random3_n5_exact

[1] ner train1:10.9405, rc_train1:1660.3686, ner train2:5.2971, rc_train2:401.0928, info:0.3474
[1] ner test1:4.0162, rc test1:11.5380, ner test2:3.1303, rc test2:3.2139, info:0.0406
[1] precision:0.6049, recall:0.5768, f1:0.5905
[*] saved best model!
[2] ner train1:3.8695, rc_train1:10.6512, ner train2:3.0501, rc_train2:3.0671, info:0.1066
[2] ner test1:3.5155, rc test1:9.8836, ner test2:2.7386, rc test2:2.6601, info:0.0385
[2] precision:0.6756, recall:0.6069, f1:0.6394
[*] saved best model!
[3] ner train1:3.5071, rc_train1:9.1854, ner train2:2.7639, rc_train2:2.6932, info:0.9813
[3] ner test1:3.3089, rc test1:8.9705, ner test2:2.5756, rc test2:2.4185, info:0.0485
[3] precision:0.7116, recall:0.6358, f1:0.6715
[*] saved best model!
[4] ner train1:3.2446, rc_train1:8.1241, ner train2:2.5489, rc_train2:2.3949, info:0.0910
[4] ner test1:3.0793, rc test1:8.6894, ner test2:2.3979, rc test2:2.2621, info:0.1348
[4] precision:0.8220, recall:0.5704, f1:0.6735
[*] saved best model!
[5] ner train1:3.0626, rc_train1:7.7879, ner train2:2.3634, rc_train2:2.1906, info:0.3520
[5] ner test1:3.0376, rc test1:7.8193, ner test2:2.3360, rc test2:2.0801, info:0.1947
[5] precision:0.7368, recall:0.6942, f1:0.7149
[*] saved best model!
[6] ner train1:2.9476, rc_train1:7.4423, ner train2:2.2696, rc_train2:2.0897, info:0.2041
[6] ner test1:2.9685, rc test1:7.9481, ner test2:2.3347, rc test2:2.0369, info:0.0957
[6] precision:0.7343, recall:0.7274, f1:0.7308
[*] saved best model!
[7] ner train1:2.8595, rc_train1:7.4646, ner train2:2.1852, rc_train2:2.0048, info:0.3271
[7] ner test1:2.9111, rc test1:7.7400, ner test2:2.2838, rc test2:2.0107, info:0.2403
[7] precision:0.7363, recall:0.7393, f1:0.7378
[*] saved best model!
[8] ner train1:2.8055, rc_train1:7.3213, ner train2:2.1364, rc_train2:1.9726, info:1.0591
[8] ner test1:2.8918, rc test1:7.5892, ner test2:2.3595, rc test2:2.0090, info:0.1393
[8] precision:0.7589, recall:0.7197, f1:0.7388
[*] saved best model!
[9] ner train1:2.7779, rc_train1:7.4435, ner train2:2.1150, rc_train2:1.9597, info:0.1570
[9] ner test1:2.8667, rc test1:7.7700, ner test2:2.1897, rc test2:1.8926, info:0.1427
[9] precision:0.7833, recall:0.7109, f1:0.7453
[*] saved best model!
[10] ner train1:2.6728, rc_train1:7.4315, ner train2:1.9887, rc_train2:1.8250, info:0.1478
[10] ner test1:2.8058, rc test1:7.2132, ner test2:2.1807, rc test2:1.9092, info:0.1474
[10] precision:0.7386, recall:0.7527, f1:0.7456
[*] saved best model!
[11] ner train1:2.6179, rc_train1:7.3999, ner train2:1.9179, rc_train2:1.7575, info:0.5232
[11] ner test1:3.1082, rc test1:7.9681, ner test2:2.1542, rc test2:1.8552, info:0.0580
[11] precision:0.7895, recall:0.7181, f1:0.7521
[*] saved best model!
[12] ner train1:2.5640, rc_train1:7.3532, ner train2:1.8558, rc_train2:1.6923, info:0.1316
[12] ner test1:2.8299, rc test1:8.0360, ner test2:2.1556, rc test2:1.8492, info:0.0677
[12] precision:0.7438, recall:0.7530, f1:0.7484
[13] ner train1:2.4848, rc_train1:7.4859, ner train2:1.7570, rc_train2:1.6056, info:0.2315
[13] ner test1:2.7716, rc test1:8.4509, ner test2:2.0945, rc test2:1.7503, info:0.1430
[13] precision:0.7976, recall:0.7277, f1:0.7611
[*] saved best model!
[14] ner train1:2.4201, rc_train1:7.4883, ner train2:1.6701, rc_train2:1.4912, info:0.8009
[14] ner test1:2.7270, rc test1:7.5527, ner test2:2.0664, rc test2:1.7146, info:0.0389
[14] precision:0.7826, recall:0.7668, f1:0.7746
[*] saved best model!
[15] ner train1:2.3708, rc_train1:7.5590, ner train2:1.5982, rc_train2:1.4361, info:0.1088
[15] ner test1:2.7208, rc test1:7.7435, ner test2:2.0967, rc test2:1.7427, info:0.0707
[15] precision:0.7621, recall:0.7818, f1:0.7718
[16] ner train1:2.3496, rc_train1:7.4758, ner train2:1.5481, rc_train2:1.3831, info:0.0561
[16] ner test1:2.7386, rc test1:7.7018, ner test2:2.0417, rc test2:1.6566, info:0.0461
[16] precision:0.7833, recall:0.7861, f1:0.7847
[*] saved best model!
[17] ner train1:2.2927, rc_train1:7.4984, ner train2:1.4644, rc_train2:1.2966, info:1.1971
[17] ner test1:2.6819, rc test1:8.3124, ner test2:2.0535, rc test2:1.6393, info:0.0418
[17] precision:0.7944, recall:0.7654, f1:0.7796
[18] ner train1:2.2555, rc_train1:7.5662, ner train2:1.4128, rc_train2:1.2441, info:0.1799
[18] ner test1:2.7128, rc test1:7.9734, ner test2:2.0324, rc test2:1.6041, info:0.0451
[18] precision:0.8089, recall:0.7745, f1:0.7913
[*] saved best model!
[19] ner train1:2.2389, rc_train1:7.4613, ner train2:1.3861, rc_train2:1.2271, info:0.0866
[19] ner test1:2.6948, rc test1:7.8998, ner test2:2.0412, rc test2:1.5856, info:0.0555
[19] precision:0.8118, recall:0.7824, f1:0.7968
[*] saved best model!
[20] ner train1:2.1875, rc_train1:7.4954, ner train2:1.3275, rc_train2:1.1728, info:0.1631
[20] ner test1:2.6916, rc test1:7.7756, ner test2:2.0767, rc test2:1.6301, info:0.0515
[20] precision:0.8112, recall:0.7729, f1:0.7916
[21] ner train1:2.1789, rc_train1:7.4042, ner train2:1.2919, rc_train2:1.1264, info:0.1181
[21] ner test1:2.6887, rc test1:7.7278, ner test2:2.0887, rc test2:1.5966, info:0.1327
[21] precision:0.8155, recall:0.7905, f1:0.8028
[*] saved best model!
[22] ner train1:2.1576, rc_train1:7.3717, ner train2:1.2528, rc_train2:1.1060, info:0.1746
[22] ner test1:2.6641, rc test1:7.7931, ner test2:2.0296, rc test2:1.5782, info:0.0470
[22] precision:0.8224, recall:0.7742, f1:0.7976
[23] ner train1:2.1366, rc_train1:7.4689, ner train2:1.2144, rc_train2:1.0710, info:0.1542
[23] ner test1:2.6964, rc test1:7.7154, ner test2:2.0824, rc test2:1.5667, info:0.2325
[23] precision:0.7974, recall:0.7901, f1:0.7938
[24] ner train1:2.1145, rc_train1:7.4508, ner train2:1.1906, rc_train2:1.0442, info:0.1563
[24] ner test1:2.7507, rc test1:7.6068, ner test2:2.0955, rc test2:1.6104, info:0.0755
[24] precision:0.7982, recall:0.7967, f1:0.7975
[25] ner train1:2.1102, rc_train1:7.3478, ner train2:1.1724, rc_train2:1.0291, info:0.2055
[25] ner test1:2.6730, rc test1:7.8053, ner test2:2.0909, rc test2:1.6270, info:0.1148
[25] precision:0.7993, recall:0.7972, f1:0.7982
[26] ner train1:2.0933, rc_train1:7.3914, ner train2:1.1425, rc_train2:0.9992, info:0.8296
[26] ner test1:2.6543, rc test1:7.5545, ner test2:2.0658, rc test2:1.5734, info:0.2920
[26] precision:0.8040, recall:0.8075, f1:0.8057
[*] saved best model!
[27] ner train1:2.0780, rc_train1:7.4016, ner train2:1.1352, rc_train2:0.9887, info:0.2034
[27] ner test1:2.6911, rc test1:8.1862, ner test2:2.2592, rc test2:1.6189, info:0.1806
[27] precision:0.8425, recall:0.8012, f1:0.8213
[*] saved best model!
[28] ner train1:2.0968, rc_train1:7.4333, ner train2:1.1405, rc_train2:1.0069, info:0.4036
[28] ner test1:2.7238, rc test1:7.5512, ner test2:2.1385, rc test2:1.6523, info:0.2324
[28] precision:0.8045, recall:0.7984, f1:0.8015
[29] ner train1:2.0919, rc_train1:7.4766, ner train2:1.1609, rc_train2:1.0368, info:0.5397
[29] ner test1:2.7350, rc test1:8.5117, ner test2:2.1805, rc test2:1.6489, info:0.2635
[29] precision:0.8185, recall:0.7773, f1:0.7974
[30] ner train1:2.1120, rc_train1:7.4818, ner train2:1.1591, rc_train2:1.0245, info:0.1518
[30] ner test1:2.7099, rc test1:7.5222, ner test2:2.0329, rc test2:1.5592, info:0.0596
[30] precision:0.8237, recall:0.7862, f1:0.8045
[31] ner train1:2.0716, rc_train1:7.3413, ner train2:1.1194, rc_train2:0.9901, info:0.2511
[31] ner test1:2.7826, rc test1:7.5960, ner test2:2.2138, rc test2:1.6586, info:0.1808
[31] precision:0.8070, recall:0.8159, f1:0.8114
[32] ner train1:2.0739, rc_train1:7.3158, ner train2:1.1044, rc_train2:0.9732, info:1.5356
[32] ner test1:2.6773, rc test1:7.8785, ner test2:2.0681, rc test2:1.5726, info:0.2613
[32] precision:0.8200, recall:0.7820, f1:0.8006
[33] ner train1:2.0333, rc_train1:7.3548, ner train2:1.0665, rc_train2:0.9340, info:0.1930
[33] ner test1:2.6914, rc test1:7.5427, ner test2:2.1242, rc test2:1.5632, info:0.1975
[33] precision:0.8165, recall:0.8139, f1:0.8152
[34] ner train1:2.0352, rc_train1:7.2549, ner train2:1.0490, rc_train2:0.9298, info:0.6106
[34] ner test1:2.7102, rc test1:7.3080, ner test2:2.1483, rc test2:1.6735, info:0.1524
[34] precision:0.7763, recall:0.8300, f1:0.8022
[35] ner train1:2.0325, rc_train1:7.2706, ner train2:1.0358, rc_train2:0.9044, info:1.4227
[35] ner test1:2.7312, rc test1:7.2900, ner test2:2.1660, rc test2:1.6036, info:0.4380
[35] precision:0.7995, recall:0.8275, f1:0.8133
[36] ner train1:2.0100, rc_train1:7.3529, ner train2:1.0301, rc_train2:0.8985, info:0.3707
[36] ner test1:2.7004, rc test1:7.9410, ner test2:2.0928, rc test2:1.5901, info:0.4361
[36] precision:0.8096, recall:0.8075, f1:0.8086
[37] ner train1:2.0347, rc_train1:7.3210, ner train2:1.0623, rc_train2:0.9326, info:0.2637
[37] ner test1:2.7779, rc test1:7.7808, ner test2:2.1321, rc test2:1.5710, info:0.1892
[37] precision:0.8317, recall:0.7940, f1:0.8124
[38] ner train1:2.0296, rc_train1:7.3549, ner train2:1.0490, rc_train2:0.9285, info:0.2683
[38] ner test1:2.7137, rc test1:7.3595, ner test2:2.1220, rc test2:1.6284, info:0.3119
[38] precision:0.7868, recall:0.8185, f1:0.8023
[39] ner train1:2.0158, rc_train1:7.3543, ner train2:1.0320, rc_train2:0.9130, info:3.1415
[39] ner test1:2.7559, rc test1:7.9202, ner test2:2.1865, rc test2:1.6617, info:0.1635
[39] precision:0.8037, recall:0.8055, f1:0.8046
[40] ner train1:1.9937, rc_train1:7.3292, ner train2:1.0109, rc_train2:0.8794, info:0.2922
[40] ner test1:2.7765, rc test1:7.3895, ner test2:2.2397, rc test2:1.6049, info:0.2751
[40] precision:0.7987, recall:0.8072, f1:0.8029
[41] ner train1:1.9864, rc_train1:7.4275, ner train2:0.9952, rc_train2:0.8793, info:3.9046
[41] ner test1:2.7949, rc test1:7.7044, ner test2:2.2080, rc test2:1.6345, info:0.2176
[41] precision:0.8167, recall:0.7962, f1:0.8063
[42] ner train1:2.0032, rc_train1:7.3559, ner train2:1.0209, rc_train2:0.9021, info:6.2873
[42] ner test1:2.7628, rc test1:7.6440, ner test2:2.1685, rc test2:1.5916, info:0.1797
[42] precision:0.8126, recall:0.7908, f1:0.8015
[43] ner train1:1.9754, rc_train1:7.3961, ner train2:0.9844, rc_train2:0.8741, info:1.4111
[43] ner test1:2.7383, rc test1:8.0020, ner test2:2.2290, rc test2:1.5839, info:0.1797
[43] precision:0.8181, recall:0.8067, f1:0.8123
[44] ner train1:1.9782, rc_train1:7.2784, ner train2:0.9812, rc_train2:0.8615, info:0.2607
[44] ner test1:2.7399, rc test1:7.4510, ner test2:2.1565, rc test2:1.5919, info:0.1316
[44] precision:0.8270, recall:0.7982, f1:0.8123
[45] ner train1:1.9732, rc_train1:7.3598, ner train2:0.9561, rc_train2:0.8412, info:0.4107
[45] ner test1:2.7912, rc test1:7.9098, ner test2:2.2822, rc test2:1.6637, info:0.4124
[45] precision:0.8185, recall:0.8194, f1:0.8189
[46] ner train1:1.9686, rc_train1:7.2240, ner train2:0.9523, rc_train2:0.8303, info:1.0415
[46] ner test1:2.8277, rc test1:7.7992, ner test2:2.2299, rc test2:1.6258, info:0.1717
[46] precision:0.8014, recall:0.8144, f1:0.8079
[47] ner train1:1.9421, rc_train1:7.1608, ner train2:0.9162, rc_train2:0.8002, info:0.2150
[47] ner test1:2.7701, rc test1:7.7913, ner test2:2.2281, rc test2:1.6295, info:0.0593
[47] precision:0.8216, recall:0.8134, f1:0.8175
[48] ner train1:1.9064, rc_train1:7.2474, ner train2:0.8864, rc_train2:0.7766, info:0.2001
[48] ner test1:2.7079, rc test1:7.5740, ner test2:2.2274, rc test2:1.5876, info:0.0871
[48] precision:0.8242, recall:0.8134, f1:0.8188
[49] ner train1:1.9109, rc_train1:7.2027, ner train2:0.8815, rc_train2:0.7668, info:0.1785
[49] ner test1:2.7343, rc test1:7.3337, ner test2:2.1931, rc test2:1.6367, info:0.2655
[49] precision:0.8019, recall:0.8295, f1:0.8154
[50] ner train1:1.8981, rc_train1:7.2003, ner train2:0.8454, rc_train2:0.7323, info:0.6238
[50] ner test1:2.7776, rc test1:7.1591, ner test2:2.2051, rc test2:1.6387, info:0.1797
[50] precision:0.8348, recall:0.8191, f1:0.8269
[*] saved best model!
[51] ner train1:1.8868, rc_train1:7.1124, ner train2:0.8534, rc_train2:0.7488, info:1.8643
[51] ner test1:2.8121, rc test1:8.0500, ner test2:2.3313, rc test2:1.6902, info:0.3376
[51] precision:0.8263, recall:0.7797, f1:0.8023
[52] ner train1:1.9256, rc_train1:7.2047, ner train2:0.8709, rc_train2:0.7728, info:0.9924
[52] ner test1:2.8723, rc test1:7.5088, ner test2:2.3157, rc test2:1.6885, info:0.2205
[52] precision:0.8032, recall:0.8303, f1:0.8165
[53] ner train1:1.9140, rc_train1:7.3162, ner train2:0.8753, rc_train2:0.7658, info:2.1222
[53] ner test1:2.7310, rc test1:7.8537, ner test2:2.1923, rc test2:1.6136, info:0.1960
[53] precision:0.8132, recall:0.7988, f1:0.8059
[54] ner train1:1.9290, rc_train1:7.1164, ner train2:0.8907, rc_train2:0.7883, info:1.2283
[54] ner test1:2.8148, rc test1:7.9177, ner test2:2.1845, rc test2:1.5810, info:0.3976
[54] precision:0.8394, recall:0.7937, f1:0.8159
[55] ner train1:1.9511, rc_train1:7.2002, ner train2:0.8918, rc_train2:0.7969, info:0.7955
[55] ner test1:2.9133, rc test1:7.2897, ner test2:2.3091, rc test2:1.6607, info:0.1955
[55] precision:0.7826, recall:0.8165, f1:0.7992
[56] ner train1:1.9083, rc_train1:7.1048, ner train2:0.8660, rc_train2:0.7650, info:0.4261
[56] ner test1:2.8098, rc test1:7.3665, ner test2:2.2871, rc test2:1.5859, info:0.1294
[56] precision:0.8166, recall:0.8250, f1:0.8208
[57] ner train1:1.8755, rc_train1:6.8921, ner train2:0.8307, rc_train2:0.7381, info:0.2187
[57] ner test1:2.7892, rc test1:7.5336, ner test2:2.2995, rc test2:1.6880, info:0.1179
[57] precision:0.8229, recall:0.8154, f1:0.8192
[58] ner train1:1.8690, rc_train1:6.9269, ner train2:0.8153, rc_train2:0.7190, info:1.1171
[58] ner test1:2.8554, rc test1:7.1259, ner test2:2.3033, rc test2:1.6223, info:0.1528
[58] precision:0.8039, recall:0.8157, f1:0.8097
[59] ner train1:1.8628, rc_train1:6.9029, ner train2:0.8189, rc_train2:0.7228, info:1.0744
[59] ner test1:2.8233, rc test1:7.4472, ner test2:2.2941, rc test2:1.6759, info:0.0713
[59] precision:0.7910, recall:0.8365, f1:0.8131
[60] ner train1:1.8600, rc_train1:7.0337, ner train2:0.8178, rc_train2:0.7255, info:2.7607
[60] ner test1:2.7521, rc test1:6.9868, ner test2:2.2013, rc test2:1.5423, info:0.2107
[60] precision:0.8250, recall:0.8263, f1:0.8257
[61] ner train1:1.8313, rc_train1:6.9492, ner train2:0.7654, rc_train2:0.6740, info:0.8170
[61] ner test1:2.7674, rc test1:7.5023, ner test2:2.2288, rc test2:1.5935, info:0.2137
[61] precision:0.8354, recall:0.8106, f1:0.8228
[62] ner train1:1.8259, rc_train1:6.8849, ner train2:0.7681, rc_train2:0.6841, info:0.2805
[62] ner test1:2.7639, rc test1:7.0900, ner test2:2.3814, rc test2:1.6598, info:0.3548
[62] precision:0.8157, recall:0.8266, f1:0.8211
[63] ner train1:1.8419, rc_train1:6.9529, ner train2:0.7880, rc_train2:0.6905, info:0.5155
[63] ner test1:2.7617, rc test1:7.5834, ner test2:2.3247, rc test2:1.6740, info:0.2806
[63] precision:0.8426, recall:0.8081, f1:0.8250
[64] ner train1:1.8427, rc_train1:7.0105, ner train2:0.7894, rc_train2:0.6996, info:0.3668
[64] ner test1:2.8606, rc test1:7.8493, ner test2:2.2084, rc test2:1.5960, info:0.1478
[64] precision:0.8050, recall:0.8123, f1:0.8086
[65] ner train1:1.8387, rc_train1:7.1456, ner train2:0.7793, rc_train2:0.6980, info:0.6471
[65] ner test1:2.8208, rc test1:7.3900, ner test2:2.3589, rc test2:1.6400, info:0.1526
[65] precision:0.8228, recall:0.8217, f1:0.8223
[66] ner train1:1.8310, rc_train1:7.1065, ner train2:0.7813, rc_train2:0.6959, info:0.3378
[66] ner test1:2.8222, rc test1:7.6104, ner test2:2.2708, rc test2:1.6404, info:0.1030
[66] precision:0.8384, recall:0.8182, f1:0.8282
[*] saved best model!
[67] ner train1:1.8482, rc_train1:7.1496, ner train2:0.7899, rc_train2:0.6904, info:1.6041
[67] ner test1:2.8193, rc test1:7.4040, ner test2:2.4560, rc test2:1.6947, info:0.1141
[67] precision:0.8055, recall:0.8260, f1:0.8156
[68] ner train1:1.8422, rc_train1:6.9131, ner train2:0.7738, rc_train2:0.6766, info:0.2679
[68] ner test1:2.8424, rc test1:7.5690, ner test2:2.2927, rc test2:1.6469, info:0.2044
[68] precision:0.8103, recall:0.8216, f1:0.8159
[69] ner train1:1.8264, rc_train1:6.9066, ner train2:0.7524, rc_train2:0.6701, info:1.3376
[69] ner test1:2.7578, rc test1:7.3286, ner test2:2.2375, rc test2:1.6225, info:0.2766
[69] precision:0.8376, recall:0.7999, f1:0.8183
[70] ner train1:1.8317, rc_train1:6.9789, ner train2:0.7614, rc_train2:0.6870, info:0.4313
[70] ner test1:2.8950, rc test1:7.3537, ner test2:2.3341, rc test2:1.6477, info:0.1907
[70] precision:0.8095, recall:0.8123, f1:0.8109
[71] ner train1:1.8265, rc_train1:6.8782, ner train2:0.7440, rc_train2:0.6661, info:0.5537
[71] ner test1:2.7787, rc test1:7.5198, ner test2:2.3799, rc test2:1.6181, info:0.2462
[71] precision:0.8438, recall:0.8270, f1:0.8353
[*] saved best model!
[72] ner train1:1.8107, rc_train1:6.9535, ner train2:0.7374, rc_train2:0.6625, info:0.2896
[72] ner test1:2.7840, rc test1:7.2377, ner test2:2.3135, rc test2:1.6426, info:0.1464
[72] precision:0.8292, recall:0.8081, f1:0.8185
[73] ner train1:1.8004, rc_train1:6.8148, ner train2:0.7185, rc_train2:0.6399, info:0.3077
[73] ner test1:2.8566, rc test1:7.2712, ner test2:2.3099, rc test2:1.6145, info:0.1091
[73] precision:0.8206, recall:0.8211, f1:0.8208
[74] ner train1:1.7997, rc_train1:6.9222, ner train2:0.7131, rc_train2:0.6283, info:0.8925
[74] ner test1:2.9205, rc test1:7.4252, ner test2:2.3252, rc test2:1.6816, info:0.1565
[74] precision:0.8014, recall:0.8259, f1:0.8135
[75] ner train1:1.7972, rc_train1:7.0126, ner train2:0.7183, rc_train2:0.6377, info:0.4475
[75] ner test1:2.8254, rc test1:7.1771, ner test2:2.3432, rc test2:1.6704, info:0.1626
[75] precision:0.8079, recall:0.8395, f1:0.8234
[76] ner train1:1.7834, rc_train1:6.8384, ner train2:0.6984, rc_train2:0.6255, info:0.3351
[76] ner test1:2.8039, rc test1:7.6932, ner test2:2.3224, rc test2:1.6685, info:0.1801
[76] precision:0.8479, recall:0.7946, f1:0.8204
[77] ner train1:1.7772, rc_train1:6.7478, ner train2:0.6938, rc_train2:0.6263, info:2.8475
[77] ner test1:2.8220, rc test1:7.0231, ner test2:2.3763, rc test2:1.6989, info:0.2407
[77] precision:0.8040, recall:0.8350, f1:0.8192
[78] ner train1:1.7759, rc_train1:6.6761, ner train2:0.6928, rc_train2:0.6172, info:0.3884
[78] ner test1:2.7973, rc test1:7.4310, ner test2:2.3351, rc test2:1.6979, info:0.1424
[78] precision:0.8011, recall:0.8164, f1:0.8087
[79] ner train1:1.7860, rc_train1:6.7665, ner train2:0.6966, rc_train2:0.6230, info:0.7155
[79] ner test1:2.8220, rc test1:7.4430, ner test2:2.3192, rc test2:1.6104, info:0.2371
[79] precision:0.8314, recall:0.8102, f1:0.8207
[80] ner train1:1.7477, rc_train1:6.6721, ner train2:0.6627, rc_train2:0.5941, info:1.6305
[80] ner test1:2.7925, rc test1:7.2828, ner test2:2.4178, rc test2:1.6943, info:0.1750
[80] precision:0.8163, recall:0.8390, f1:0.8275
[81] ner train1:1.7375, rc_train1:6.6553, ner train2:0.6693, rc_train2:0.5978, info:0.9593
[81] ner test1:2.7865, rc test1:7.2008, ner test2:2.3641, rc test2:1.6488, info:0.1525
[81] precision:0.8216, recall:0.8319, f1:0.8267
[82] ner train1:1.7259, rc_train1:6.6667, ner train2:0.6564, rc_train2:0.5879, info:0.7091
[82] ner test1:2.8097, rc test1:7.4754, ner test2:2.4273, rc test2:1.6504, info:0.5050
[82] precision:0.8402, recall:0.8190, f1:0.8295
[83] ner train1:1.7389, rc_train1:6.6612, ner train2:0.6632, rc_train2:0.5887, info:3.3253
[83] ner test1:2.8046, rc test1:7.0556, ner test2:2.4139, rc test2:1.6943, info:0.7558
[83] precision:0.8352, recall:0.8231, f1:0.8291
[84] ner train1:1.7455, rc_train1:6.8524, ner train2:0.6694, rc_train2:0.6063, info:0.6151
[84] ner test1:2.8131, rc test1:7.1645, ner test2:2.5361, rc test2:1.8440, info:0.2309
[84] precision:0.8017, recall:0.8311, f1:0.8161
[85] ner train1:1.7533, rc_train1:6.7822, ner train2:0.6617, rc_train2:0.5965, info:0.6677
[85] ner test1:2.8116, rc test1:7.1190, ner test2:2.4086, rc test2:1.7950, info:0.6597
[85] precision:0.8347, recall:0.8022, f1:0.8182
[86] ner train1:1.7314, rc_train1:6.6803, ner train2:0.6335, rc_train2:0.5636, info:0.3225
[86] ner test1:2.8606, rc test1:7.3384, ner test2:2.4165, rc test2:1.7322, info:0.3362
[86] precision:0.8296, recall:0.8194, f1:0.8244
[87] ner train1:1.7246, rc_train1:6.7695, ner train2:0.6398, rc_train2:0.5780, info:0.9395
[87] ner test1:2.8348, rc test1:7.6973, ner test2:2.4461, rc test2:1.7453, info:0.2031
[87] precision:0.8425, recall:0.8143, f1:0.8281
[88] ner train1:1.7247, rc_train1:6.7107, ner train2:0.6405, rc_train2:0.5766, info:0.4185
[88] ner test1:2.8431, rc test1:7.1003, ner test2:2.4457, rc test2:1.7666, info:0.1111
[88] precision:0.8218, recall:0.8295, f1:0.8256
[89] ner train1:1.7140, rc_train1:6.7280, ner train2:0.6350, rc_train2:0.5670, info:0.6178
[89] ner test1:2.8414, rc test1:7.2110, ner test2:2.4380, rc test2:1.7434, info:0.1156
[89] precision:0.8110, recall:0.8231, f1:0.8170
[90] ner train1:1.7047, rc_train1:6.6041, ner train2:0.6242, rc_train2:0.5592, info:1.0296
[90] ner test1:2.8599, rc test1:7.3143, ner test2:2.4874, rc test2:1.8099, info:0.0732
[90] precision:0.8171, recall:0.8166, f1:0.8169
[91] ner train1:1.6974, rc_train1:6.5490, ner train2:0.5925, rc_train2:0.5379, info:0.3453
[91] ner test1:2.8299, rc test1:6.9413, ner test2:2.5147, rc test2:1.7321, info:0.1741
[91] precision:0.8175, recall:0.8506, f1:0.8337
[92] ner train1:1.6895, rc_train1:6.5798, ner train2:0.5811, rc_train2:0.5088, info:0.1606
[92] ner test1:2.8735, rc test1:7.0921, ner test2:2.4506, rc test2:1.7953, info:0.1269
[92] precision:0.8324, recall:0.8236, f1:0.8279
[93] ner train1:1.6869, rc_train1:6.5887, ner train2:0.5745, rc_train2:0.5199, info:0.2108
[93] ner test1:2.8116, rc test1:6.8239, ner test2:2.4476, rc test2:1.7269, info:0.3433
[93] precision:0.8314, recall:0.8226, f1:0.8269
[94] ner train1:1.6735, rc_train1:6.4802, ner train2:0.5595, rc_train2:0.5110, info:0.2204
[94] ner test1:2.8610, rc test1:6.8762, ner test2:2.4527, rc test2:1.7190, info:0.0693
[94] precision:0.8368, recall:0.8185, f1:0.8275
[95] ner train1:1.6765, rc_train1:6.4662, ner train2:0.5625, rc_train2:0.5133, info:0.1990
[95] ner test1:2.8527, rc test1:6.8235, ner test2:2.5189, rc test2:1.7761, info:0.0847
[95] precision:0.8248, recall:0.8340, f1:0.8294
[96] ner train1:1.6553, rc_train1:6.4883, ner train2:0.5420, rc_train2:0.4917, info:1.8822
[96] ner test1:2.8083, rc test1:6.9104, ner test2:2.4197, rc test2:1.7448, info:0.0721
[96] precision:0.8197, recall:0.8270, f1:0.8233
[97] ner train1:1.6564, rc_train1:6.4931, ner train2:0.5463, rc_train2:0.4924, info:0.2737
[97] ner test1:2.8232, rc test1:7.0452, ner test2:2.4695, rc test2:1.7817, info:0.0874
[97] precision:0.8254, recall:0.8313, f1:0.8284
[98] ner train1:1.6340, rc_train1:6.4560, ner train2:0.5228, rc_train2:0.4715, info:0.1153
[98] ner test1:2.8379, rc test1:7.1253, ner test2:2.5054, rc test2:1.7269, info:0.1050
[98] precision:0.8235, recall:0.8350, f1:0.8292
[99] ner train1:1.6494, rc_train1:6.3994, ner train2:0.5352, rc_train2:0.4951, info:0.1693
[99] ner test1:2.8332, rc test1:6.9287, ner test2:2.5765, rc test2:1.8298, info:0.3026
[99] precision:0.8448, recall:0.8179, f1:0.8311
[100] ner train1:1.6502, rc_train1:6.4012, ner train2:0.5346, rc_train2:0.4792, info:0.5147
[100] ner test1:2.8750, rc test1:6.9352, ner test2:2.6177, rc test2:1.8308, info:0.3797
[100] precision:0.8222, recall:0.8311, f1:0.8266