45 94.07 95.73 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.34 96.25 96.30 Avg2. 1945 1945 1874 96.35 96.35 96.35 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 528 483 91.48 91.30 91.39 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.48 91.30 91.39 Avg2. 529 528 483 91.48 91.30 91.39 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 47 Log-likelihood = -40.331006 Norm(log-likelihood gradient vector) = 2.024350 Norm(lambda vector) = 75.483616 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 526 505 96.01 95.46 95.73 o 725 749 718 95.86 99.03 97.42 i-np 691 670 651 97.16 94.21 95.66 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.34 96.24 96.29 Avg2. 1945 1945 1874 96.35 96.35 96.35 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 526 481 91.44 90.93 91.18 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.44 90.93 91.18 Avg2. 529 526 481 91.44 90.93 91.18 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 1 secondsIteration: 48 Log-likelihood = -39.439629 Norm(log-likelihood gradient vector) = 3.960332 Norm(lambda vector) = 73.540712 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 528 507 96.02 95.84 95.93 o 725 751 720 95.87 99.31 97.56 i-np 691 666 651 97.75 94.21 95.95 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.55 96.45 96.50 Avg2. 1945 1945 1878 96.56 96.56 96.56 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 528 483 91.48 91.30 91.39 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.48 91.30 91.39 Avg2. 529 528 483 91.48 91.30 91.39 Current max chunk-based F1: 91.89 (iteration 25) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 49 Log-likelihood = -38.894157 Norm(log-likelihood gradient vector) = 5.528328 Norm(lambda vector) = 72.833133 Iteration elapsed: 2 seconds Label-based performance evaluation:⌨️ 快捷键说明
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