random3_lr_exact

[1] ner train1:11.2421, rc_train1:2523.1313, ner train2:4.6700, rc_train2:322.8091, info:0.0468
[1] ner test1:7.0605, rc test1:71.4634, ner test2:3.0113, rc test2:2.9048, info:0.0078
[1] precision:0.7007, recall:0.5422, f1:0.6113
[*] saved best model!
[2] ner train1:6.5708, rc_train1:62.3544, ner train2:2.9275, rc_train2:2.8483, info:0.0073
[2] ner test1:5.9259, rc test1:57.9860, ner test2:2.6776, rc test2:2.4778, info:0.0067
[2] precision:0.7130, recall:0.6420, f1:0.6757
[*] saved best model!
[3] ner train1:5.8653, rc_train1:54.3812, ner train2:2.6089, rc_train2:2.4356, info:0.0068
[3] ner test1:5.3943, rc test1:50.0166, ner test2:2.4713, rc test2:2.2014, info:0.0062
[3] precision:0.7425, recall:0.6753, f1:0.7073
[*] saved best model!
[4] ner train1:5.5171, rc_train1:51.0012, ner train2:2.3784, rc_train2:2.1875, info:0.0062
[4] ner test1:5.3230, rc test1:51.1506, ner test2:2.3408, rc test2:2.0491, info:0.0059
[4] precision:0.7591, recall:0.6983, f1:0.7274
[*] saved best model!
[5] ner train1:5.2131, rc_train1:47.9767, ner train2:2.1939, rc_train2:1.9946, info:0.0089
[5] ner test1:5.0385, rc test1:47.0866, ner test2:2.2341, rc test2:1.9697, info:0.0057
[5] precision:0.7404, recall:0.7432, f1:0.7418
[*] saved best model!
[6] ner train1:4.9171, rc_train1:44.6981, ner train2:2.0415, rc_train2:1.8268, info:0.0058
[6] ner test1:4.7548, rc test1:46.1267, ner test2:2.1412, rc test2:1.8456, info:0.0055
[6] precision:0.7736, recall:0.7372, f1:0.7550
[*] saved best model!
[7] ner train1:4.6776, rc_train1:42.1612, ner train2:1.9065, rc_train2:1.7072, info:0.0055
[7] ner test1:4.6384, rc test1:42.0411, ner test2:2.1167, rc test2:1.7932, info:0.0053
[7] precision:0.7623, recall:0.7543, f1:0.7583
[*] saved best model!
[8] ner train1:4.4719, rc_train1:40.8263, ner train2:1.8000, rc_train2:1.6073, info:0.0083
[8] ner test1:4.4589, rc test1:40.0073, ner test2:2.0767, rc test2:1.7572, info:0.0054
[8] precision:0.7641, recall:0.7593, f1:0.7617
[*] saved best model!
[9] ner train1:4.3180, rc_train1:39.1907, ner train2:1.7088, rc_train2:1.5018, info:0.0058
[9] ner test1:4.3826, rc test1:37.5715, ner test2:2.0427, rc test2:1.7296, info:0.0052
[9] precision:0.7678, recall:0.7651, f1:0.7664
[*] saved best model!
[10] ner train1:4.2543, rc_train1:38.7340, ner train2:1.6241, rc_train2:1.4244, info:0.0053
[10] ner test1:4.3395, rc test1:39.1254, ner test2:1.9917, rc test2:1.6536, info:0.0050
[10] precision:0.7810, recall:0.7723, f1:0.7766
[*] saved best model!
[11] ner train1:4.2008, rc_train1:37.8168, ner train2:1.5555, rc_train2:1.3759, info:0.0057
[11] ner test1:4.3209, rc test1:37.4542, ner test2:2.0291, rc test2:1.6658, info:0.0049
[11] precision:0.7770, recall:0.7856, f1:0.7812
[*] saved best model!
[12] ner train1:4.1365, rc_train1:37.2412, ner train2:1.4870, rc_train2:1.3053, info:0.0055
[12] ner test1:4.2307, rc test1:38.2221, ner test2:2.0152, rc test2:1.6795, info:0.0055
[12] precision:0.7672, recall:0.7813, f1:0.7741
[13] ner train1:4.0179, rc_train1:37.0948, ner train2:1.4101, rc_train2:1.2386, info:0.4044
[13] ner test1:4.2301, rc test1:37.3607, ner test2:2.0202, rc test2:1.6025, info:0.0499
[13] precision:0.8015, recall:0.7859, f1:0.7936
[*] saved best model!
[14] ner train1:4.0836, rc_train1:37.5882, ner train2:1.4623, rc_train2:1.2964, info:0.0382
[14] ner test1:4.3324, rc test1:38.7479, ner test2:2.0309, rc test2:1.6222, info:0.0058
[14] precision:0.7980, recall:0.7734, f1:0.7855
[15] ner train1:4.0987, rc_train1:37.1916, ner train2:1.4058, rc_train2:1.2323, info:0.0085
[15] ner test1:4.3151, rc test1:37.4948, ner test2:2.0438, rc test2:1.6190, info:0.0128
[15] precision:0.7981, recall:0.7846, f1:0.7913
[16] ner train1:4.0697, rc_train1:36.4702, ner train2:1.3391, rc_train2:1.1719, info:0.0162
[16] ner test1:4.2861, rc test1:37.1885, ner test2:2.0217, rc test2:1.5557, info:0.0131
[16] precision:0.8159, recall:0.7649, f1:0.7895
[17] ner train1:4.0798, rc_train1:36.9248, ner train2:1.3108, rc_train2:1.1489, info:0.0279
[17] ner test1:4.2853, rc test1:35.4220, ner test2:2.1594, rc test2:1.6110, info:0.0932
[17] precision:0.8199, recall:0.7546, f1:0.7859
[18] ner train1:4.0135, rc_train1:36.4805, ner train2:1.2585, rc_train2:1.0992, info:0.0209
[18] ner test1:4.2565, rc test1:34.8048, ner test2:2.0036, rc test2:1.5671, info:0.0054
[18] precision:0.7981, recall:0.7945, f1:0.7963
[*] saved best model!
[19] ner train1:4.1028, rc_train1:36.0943, ner train2:1.2254, rc_train2:1.0658, info:0.0090
[19] ner test1:4.3597, rc test1:37.0339, ner test2:1.9759, rc test2:1.5762, info:0.0053
[19] precision:0.7953, recall:0.7895, f1:0.7924
[20] ner train1:4.0336, rc_train1:36.0121, ner train2:1.1885, rc_train2:1.0301, info:0.0054
[20] ner test1:4.2145, rc test1:35.2274, ner test2:2.0053, rc test2:1.5719, info:0.0051
[20] precision:0.8005, recall:0.7901, f1:0.7953
[21] ner train1:3.9971, rc_train1:35.7180, ner train2:1.1589, rc_train2:1.0062, info:0.0052
[21] ner test1:4.2586, rc test1:35.2727, ner test2:2.0146, rc test2:1.5701, info:0.0050
[21] precision:0.7983, recall:0.8021, f1:0.8002
[*] saved best model!
[22] ner train1:3.9775, rc_train1:35.2759, ner train2:1.1147, rc_train2:0.9639, info:0.0511
[22] ner test1:4.2277, rc test1:34.7659, ner test2:2.0561, rc test2:1.5288, info:0.1042
[22] precision:0.8099, recall:0.8015, f1:0.8057
[*] saved best model!
[23] ner train1:3.9559, rc_train1:35.1550, ner train2:1.1015, rc_train2:0.9595, info:0.0115
[23] ner test1:4.4219, rc test1:36.0674, ner test2:2.0759, rc test2:1.5991, info:0.0621
[23] precision:0.7982, recall:0.8033, f1:0.8008
[24] ner train1:3.9937, rc_train1:34.9809, ner train2:1.0797, rc_train2:0.9454, info:0.0301
[24] ner test1:4.2579, rc test1:35.4316, ner test2:2.0410, rc test2:1.5782, info:0.0114
[24] precision:0.8099, recall:0.7963, f1:0.8030
[25] ner train1:3.9992, rc_train1:35.2536, ner train2:1.0487, rc_train2:0.9097, info:0.0181
[25] ner test1:4.2001, rc test1:35.4266, ner test2:2.0609, rc test2:1.5945, info:0.0072
[25] precision:0.8126, recall:0.7792, f1:0.7955
[26] ner train1:4.0187, rc_train1:34.7277, ner train2:1.0333, rc_train2:0.8977, info:0.0058
[26] ner test1:4.2609, rc test1:35.8867, ner test2:2.0674, rc test2:1.5329, info:0.0050
[26] precision:0.8055, recall:0.7982, f1:0.8018
[27] ner train1:4.0303, rc_train1:34.8560, ner train2:1.0026, rc_train2:0.8656, info:0.0263
[27] ner test1:4.4165, rc test1:33.2199, ner test2:2.1151, rc test2:1.5713, info:0.0052
[27] precision:0.8042, recall:0.8201, f1:0.8121
[*] saved best model!
[28] ner train1:3.9981, rc_train1:34.0766, ner train2:0.9740, rc_train2:0.8518, info:0.0134
[28] ner test1:4.3613, rc test1:33.6300, ner test2:2.1032, rc test2:1.6098, info:0.0050
[28] precision:0.8141, recall:0.8079, f1:0.8110
[29] ner train1:3.9993, rc_train1:33.4107, ner train2:0.9536, rc_train2:0.8380, info:0.0100
[29] ner test1:4.5195, rc test1:33.9782, ner test2:2.0753, rc test2:1.5404, info:0.0050
[29] precision:0.8202, recall:0.7868, f1:0.8031
[30] ner train1:3.9801, rc_train1:33.5394, ner train2:0.9229, rc_train2:0.7966, info:0.0084
[30] ner test1:4.4031, rc test1:34.5636, ner test2:2.0668, rc test2:1.5261, info:0.0049
[30] precision:0.8201, recall:0.7851, f1:0.8022
[31] ner train1:3.9897, rc_train1:33.3793, ner train2:0.9146, rc_train2:0.7871, info:0.0051
[31] ner test1:4.3524, rc test1:33.8923, ner test2:2.1347, rc test2:1.5983, info:0.0049
[31] precision:0.8182, recall:0.7893, f1:0.8035
[32] ner train1:3.9746, rc_train1:33.0837, ner train2:0.8986, rc_train2:0.7890, info:0.1623
[32] ner test1:4.4057, rc test1:34.3729, ner test2:2.1189, rc test2:1.5507, info:0.0068
[32] precision:0.8271, recall:0.8022, f1:0.8145
[*] saved best model!
[33] ner train1:3.9298, rc_train1:33.1094, ner train2:0.8725, rc_train2:0.7611, info:0.0308
[33] ner test1:4.4264, rc test1:35.0216, ner test2:2.1158, rc test2:1.5623, info:0.0089
[33] precision:0.8128, recall:0.7894, f1:0.8010
[34] ner train1:3.9700, rc_train1:33.0632, ner train2:0.8658, rc_train2:0.7600, info:0.0054
[34] ner test1:4.3605, rc test1:33.8252, ner test2:2.1588, rc test2:1.5861, info:0.0051
[34] precision:0.8189, recall:0.8041, f1:0.8114
[35] ner train1:3.9987, rc_train1:32.9677, ner train2:0.8455, rc_train2:0.7372, info:0.0051
[35] ner test1:4.5481, rc test1:32.5356, ner test2:2.2007, rc test2:1.5866, info:0.0049
[35] precision:0.8182, recall:0.8231, f1:0.8206
[*] saved best model!
[36] ner train1:4.0735, rc_train1:32.9929, ner train2:0.8272, rc_train2:0.7253, info:0.0096
[36] ner test1:4.5044, rc test1:33.6855, ner test2:2.1453, rc test2:1.5724, info:0.0050
[36] precision:0.8326, recall:0.7903, f1:0.8109
[37] ner train1:4.0978, rc_train1:32.3897, ner train2:0.8111, rc_train2:0.7033, info:0.0051
[37] ner test1:4.5688, rc test1:32.7501, ner test2:2.2128, rc test2:1.6088, info:0.0048
[37] precision:0.8058, recall:0.8152, f1:0.8105
[38] ner train1:4.0275, rc_train1:32.4213, ner train2:0.7946, rc_train2:0.7080, info:0.0254
[38] ner test1:4.4167, rc test1:33.5359, ner test2:2.2327, rc test2:1.6413, info:0.0049
[38] precision:0.8221, recall:0.8052, f1:0.8136
[39] ner train1:3.9842, rc_train1:32.3436, ner train2:0.7797, rc_train2:0.6845, info:0.0050
[39] ner test1:4.5633, rc test1:31.5208, ner test2:2.1934, rc test2:1.5859, info:0.0082
[39] precision:0.8030, recall:0.8084, f1:0.8057
[40] ner train1:3.9458, rc_train1:31.4706, ner train2:0.7686, rc_train2:0.6658, info:0.3196
[40] ner test1:4.3729, rc test1:31.5594, ner test2:2.2473, rc test2:1.6738, info:0.0073
[40] precision:0.8120, recall:0.8033, f1:0.8077
[41] ner train1:3.9624, rc_train1:31.6399, ner train2:0.7584, rc_train2:0.6670, info:0.0332
[41] ner test1:4.4332, rc test1:31.9725, ner test2:2.2734, rc test2:1.6386, info:0.0080
[41] precision:0.8183, recall:0.8053, f1:0.8118
[42] ner train1:3.9699, rc_train1:31.7831, ner train2:0.7384, rc_train2:0.6485, info:0.0063
[42] ner test1:4.5513, rc test1:32.9553, ner test2:2.2941, rc test2:1.6958, info:0.0049
[42] precision:0.8118, recall:0.8150, f1:0.8134
[43] ner train1:3.9642, rc_train1:31.7321, ner train2:0.7347, rc_train2:0.6552, info:0.0278
[43] ner test1:4.4347, rc test1:31.2133, ner test2:2.2938, rc test2:1.6004, info:0.0050
[43] precision:0.8166, recall:0.8153, f1:0.8159
[44] ner train1:3.9210, rc_train1:31.4419, ner train2:0.7265, rc_train2:0.6397, info:0.0051
[44] ner test1:4.4367, rc test1:31.9948, ner test2:2.2380, rc test2:1.5998, info:0.0048
[44] precision:0.8142, recall:0.8144, f1:0.8143
[45] ner train1:3.8801, rc_train1:31.2926, ner train2:0.7036, rc_train2:0.6230, info:0.1606
[45] ner test1:4.4163, rc test1:30.4066, ner test2:2.3738, rc test2:1.7283, info:0.0182
[45] precision:0.7965, recall:0.8308, f1:0.8133
[46] ner train1:3.9263, rc_train1:31.2320, ner train2:0.7052, rc_train2:0.6171, info:0.0616
[46] ner test1:4.4006, rc test1:31.8082, ner test2:2.2972, rc test2:1.6754, info:0.0174
[46] precision:0.8212, recall:0.8043, f1:0.8127
[47] ner train1:3.9327, rc_train1:31.3618, ner train2:0.7205, rc_train2:0.6395, info:0.0249
[47] ner test1:4.5855, rc test1:31.3455, ner test2:2.4177, rc test2:1.7036, info:0.0301
[47] precision:0.8127, recall:0.8284, f1:0.8205
[48] ner train1:3.9099, rc_train1:31.3352, ner train2:0.7139, rc_train2:0.6304, info:0.0584
[48] ner test1:4.5040, rc test1:30.9893, ner test2:2.3097, rc test2:1.6666, info:0.0049
[48] precision:0.8178, recall:0.8106, f1:0.8142
[49] ner train1:3.9451, rc_train1:30.9547, ner train2:0.6904, rc_train2:0.6125, info:0.0107
[49] ner test1:4.5724, rc test1:30.0711, ner test2:2.4494, rc test2:1.8302, info:0.0140
[49] precision:0.7909, recall:0.8392, f1:0.8144
[50] ner train1:3.9660, rc_train1:30.8280, ner train2:0.6833, rc_train2:0.6068, info:0.0198
[50] ner test1:4.4940, rc test1:30.8659, ner test2:2.3397, rc test2:1.7259, info:0.0125
[50] precision:0.8038, recall:0.8265, f1:0.8150
[51] ner train1:3.9324, rc_train1:31.0112, ner train2:0.6497, rc_train2:0.5785, info:0.0235
[51] ner test1:4.4899, rc test1:30.3625, ner test2:2.4740, rc test2:1.7887, info:0.0053
[51] precision:0.8128, recall:0.8358, f1:0.8241
[*] saved best model!
[52] ner train1:3.9272, rc_train1:30.3594, ner train2:0.6564, rc_train2:0.5816, info:0.0309
[52] ner test1:4.5334, rc test1:32.2234, ner test2:2.3520, rc test2:1.6614, info:0.0909
[52] precision:0.8135, recall:0.7994, f1:0.8064
[53] ner train1:3.9131, rc_train1:30.3614, ner train2:0.6385, rc_train2:0.5711, info:0.0536
[53] ner test1:4.6789, rc test1:31.7928, ner test2:2.3743, rc test2:1.6997, info:0.0667
[53] precision:0.7886, recall:0.8196, f1:0.8038
[54] ner train1:3.9804, rc_train1:31.1578, ner train2:0.6488, rc_train2:0.5929, info:0.0295
[54] ner test1:4.4607, rc test1:31.3959, ner test2:2.4040, rc test2:1.7528, info:0.0142
[54] precision:0.8234, recall:0.8253, f1:0.8244
[*] saved best model!
[55] ner train1:3.9360, rc_train1:30.9797, ner train2:0.6509, rc_train2:0.5889, info:0.0128
[55] ner test1:4.5517, rc test1:31.7252, ner test2:2.3042, rc test2:1.6994, info:0.0095
[55] precision:0.8220, recall:0.8247, f1:0.8233
[56] ner train1:3.9255, rc_train1:30.7265, ner train2:0.6345, rc_train2:0.5798, info:0.0079
[56] ner test1:4.6765, rc test1:31.4986, ner test2:2.4062, rc test2:1.7005, info:0.0052
[56] precision:0.8272, recall:0.8182, f1:0.8227
[57] ner train1:3.9269, rc_train1:30.3324, ner train2:0.6315, rc_train2:0.5659, info:0.0094
[57] ner test1:4.5036, rc test1:30.8038, ner test2:2.3363, rc test2:1.7000, info:0.0144
[57] precision:0.8216, recall:0.8122, f1:0.8169
[58] ner train1:3.9351, rc_train1:30.0788, ner train2:0.6132, rc_train2:0.5536, info:0.0110
[58] ner test1:4.5105, rc test1:29.4901, ner test2:2.3592, rc test2:1.6680, info:0.0047
[58] precision:0.8141, recall:0.8152, f1:0.8146
[59] ner train1:3.9021, rc_train1:30.1889, ner train2:0.5975, rc_train2:0.5485, info:0.0117
[59] ner test1:4.6280, rc test1:31.0161, ner test2:2.4320, rc test2:1.7456, info:0.0047
[59] precision:0.8241, recall:0.8200, f1:0.8221
[60] ner train1:3.9108, rc_train1:29.9590, ner train2:0.5903, rc_train2:0.5327, info:0.0076
[60] ner test1:4.5854, rc test1:28.3956, ner test2:2.4678, rc test2:1.7526, info:0.0047
[60] precision:0.7921, recall:0.8280, f1:0.8096
[61] ner train1:3.9057, rc_train1:29.9571, ner train2:0.5712, rc_train2:0.5171, info:0.0048
[61] ner test1:4.6516, rc test1:30.7047, ner test2:2.5161, rc test2:1.7204, info:0.0046
[61] precision:0.8206, recall:0.8238, f1:0.8222
[62] ner train1:3.9200, rc_train1:29.6957, ner train2:0.5704, rc_train2:0.5200, info:0.5610
[62] ner test1:4.5791, rc test1:28.7920, ner test2:2.4418, rc test2:1.7427, info:0.0116
[62] precision:0.8112, recall:0.8279, f1:0.8194
[63] ner train1:3.8400, rc_train1:29.2391, ner train2:0.5728, rc_train2:0.5205, info:0.0084
[63] ner test1:4.4756, rc test1:30.1428, ner test2:2.4784, rc test2:1.7442, info:0.0045
[63] precision:0.8277, recall:0.8155, f1:0.8216
[64] ner train1:3.7995, rc_train1:29.0234, ner train2:0.5556, rc_train2:0.5074, info:0.0079
[64] ner test1:4.5512, rc test1:28.8477, ner test2:2.4745, rc test2:1.7784, info:0.0046
[64] precision:0.8173, recall:0.8318, f1:0.8245
[*] saved best model!
[65] ner train1:3.8345, rc_train1:28.8711, ner train2:0.5561, rc_train2:0.5023, info:2.3734
[65] ner test1:4.5734, rc test1:29.8769, ner test2:2.4692, rc test2:1.7946, info:0.0334
[65] precision:0.8195, recall:0.8238, f1:0.8216
[66] ner train1:3.8433, rc_train1:29.1048, ner train2:0.5457, rc_train2:0.5044, info:0.3243
[66] ner test1:4.6528, rc test1:29.5175, ner test2:2.4143, rc test2:1.7607, info:0.0047
[66] precision:0.8201, recall:0.8324, f1:0.8262
[*] saved best model!
[67] ner train1:3.8321, rc_train1:29.2161, ner train2:0.5429, rc_train2:0.4998, info:0.0195
[67] ner test1:4.5199, rc test1:30.2936, ner test2:2.4904, rc test2:1.7827, info:0.0198
[67] precision:0.8189, recall:0.8311, f1:0.8249
[68] ner train1:3.7555, rc_train1:29.4503, ner train2:0.5479, rc_train2:0.4992, info:7.2325
[68] ner test1:4.4280, rc test1:30.6801, ner test2:2.4842, rc test2:1.7420, info:4.2435
[68] precision:0.8155, recall:0.8137, f1:0.8146
[69] ner train1:3.7436, rc_train1:29.4081, ner train2:0.5335, rc_train2:0.4824, info:3.1433
[69] ner test1:4.4365, rc test1:30.1917, ner test2:2.4871, rc test2:1.7366, info:0.9226
[69] precision:0.8116, recall:0.8216, f1:0.8165
[70] ner train1:3.7539, rc_train1:29.2618, ner train2:0.5195, rc_train2:0.4672, info:1.5719
[70] ner test1:4.4657, rc test1:30.2661, ner test2:2.4769, rc test2:1.7016, info:1.0507
[70] precision:0.8158, recall:0.8229, f1:0.8193
[71] ner train1:3.7937, rc_train1:29.4338, ner train2:0.5023, rc_train2:0.4578, info:1.7724
[71] ner test1:4.4845, rc test1:30.4428, ner test2:2.4643, rc test2:1.6807, info:1.3388
[71] precision:0.8143, recall:0.8196, f1:0.8169
[72] ner train1:3.8111, rc_train1:29.3197, ner train2:0.4971, rc_train2:0.4441, info:2.0556
[72] ner test1:4.5262, rc test1:30.3257, ner test2:2.4777, rc test2:1.6565, info:2.0783
[72] precision:0.8176, recall:0.8236, f1:0.8206
[73] ner train1:3.8415, rc_train1:29.4154, ner train2:0.4992, rc_train2:0.4485, info:2.0220
[73] ner test1:4.5427, rc test1:31.5693, ner test2:2.4856, rc test2:1.6784, info:1.7340
[73] precision:0.8257, recall:0.8158, f1:0.8207
[74] ner train1:3.8323, rc_train1:29.6240, ner train2:0.4856, rc_train2:0.4357, info:2.2450
[74] ner test1:4.5590, rc test1:30.5130, ner test2:2.5154, rc test2:1.6866, info:1.8304
[74] precision:0.8160, recall:0.8273, f1:0.8216
[75] ner train1:3.8964, rc_train1:29.9114, ner train2:0.5290, rc_train2:0.4722, info:2.1736
[75] ner test1:4.5506, rc test1:30.3811, ner test2:2.4823, rc test2:1.7717, info:1.4998
[75] precision:0.8397, recall:0.8202, f1:0.8298
[*] saved best model!
[76] ner train1:3.8388, rc_train1:28.7881, ner train2:0.5855, rc_train2:0.5274, info:1.7896
[76] ner test1:4.5917, rc test1:29.4937, ner test2:2.6031, rc test2:1.7690, info:2.4487
[76] precision:0.8165, recall:0.8270, f1:0.8217
[77] ner train1:3.8583, rc_train1:29.1952, ner train2:0.6709, rc_train2:0.6996, info:1.7245
[77] ner test1:4.6525, rc test1:29.5095, ner test2:2.7686, rc test2:2.1966, info:1.3246
[77] precision:0.7900, recall:0.7882, f1:0.7891
[78] ner train1:3.9216, rc_train1:28.8567, ner train2:0.6771, rc_train2:0.6588, info:1.1521
[78] ner test1:4.6830, rc test1:30.5904, ner test2:2.5148, rc test2:1.8090, info:1.3108
[78] precision:0.7894, recall:0.8090, f1:0.7991
[79] ner train1:3.9851, rc_train1:30.6959, ner train2:0.8345, rc_train2:0.8455, info:1.5405
[79] ner test1:4.8224, rc test1:33.0900, ner test2:2.7106, rc test2:1.9345, info:0.8390
[79] precision:0.8132, recall:0.7940, f1:0.8035
[80] ner train1:3.9888, rc_train1:31.9831, ner train2:0.8286, rc_train2:0.8355, info:1.3035
[80] ner test1:4.7944, rc test1:32.4068, ner test2:2.4620, rc test2:1.8750, info:0.9683
[80] precision:0.8054, recall:0.8021, f1:0.8037
[81] ner train1:4.0320, rc_train1:32.8100, ner train2:0.7897, rc_train2:0.8040, info:1.1607
[81] ner test1:4.8936, rc test1:34.3041, ner test2:2.6306, rc test2:1.9209, info:0.9054
[81] precision:0.7878, recall:0.8138, f1:0.8006
[82] ner train1:4.1663, rc_train1:31.9442, ner train2:0.7338, rc_train2:0.7328, info:1.1935
[82] ner test1:4.7623, rc test1:31.0619, ner test2:2.5459, rc test2:2.1560, info:0.7021
[82] precision:0.7586, recall:0.7523, f1:0.7554
[83] ner train1:4.1055, rc_train1:32.5105, ner train2:0.8475, rc_train2:0.8568, info:1.0463
[83] ner test1:4.9398, rc test1:36.0797, ner test2:2.5144, rc test2:2.0621, info:0.5974
[83] precision:0.8530, recall:0.7318, f1:0.7878
[84] ner train1:4.2366, rc_train1:32.6707, ner train2:0.8397, rc_train2:0.8359, info:1.2952
[84] ner test1:4.8709, rc test1:35.6097, ner test2:2.4204, rc test2:1.8138, info:0.6274
[84] precision:0.7973, recall:0.7954, f1:0.7964
[85] ner train1:4.2374, rc_train1:33.2107, ner train2:0.8354, rc_train2:0.8288, info:0.9861
[85] ner test1:5.8273, rc test1:23.2313, ner test2:5.2489, rc test2:4.1473, info:0.4900
[85] precision:0.5648, recall:0.8672, f1:0.6840
[86] ner train1:4.2596, rc_train1:32.4375, ner train2:0.9448, rc_train2:0.9584, info:0.7273
[86] ner test1:4.8630, rc test1:30.8396, ner test2:2.8212, rc test2:2.3630, info:0.5904
[86] precision:0.7819, recall:0.7371, f1:0.7589
[87] ner train1:4.2972, rc_train1:33.1723, ner train2:0.8622, rc_train2:0.8659, info:0.6901
[87] ner test1:5.1573, rc test1:38.6215, ner test2:2.4543, rc test2:1.9499, info:0.3807
[87] precision:0.8212, recall:0.7800, f1:0.8001
[88] ner train1:4.1656, rc_train1:33.8032, ner train2:0.8665, rc_train2:0.8796, info:0.8035
[88] ner test1:4.7723, rc test1:34.5644, ner test2:2.5966, rc test2:1.9692, info:0.5386
[88] precision:0.7646, recall:0.7970, f1:0.7805
[89] ner train1:4.2411, rc_train1:33.8499, ner train2:0.8004, rc_train2:0.7997, info:0.5386
[89] ner test1:5.1280, rc test1:33.4129, ner test2:2.4004, rc test2:1.8299, info:0.5174
[89] precision:0.8038, recall:0.8012, f1:0.8025
[90] ner train1:4.2985, rc_train1:33.1375, ner train2:0.8861, rc_train2:0.9415, info:0.5940
[90] ner test1:4.7940, rc test1:32.5838, ner test2:2.6662, rc test2:2.2629, info:0.4143
[90] precision:0.7398, recall:0.8149, f1:0.7756
[91] ner train1:4.3664, rc_train1:35.5162, ner train2:0.8598, rc_train2:0.8485, info:0.7821
[91] ner test1:5.2747, rc test1:37.8855, ner test2:2.4421, rc test2:1.9095, info:0.4111
[91] precision:0.7955, recall:0.7924, f1:0.7939
[92] ner train1:4.3027, rc_train1:34.9130, ner train2:0.8677, rc_train2:0.8900, info:0.4732
[92] ner test1:5.3209, rc test1:36.8409, ner test2:2.4457, rc test2:1.8505, info:0.4110
[92] precision:0.8007, recall:0.7962, f1:0.7984
[93] ner train1:4.6714, rc_train1:35.4358, ner train2:0.8150, rc_train2:0.8083, info:0.4786
[93] ner test1:5.3925, rc test1:36.1875, ner test2:2.6110, rc test2:1.9519, info:0.2818
[93] precision:0.7919, recall:0.8215, f1:0.8064
[94] ner train1:4.5632, rc_train1:35.0232, ner train2:0.7910, rc_train2:0.7653, info:0.5403
[94] ner test1:5.6946, rc test1:37.4139, ner test2:2.6323, rc test2:1.8710, info:0.3045
[94] precision:0.7855, recall:0.8173, f1:0.8011
[95] ner train1:4.6517, rc_train1:36.1532, ner train2:0.8263, rc_train2:0.8221, info:0.3536
[95] ner test1:5.4602, rc test1:38.1539, ner test2:2.4874, rc test2:1.9352, info:0.2346
[95] precision:0.7920, recall:0.7973, f1:0.7946
[96] ner train1:4.5680, rc_train1:36.3851, ner train2:0.8208, rc_train2:0.8047, info:0.5098
[96] ner test1:5.0758, rc test1:33.5353, ner test2:2.5764, rc test2:1.9033, info:0.1612
[96] precision:0.7821, recall:0.8190, f1:0.8001
[97] ner train1:4.4545, rc_train1:35.8222, ner train2:0.7673, rc_train2:0.7394, info:1.0358
[97] ner test1:5.2223, rc test1:36.3024, ner test2:2.4158, rc test2:1.7112, info:0.5283
[97] precision:0.8058, recall:0.7938, f1:0.7998
[98] ner train1:4.4260, rc_train1:38.0132, ner train2:0.7482, rc_train2:0.7123, info:1.2555
[98] ner test1:5.0552, rc test1:38.8435, ner test2:2.3964, rc test2:1.7833, info:0.2411
[98] precision:0.7961, recall:0.7972, f1:0.7966
[99] ner train1:4.3389, rc_train1:35.4035, ner train2:0.7833, rc_train2:0.8087, info:0.9968
[99] ner test1:5.2876, rc test1:34.2801, ner test2:2.5176, rc test2:1.8252, info:0.4325
[99] precision:0.7931, recall:0.8113, f1:0.8021
[100] ner train1:4.3492, rc_train1:34.6998, ner train2:0.7607, rc_train2:0.7548, info:0.5322
[100] ner test1:5.0078, rc test1:39.0823, ner test2:2.5403, rc test2:1.8909, info:0.3519
[100] precision:0.7767, recall:0.7917, f1:0.7841