📄 trainlog.txt_first-ordercrfs
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----- ------ ----- ----- ------- ------- ------------- b-np 529 522 504 96.55 95.27 95.91 o 725 747 719 96.25 99.17 97.69 i-np 691 676 656 97.04 94.93 95.98 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.61 96.46 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 529 522 474 90.80 89.60 90.20 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.80 89.60 90.20 Avg2. 529 522 474 90.80 89.60 90.20 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 13 Log-likelihood = -524.271758 Norm(log-likelihood gradient vector) = 85.863864 Norm(lambda vector) = 33.640256 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 522 501 95.98 94.71 95.34 o 725 749 718 95.86 99.03 97.42 i-np 691 674 653 96.88 94.50 95.68 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.24 96.08 96.16 Avg2. 1945 1945 1872 96.25 96.25 96.25 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 522 472 90.42 89.22 89.82 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.42 89.22 89.82 Avg2. 529 522 472 90.42 89.22 89.82 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 14 Log-likelihood = -468.573906 Norm(log-likelihood gradient vector) = 84.256056 Norm(lambda vector) = 35.416702 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 517 499 96.52 94.33 95.41 o 725 739 714 96.62 98.48 97.54 i-np 691 689 658 95.50 95.22 95.36 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.21 96.01 96.11 Avg2. 1945 1945 1871 96.20 96.20 96.20 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 517 467 90.33 88.28 89.29 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.33 88.28 89.29 Avg2. 529 517 467 90.33 88.28 89.29 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 1 secondsIteration: 15 Log-likelihood = -381.496879 Norm(log-likelihood gradient vector) = 159.889744 Norm(lambda vector) = 40.186470 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 529 498 94.14 94.14 94.14 o 725 761 719 94.48 99.17 96.77 i-np 691 655 638 97.40 92.33 94.80 ----- ------ ----- ----- ------- ------- ------------- Avg1. 95.34 95.21 95.28 Avg2. 1945 1945 1855 95.37 95.37 95.37 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 529 471 89.04 89.04 89.04 ----- ------ ----- ----- ------- ------- ------------- Avg1. 89.04 89.04 89.04 Avg2. 529 529 471 89.04 89.04 89.04 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 16 Log-likelihood = -331.494469 Norm(log-likelihood gradient vector) = 123.847229 Norm(lambda vector) = 44.152125 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 527 502 95.26 94.90 95.08 o 725 754 719 95.36 99.17 97.23 i-np 691 664 646 97.29 93.49 95.35 ----- ------ ----- ----- ------- ------- ------------- Avg1. 95.97 95.85 95.91 Avg2. 1945 1945 1867 95.99 95.99 95.99 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 527 475 90.13 89.79 89.96 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.13 89.79 89.96 Avg2. 529 527 475 90.13 89.79 89.96 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 17 Log-likelihood = -296.018906 Norm(log-likelihood gradient vector) = 73.589532 Norm(lambda vector) = 43.793367 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 526 501 95.25 94.71 94.98 o 725 751 716 95.34 98.76 97.02 i-np 691 668 646 96.71 93.49 95.07 ----- ------ ----- ----- ------- ------- ------------- Avg1. 95.76 95.65 95.71 Avg2. 1945 1945 1863 95.78 95.78 95.78 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 526 472 89.73 89.22 89.48 ----- ------ ----- ----- ------- ------- ------------- Avg1. 89.73 89.22 89.48 Avg2. 529 526 472 89.73 89.22 89.48 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 18 Log-likelihood = -250.904825 Norm(log-likelihood gradient vector) = 47.837607 Norm(lambda vector) = 45.524808 Iteration elapsed: 1 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 725 750 717 95.60 98.90 97.22 i-np 691 669 648 96.86 93.78 95.29 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.09 95.98 96.04 Avg2. 1945 1945 1869 96.09 96.09 96.09 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 526 475 90.30 89.79 90.05 ----- ------ ----- ----- ------- ------- ------------- Avg1. 90.30 89.79 90.05 Avg2. 529 526 475 90.30 89.79 90.05 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 1 secondsIteration: 19 Log-likelihood = -209.967747 Norm(log-likelihood gradient vector) = 44.740460 Norm(lambda vector) = 48.899654 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 521 502 96.35 94.90 95.62 o 725 752 716 95.21 98.76 96.95 i-np 691 672 651 96.88 94.21 95.52 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.15 95.96 96.05 Avg2. 1945 1945 1869 96.09 96.09 96.09 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 521 476 91.36 89.98 90.67 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.36 89.98 90.67 Avg2. 529 521 476 91.36 89.98 90.67 Current max chunk-based F1: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 20 Log-likelihood = -194.998161 Norm(log-likelihood gradient vector) = 131.106178 Norm(lambda vector) = 55.938246 Iteration elapsed: 2 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 725 756 720 95.24 99.31 97.23 i-np 691 663 648 97.74 93.78 95.72 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.33 96.18 96.26 Avg2. 1945 1945 1873 96.30 96.30 96.30 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: 90.70 (iteration 9) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 21 Log-likelihood = -150.581933 Norm(log-likelihood gradient vector) = 35.885017 Norm(lambda vector) = 57.389940 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 527 506 96.02 95.65 95.83 o 725 755 720 95.36 99.31 97.30 i-np 691 663 648 97.74 93.78 95.72 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.37 96.25 96.31 Avg2. 1945 1945 1874 96.35 96.35 96.35 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: 90.91 (iteration 21) Training iteration elapsed (including testing & evaluation time): 1 secondsIteration: 22 Log-likelihood = -140.225225 Norm(log-likelihood gradient vector) = 26.125514 Norm(lambda vector) = 58.389717 Iteration elapsed: 2 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 527 506 96.02 95.65 95.83 o 725 754 720 95.49 99.31 97.36 i-np 691 664 649 97.74 93.92 95.79 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.42 96.29 96.36 Avg2. 1945 1945 1875 96.40 96.40 96.40 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 527 482 91.46 91.12 91.29 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.46 91.12 91.29 Avg2. 529 527 482 91.46 91.12 91.29 Current max chunk-based F1: 91.29 (iteration 22) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 23 Log-likelihood = -120.704597 Norm(log-likelihood gradient vector) = 27.785201 Norm(lambda vector) = 61.122523 Iteration elapsed: 2 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 725 755 720 95.36 99.31 97.30 i-np 691 665 650 97.74 94.07 95.87 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.43 96.28 96.36 Avg2. 1945 1945 1875 96.40 96.40 96.40 Chunk-based performance evaluation: Chunk Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- np 529 525 482 91.81 91.12 91.46 ----- ------ ----- ----- ------- ------- ------------- Avg1. 91.81 91.12 91.46 Avg2. 529 525 482 91.81 91.12 91.46 Current max chunk-based F1: 91.46 (iteration 23) Training iteration elapsed (including testing & evaluation time): 2 secondsIteration: 24 Log-likelihood = -100.218394 Norm(log-likelihood gradient vector) = 23.887901 Norm(lambda vector) = 65.490397 Iteration elapsed: 1 seconds Label-based performance evaluation: Label Manual Model Match Pre.(%) Rec.(%) F1-Measure(%) ----- ------ ----- ----- ------- ------- ------------- b-np 529 537 510 94.97 96.41 95.68 o 725 761 722 94.88 99.59 97.17 i-np 691 647 638 98.61 92.33 95.37 ----- ------ ----- ----- ------- ------- ------------- Avg1. 96.15 96.11 96.13 Avg2. 1945 1945 1870 96.14 96.14 96.14 Chunk-based performance evaluation:⌨️ 快捷键说明
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