random13_n6_partial

[1] ner train1:7.4128, rc_train1:1433.2493, ner train2:5.1314, rc_train2:338.8149, info:0.0311
[1] ner test1:4.0059, rc test1:11.9819, ner test2:3.1466, rc test2:3.3099, info:0.0085
[1] precision:0.6342, recall:0.6185, f1:0.6263
[*] saved best model!
[2] ner train1:3.8963, rc_train1:11.2130, ner train2:3.0485, rc_train2:3.0633, info:0.0087
[2] ner test1:3.6154, rc test1:10.8213, ner test2:2.8184, rc test2:2.7198, info:0.0080
[2] precision:0.6933, recall:0.6816, f1:0.6874
[*] saved best model!
[3] ner train1:3.5284, rc_train1:10.7549, ner train2:2.7529, rc_train2:2.6293, info:0.0136
[3] ner test1:3.3490, rc test1:10.9667, ner test2:2.5936, rc test2:2.3720, info:0.0079
[3] precision:0.7580, recall:0.7117, f1:0.7341
[*] saved best model!
[4] ner train1:3.2444, rc_train1:10.5302, ner train2:2.5046, rc_train2:2.3223, info:0.0095
[4] ner test1:3.1238, rc test1:10.6162, ner test2:2.4082, rc test2:2.1669, info:0.0071
[4] precision:0.8114, recall:0.6935, f1:0.7478
[*] saved best model!
[5] ner train1:3.0349, rc_train1:10.0816, ner train2:2.2784, rc_train2:2.0919, info:0.0082
[5] ner test1:3.0372, rc test1:10.2750, ner test2:2.3059, rc test2:2.0404, info:0.1688
[5] precision:0.7900, recall:0.7493, f1:0.7691
[*] saved best model!
[6] ner train1:2.8841, rc_train1:9.5612, ner train2:2.1168, rc_train2:1.9434, info:0.2387
[6] ner test1:2.9465, rc test1:9.4064, ner test2:2.2415, rc test2:1.9953, info:0.0294
[6] precision:0.7742, recall:0.7878, f1:0.7810
[*] saved best model!
[7] ner train1:2.7833, rc_train1:9.2388, ner train2:2.0078, rc_train2:1.8309, info:0.0179
[7] ner test1:2.8763, rc test1:9.0964, ner test2:2.2000, rc test2:1.8968, info:0.0152
[7] precision:0.7931, recall:0.7841, f1:0.7886
[*] saved best model!
[8] ner train1:2.6978, rc_train1:8.9070, ner train2:1.9122, rc_train2:1.7426, info:0.0178
[8] ner test1:2.8829, rc test1:8.6511, ner test2:2.1846, rc test2:1.8700, info:0.0090
[8] precision:0.7891, recall:0.7962, f1:0.7926
[*] saved best model!
[9] ner train1:2.6394, rc_train1:8.6731, ner train2:1.8302, rc_train2:1.6411, info:0.0142
[9] ner test1:2.7801, rc test1:8.3266, ner test2:2.1107, rc test2:1.8438, info:0.0084
[9] precision:0.7811, recall:0.8199, f1:0.8000
[*] saved best model!
[10] ner train1:2.5513, rc_train1:8.4982, ner train2:1.7228, rc_train2:1.5413, info:0.0111
[10] ner test1:2.7742, rc test1:8.5802, ner test2:2.0503, rc test2:1.7413, info:0.0121
[10] precision:0.7997, recall:0.8180, f1:0.8087
[*] saved best model!
[11] ner train1:2.4857, rc_train1:8.3861, ner train2:1.6441, rc_train2:1.4482, info:0.0109
[11] ner test1:2.7581, rc test1:8.0771, ner test2:2.0667, rc test2:1.7636, info:0.0085
[11] precision:0.7974, recall:0.8263, f1:0.8116
[*] saved best model!
[12] ner train1:2.4320, rc_train1:8.2586, ner train2:1.5848, rc_train2:1.3899, info:0.0371
[12] ner test1:2.7218, rc test1:8.2334, ner test2:2.0526, rc test2:1.6466, info:0.0180
[12] precision:0.8502, recall:0.8031, f1:0.8259
[*] saved best model!
[13] ner train1:2.3944, rc_train1:8.3052, ner train2:1.5392, rc_train2:1.3396, info:0.0201
[13] ner test1:2.7753, rc test1:8.6136, ner test2:2.0746, rc test2:1.6645, info:0.0085
[13] precision:0.8352, recall:0.8095, f1:0.8222
[14] ner train1:2.3512, rc_train1:8.1776, ner train2:1.4901, rc_train2:1.2968, info:0.0096
[14] ner test1:2.7110, rc test1:8.2759, ner test2:2.0720, rc test2:1.6097, info:0.0113
[14] precision:0.8272, recall:0.8280, f1:0.8276
[*] saved best model!
[15] ner train1:2.3042, rc_train1:8.1753, ner train2:1.4118, rc_train2:1.2191, info:0.0702
[15] ner test1:2.7446, rc test1:8.3718, ner test2:2.1441, rc test2:1.6358, info:0.0319
[15] precision:0.8253, recall:0.8339, f1:0.8296
[*] saved best model!
[16] ner train1:2.2862, rc_train1:8.1376, ner train2:1.4066, rc_train2:1.2283, info:0.0293
[16] ner test1:2.6727, rc test1:7.6768, ner test2:2.0955, rc test2:1.6694, info:0.0094
[16] precision:0.8063, recall:0.8471, f1:0.8262
[17] ner train1:2.2479, rc_train1:8.1613, ner train2:1.3686, rc_train2:1.1773, info:0.0133
[17] ner test1:2.6610, rc test1:8.0022, ner test2:2.0841, rc test2:1.5938, info:0.0136
[17] precision:0.8408, recall:0.8351, f1:0.8380
[*] saved best model!
[18] ner train1:2.2150, rc_train1:8.0000, ner train2:1.3131, rc_train2:1.1251, info:0.0241
[18] ner test1:2.7205, rc test1:8.0833, ner test2:2.1095, rc test2:1.5826, info:0.0069
[18] precision:0.8336, recall:0.8412, f1:0.8374
[19] ner train1:2.1787, rc_train1:7.9459, ner train2:1.2679, rc_train2:1.0693, info:0.0078
[19] ner test1:2.6771, rc test1:7.9117, ner test2:2.1016, rc test2:1.5862, info:0.0069
[19] precision:0.8296, recall:0.8527, f1:0.8410
[*] saved best model!
[20] ner train1:2.1459, rc_train1:7.9111, ner train2:1.2092, rc_train2:1.0239, info:0.0141
[20] ner test1:2.6457, rc test1:8.0524, ner test2:2.1461, rc test2:1.6113, info:0.0074
[20] precision:0.8255, recall:0.8547, f1:0.8399
[21] ner train1:2.1183, rc_train1:7.8256, ner train2:1.1827, rc_train2:0.9883, info:0.0096
[21] ner test1:2.6578, rc test1:8.3357, ner test2:2.2143, rc test2:1.5992, info:0.0255
[21] precision:0.8454, recall:0.8427, f1:0.8440
[*] saved best model!
[22] ner train1:2.0921, rc_train1:7.8423, ner train2:1.1461, rc_train2:0.9582, info:0.0138
[22] ner test1:2.6493, rc test1:8.1779, ner test2:2.1341, rc test2:1.5719, info:0.0096
[22] precision:0.8821, recall:0.8141, f1:0.8467
[*] saved best model!
[23] ner train1:2.0790, rc_train1:7.7795, ner train2:1.1229, rc_train2:0.9310, info:0.0091
[23] ner test1:2.6576, rc test1:7.7905, ner test2:2.1609, rc test2:1.6071, info:0.0066
[23] precision:0.8397, recall:0.8483, f1:0.8440
[24] ner train1:2.0416, rc_train1:7.7156, ner train2:1.0784, rc_train2:0.9017, info:0.0098
[24] ner test1:2.6986, rc test1:8.0073, ner test2:2.1356, rc test2:1.5662, info:0.0069
[24] precision:0.8887, recall:0.8129, f1:0.8491
[*] saved best model!
[25] ner train1:2.0368, rc_train1:7.6755, ner train2:1.0536, rc_train2:0.8637, info:0.0079
[25] ner test1:2.6632, rc test1:7.9641, ner test2:2.1520, rc test2:1.5761, info:0.0067
[25] precision:0.8616, recall:0.8446, f1:0.8531
[*] saved best model!
[26] ner train1:2.0033, rc_train1:7.6594, ner train2:1.0196, rc_train2:0.8430, info:0.0080
[26] ner test1:2.6747, rc test1:7.8576, ner test2:2.1793, rc test2:1.5718, info:0.0099
[26] precision:0.8828, recall:0.8248, f1:0.8528
[27] ner train1:1.9862, rc_train1:7.6546, ner train2:0.9929, rc_train2:0.8216, info:0.0090
[27] ner test1:2.7374, rc test1:8.2259, ner test2:2.2253, rc test2:1.6373, info:0.0149
[27] precision:0.8652, recall:0.8397, f1:0.8523
[28] ner train1:1.9696, rc_train1:7.5311, ner train2:0.9741, rc_train2:0.8111, info:0.0165
[28] ner test1:2.6650, rc test1:7.8000, ner test2:2.2319, rc test2:1.5739, info:0.0234
[28] precision:0.8590, recall:0.8460, f1:0.8525
[29] ner train1:1.9555, rc_train1:7.5452, ner train2:0.9505, rc_train2:0.7917, info:0.0824
[29] ner test1:2.6702, rc test1:7.6912, ner test2:2.2594, rc test2:1.6016, info:0.0154
[29] precision:0.8751, recall:0.8326, f1:0.8533
[*] saved best model!
[30] ner train1:1.9488, rc_train1:7.5430, ner train2:0.9391, rc_train2:0.7792, info:0.0177
[30] ner test1:2.6761, rc test1:7.9333, ner test2:2.2890, rc test2:1.6777, info:0.0081
[30] precision:0.8744, recall:0.8360, f1:0.8548
[*] saved best model!
[31] ner train1:1.9341, rc_train1:7.5286, ner train2:0.9199, rc_train2:0.7606, info:0.0594
[31] ner test1:2.7011, rc test1:7.5752, ner test2:2.4499, rc test2:1.8337, info:0.0251
[31] precision:0.8067, recall:0.8723, f1:0.8382
[32] ner train1:1.9168, rc_train1:7.5211, ner train2:0.8929, rc_train2:0.7504, info:0.0106
[32] ner test1:2.7237, rc test1:7.9432, ner test2:2.3906, rc test2:1.6398, info:0.0084
[32] precision:0.8513, recall:0.8552, f1:0.8532
[33] ner train1:1.9034, rc_train1:7.4593, ner train2:0.8869, rc_train2:0.7327, info:0.0202
[33] ner test1:2.6728, rc test1:7.7456, ner test2:2.2698, rc test2:1.6115, info:0.0144
[33] precision:0.8587, recall:0.8519, f1:0.8553
[*] saved best model!
[34] ner train1:1.8802, rc_train1:7.4475, ner train2:0.8504, rc_train2:0.7114, info:0.0119
[34] ner test1:2.7171, rc test1:7.5140, ner test2:2.4168, rc test2:1.7101, info:0.0078
[34] precision:0.8444, recall:0.8658, f1:0.8550
[35] ner train1:1.8726, rc_train1:7.3859, ner train2:0.8400, rc_train2:0.6996, info:0.0097
[35] ner test1:2.7124, rc test1:7.4731, ner test2:2.3751, rc test2:1.6504, info:0.0085
[35] precision:0.8632, recall:0.8499, f1:0.8565
[*] saved best model!
[36] ner train1:1.8479, rc_train1:7.3228, ner train2:0.8154, rc_train2:0.6783, info:0.0381
[36] ner test1:2.7253, rc test1:7.4205, ner test2:2.4426, rc test2:1.6584, info:0.0150
[36] precision:0.8548, recall:0.8604, f1:0.8576
[*] saved best model!
[37] ner train1:1.8498, rc_train1:7.3118, ner train2:0.8147, rc_train2:0.6829, info:0.0878
[37] ner test1:2.7050, rc test1:7.2395, ner test2:2.3463, rc test2:1.7130, info:0.0192
[37] precision:0.8461, recall:0.8629, f1:0.8544
[38] ner train1:1.8387, rc_train1:7.1779, ner train2:0.8016, rc_train2:0.6567, info:0.2177
[38] ner test1:2.7150, rc test1:7.7241, ner test2:2.4170, rc test2:1.6721, info:0.0091
[38] precision:0.8894, recall:0.8376, f1:0.8627
[*] saved best model!
[39] ner train1:1.8377, rc_train1:7.2814, ner train2:0.7923, rc_train2:0.6592, info:0.0137
[39] ner test1:2.6922, rc test1:7.5035, ner test2:2.4466, rc test2:1.6941, info:0.0151
[39] precision:0.8705, recall:0.8568, f1:0.8636
[*] saved best model!
[40] ner train1:1.8169, rc_train1:7.2617, ner train2:0.7761, rc_train2:0.6502, info:0.0234
[40] ner test1:2.7271, rc test1:7.4537, ner test2:2.4262, rc test2:1.7347, info:0.0112
[40] precision:0.8616, recall:0.8539, f1:0.8577
[41] ner train1:1.8116, rc_train1:7.2434, ner train2:0.7583, rc_train2:0.6349, info:0.0314
[41] ner test1:2.7204, rc test1:7.6425, ner test2:2.4483, rc test2:1.7491, info:0.0105
[41] precision:0.8610, recall:0.8597, f1:0.8603
[42] ner train1:1.7909, rc_train1:7.2510, ner train2:0.7459, rc_train2:0.6334, info:0.0171
[42] ner test1:2.7425, rc test1:7.5714, ner test2:2.4285, rc test2:1.7004, info:0.0148
[42] precision:0.8667, recall:0.8552, f1:0.8609
[43] ner train1:1.7868, rc_train1:7.1852, ner train2:0.7221, rc_train2:0.6054, info:0.0135
[43] ner test1:2.7099, rc test1:7.4504, ner test2:2.5633, rc test2:1.7572, info:0.0089
[43] precision:0.8534, recall:0.8535, f1:0.8535
[44] ner train1:1.7707, rc_train1:7.1779, ner train2:0.7095, rc_train2:0.5967, info:0.3204
[44] ner test1:2.7558, rc test1:7.4599, ner test2:2.6111, rc test2:1.7383, info:0.0075
[44] precision:0.8423, recall:0.8665, f1:0.8542
[45] ner train1:1.7537, rc_train1:7.1243, ner train2:0.6955, rc_train2:0.5837, info:0.0110
[45] ner test1:2.7100, rc test1:7.0667, ner test2:2.5154, rc test2:1.7545, info:0.0070
[45] precision:0.8562, recall:0.8587, f1:0.8574
[46] ner train1:1.7413, rc_train1:7.1154, ner train2:0.6769, rc_train2:0.5641, info:0.7855
[46] ner test1:2.7137, rc test1:7.3756, ner test2:2.5299, rc test2:1.6828, info:0.0245
[46] precision:0.8645, recall:0.8602, f1:0.8624
[47] ner train1:1.7389, rc_train1:7.0641, ner train2:0.6732, rc_train2:0.5721, info:0.0208
[47] ner test1:2.7007, rc test1:7.2583, ner test2:2.5330, rc test2:1.7222, info:0.0190
[47] precision:0.8568, recall:0.8583, f1:0.8576
[48] ner train1:1.7299, rc_train1:7.0262, ner train2:0.6781, rc_train2:0.5717, info:0.0215
[48] ner test1:2.6935, rc test1:7.5356, ner test2:2.5071, rc test2:1.7725, info:0.0114
[48] precision:0.8627, recall:0.8519, f1:0.8572
[49] ner train1:1.7171, rc_train1:7.0804, ner train2:0.6557, rc_train2:0.5468, info:0.0201
[49] ner test1:2.7411, rc test1:7.3837, ner test2:2.6445, rc test2:1.7253, info:0.0106
[49] precision:0.8591, recall:0.8668, f1:0.8629
[50] ner train1:1.7151, rc_train1:7.0596, ner train2:0.6466, rc_train2:0.5585, info:0.0240
[50] ner test1:2.7581, rc test1:7.6131, ner test2:2.5725, rc test2:1.7223, info:0.0170
[50] precision:0.8641, recall:0.8545, f1:0.8593
[51] ner train1:1.7198, rc_train1:7.0020, ner train2:0.6534, rc_train2:0.5578, info:0.0159
[51] ner test1:2.7948, rc test1:7.3400, ner test2:2.5969, rc test2:1.7759, info:0.0118
[51] precision:0.8560, recall:0.8640, f1:0.8600
[52] ner train1:1.7070, rc_train1:6.9136, ner train2:0.6361, rc_train2:0.5410, info:0.0192
[52] ner test1:2.7388, rc test1:7.1085, ner test2:2.6514, rc test2:1.7738, info:0.0098
[52] precision:0.8537, recall:0.8628, f1:0.8582
[53] ner train1:1.6986, rc_train1:6.9469, ner train2:0.6136, rc_train2:0.5259, info:0.0169
[53] ner test1:2.7905, rc test1:7.3743, ner test2:2.7016, rc test2:1.8140, info:0.0201
[53] precision:0.8688, recall:0.8385, f1:0.8534
[54] ner train1:1.6760, rc_train1:6.9384, ner train2:0.6002, rc_train2:0.5055, info:0.0120
[54] ner test1:2.7277, rc test1:7.0949, ner test2:2.5335, rc test2:1.7609, info:0.0073
[54] precision:0.8747, recall:0.8518, f1:0.8631
[55] ner train1:1.6704, rc_train1:6.8911, ner train2:0.5862, rc_train2:0.4993, info:0.0102
[55] ner test1:2.7454, rc test1:7.1275, ner test2:2.5760, rc test2:1.7644, info:0.0297
[55] precision:0.8599, recall:0.8668, f1:0.8634
[56] ner train1:1.6630, rc_train1:6.9389, ner train2:0.5751, rc_train2:0.4937, info:0.1477
[56] ner test1:2.7567, rc test1:7.5869, ner test2:2.7111, rc test2:1.8573, info:0.0082
[56] precision:0.8849, recall:0.8355, f1:0.8595
[57] ner train1:1.6575, rc_train1:6.8489, ner train2:0.5812, rc_train2:0.4975, info:0.0231
[57] ner test1:2.8111, rc test1:7.3824, ner test2:2.7010, rc test2:1.8946, info:0.0125
[57] precision:0.8756, recall:0.8478, f1:0.8615
[58] ner train1:1.6639, rc_train1:6.8689, ner train2:0.5768, rc_train2:0.4934, info:0.0151
[58] ner test1:2.7787, rc test1:7.3987, ner test2:2.6904, rc test2:1.8564, info:0.0147
[58] precision:0.8743, recall:0.8486, f1:0.8613
[59] ner train1:1.6538, rc_train1:6.7669, ner train2:0.5729, rc_train2:0.4868, info:0.0136
[59] ner test1:2.7913, rc test1:7.4587, ner test2:2.6450, rc test2:1.8435, info:0.0088
[59] precision:0.8741, recall:0.8418, f1:0.8577
[60] ner train1:1.6461, rc_train1:6.8042, ner train2:0.5656, rc_train2:0.4849, info:0.0138
[60] ner test1:2.8036, rc test1:7.3723, ner test2:2.8086, rc test2:1.8826, info:0.0164
[60] precision:0.8605, recall:0.8661, f1:0.8633
[61] ner train1:1.6397, rc_train1:6.7527, ner train2:0.5543, rc_train2:0.4691, info:0.0596
[61] ner test1:2.8136, rc test1:7.5237, ner test2:2.7553, rc test2:1.8758, info:0.0097
[61] precision:0.8730, recall:0.8609, f1:0.8669
[*] saved best model!
[62] ner train1:1.6327, rc_train1:6.7620, ner train2:0.5462, rc_train2:0.4649, info:0.0121
[62] ner test1:2.8267, rc test1:6.8816, ner test2:2.7738, rc test2:1.8808, info:0.0098
[62] precision:0.8601, recall:0.8678, f1:0.8639
[63] ner train1:1.6190, rc_train1:6.6738, ner train2:0.5332, rc_train2:0.4622, info:0.0149
[63] ner test1:2.7726, rc test1:7.4063, ner test2:2.7585, rc test2:1.8693, info:0.0089
[63] precision:0.8650, recall:0.8582, f1:0.8616
[64] ner train1:1.6122, rc_train1:6.7242, ner train2:0.5191, rc_train2:0.4576, info:0.0106
[64] ner test1:2.8409, rc test1:7.3368, ner test2:2.7734, rc test2:1.9111, info:0.0123
[64] precision:0.8602, recall:0.8620, f1:0.8611
[65] ner train1:1.6043, rc_train1:6.7158, ner train2:0.5167, rc_train2:0.4489, info:0.0124
[65] ner test1:2.8223, rc test1:6.9476, ner test2:2.7792, rc test2:1.8467, info:0.0108
[65] precision:0.8655, recall:0.8649, f1:0.8652
[66] ner train1:1.5991, rc_train1:6.6818, ner train2:0.5050, rc_train2:0.4482, info:0.0152
[66] ner test1:2.8491, rc test1:7.1476, ner test2:2.8516, rc test2:1.9383, info:0.0140
[66] precision:0.8491, recall:0.8745, f1:0.8616
[67] ner train1:1.5907, rc_train1:6.5850, ner train2:0.5101, rc_train2:0.4472, info:0.0182
[67] ner test1:2.7871, rc test1:7.0789, ner test2:2.8603, rc test2:1.9243, info:0.0197
[67] precision:0.8591, recall:0.8655, f1:0.8623
[68] ner train1:1.5703, rc_train1:6.5918, ner train2:0.4839, rc_train2:0.4265, info:0.0087
[68] ner test1:2.7792, rc test1:6.9828, ner test2:2.8308, rc test2:1.8717, info:0.0070
[68] precision:0.8716, recall:0.8520, f1:0.8617
[69] ner train1:1.5808, rc_train1:6.6422, ner train2:0.4849, rc_train2:0.4254, info:0.0123
[69] ner test1:2.8158, rc test1:7.0294, ner test2:2.8532, rc test2:1.9941, info:0.0102
[69] precision:0.8595, recall:0.8615, f1:0.8605
[70] ner train1:1.5591, rc_train1:6.5244, ner train2:0.4698, rc_train2:0.4240, info:0.0436
[70] ner test1:2.9019, rc test1:7.0545, ner test2:2.9334, rc test2:1.9320, info:0.0129
[70] precision:0.8649, recall:0.8598, f1:0.8624
[71] ner train1:1.5674, rc_train1:6.5708, ner train2:0.4735, rc_train2:0.4147, info:0.0154
[71] ner test1:2.8653, rc test1:7.0878, ner test2:2.9233, rc test2:1.9722, info:0.0130
[71] precision:0.8391, recall:0.8808, f1:0.8594
[72] ner train1:1.5708, rc_train1:6.5282, ner train2:0.4733, rc_train2:0.4228, info:0.0114
[72] ner test1:2.8560, rc test1:7.2278, ner test2:2.9086, rc test2:1.9412, info:0.0114
[72] precision:0.8623, recall:0.8619, f1:0.8621
[73] ner train1:1.5520, rc_train1:6.5461, ner train2:0.4671, rc_train2:0.4134, info:0.1178
[73] ner test1:2.8299, rc test1:6.8043, ner test2:2.9184, rc test2:1.9736, info:0.0074
[73] precision:0.8269, recall:0.8851, f1:0.8550
[74] ner train1:1.5453, rc_train1:6.5107, ner train2:0.4605, rc_train2:0.4050, info:0.0199
[74] ner test1:2.8013, rc test1:6.7709, ner test2:2.8859, rc test2:1.9779, info:0.0237
[74] precision:0.8442, recall:0.8782, f1:0.8609
[75] ner train1:1.5411, rc_train1:6.4650, ner train2:0.4612, rc_train2:0.4108, info:0.0148
[75] ner test1:2.8757, rc test1:7.0067, ner test2:2.9058, rc test2:1.9407, info:0.0076
[75] precision:0.8571, recall:0.8708, f1:0.8639
[76] ner train1:1.5288, rc_train1:6.4438, ner train2:0.4454, rc_train2:0.4055, info:0.0134
[76] ner test1:2.8277, rc test1:6.8427, ner test2:2.9399, rc test2:1.9426, info:0.0126
[76] precision:0.8665, recall:0.8610, f1:0.8638
[77] ner train1:1.5230, rc_train1:6.4519, ner train2:0.4392, rc_train2:0.3977, info:0.9141
[77] ner test1:2.9006, rc test1:7.2013, ner test2:2.9829, rc test2:1.9628, info:0.1393
[77] precision:0.8720, recall:0.8540, f1:0.8629
[78] ner train1:1.5319, rc_train1:6.4868, ner train2:0.4456, rc_train2:0.3932, info:0.0408
[78] ner test1:2.8992, rc test1:7.1105, ner test2:2.9658, rc test2:1.9985, info:0.0195
[78] precision:0.8743, recall:0.8591, f1:0.8666
[79] ner train1:1.5329, rc_train1:6.4991, ner train2:0.4429, rc_train2:0.4002, info:0.0162
[79] ner test1:2.7941, rc test1:7.0422, ner test2:2.9077, rc test2:1.9429, info:0.0154
[79] precision:0.8820, recall:0.8555, f1:0.8686
[*] saved best model!
[80] ner train1:1.5286, rc_train1:6.4754, ner train2:0.4380, rc_train2:0.4000, info:0.0993
[80] ner test1:2.8438, rc test1:6.8712, ner test2:2.9540, rc test2:1.9651, info:0.0069
[80] precision:0.8720, recall:0.8570, f1:0.8644
[81] ner train1:1.5185, rc_train1:6.4663, ner train2:0.4280, rc_train2:0.3853, info:0.3648
[81] ner test1:2.8509, rc test1:6.9613, ner test2:2.9700, rc test2:2.0140, info:0.0088
[81] precision:0.8495, recall:0.8740, f1:0.8616
[82] ner train1:1.5185, rc_train1:6.3944, ner train2:0.4275, rc_train2:0.3912, info:0.0293
[82] ner test1:2.8352, rc test1:6.9072, ner test2:2.8127, rc test2:1.9551, info:0.0083
[82] precision:0.8811, recall:0.8535, f1:0.8671
[83] ner train1:1.5141, rc_train1:6.4174, ner train2:0.4207, rc_train2:0.3926, info:0.0559
[83] ner test1:2.8250, rc test1:6.8267, ner test2:2.9784, rc test2:1.9567, info:0.0207
[83] precision:0.8434, recall:0.8815, f1:0.8621
[84] ner train1:1.5154, rc_train1:6.4208, ner train2:0.4266, rc_train2:0.3965, info:0.0140
[84] ner test1:2.9160, rc test1:6.8053, ner test2:3.0077, rc test2:2.0492, info:0.0130
[84] precision:0.8517, recall:0.8692, f1:0.8604
[85] ner train1:1.5103, rc_train1:6.3509, ner train2:0.4221, rc_train2:0.3774, info:0.0481
[85] ner test1:2.8106, rc test1:6.8651, ner test2:3.0497, rc test2:2.0159, info:0.0248
[85] precision:0.8594, recall:0.8660, f1:0.8627
[86] ner train1:1.4877, rc_train1:6.4267, ner train2:0.3957, rc_train2:0.3769, info:0.0221
[86] ner test1:2.8816, rc test1:6.8435, ner test2:3.1105, rc test2:2.0360, info:0.0143
[86] precision:0.8542, recall:0.8658, f1:0.8600
[87] ner train1:1.4896, rc_train1:6.3572, ner train2:0.4097, rc_train2:0.3785, info:0.0164
[87] ner test1:2.8395, rc test1:6.6494, ner test2:2.9985, rc test2:2.0471, info:0.0108
[87] precision:0.8581, recall:0.8617, f1:0.8598
[88] ner train1:1.4850, rc_train1:6.2951, ner train2:0.4039, rc_train2:0.3756, info:0.1645
[88] ner test1:2.8660, rc test1:6.8912, ner test2:2.9198, rc test2:2.0024, info:0.0101
[88] precision:0.8642, recall:0.8666, f1:0.8654
[89] ner train1:1.4828, rc_train1:6.3191, ner train2:0.4016, rc_train2:0.3760, info:0.0196
[89] ner test1:2.8659, rc test1:6.8414, ner test2:3.0159, rc test2:2.0527, info:0.0386
[89] precision:0.8616, recall:0.8695, f1:0.8655
[90] ner train1:1.4820, rc_train1:6.3341, ner train2:0.3984, rc_train2:0.3709, info:0.1421
[90] ner test1:2.8745, rc test1:6.9030, ner test2:3.0267, rc test2:2.1305, info:0.0162
[90] precision:0.8614, recall:0.8658, f1:0.8636
[91] ner train1:1.4788, rc_train1:6.3381, ner train2:0.4155, rc_train2:0.3880, info:0.3197
[91] ner test1:2.8256, rc test1:6.6367, ner test2:2.9736, rc test2:2.0180, info:0.0161
[91] precision:0.8734, recall:0.8594, f1:0.8664
[92] ner train1:1.4741, rc_train1:6.3315, ner train2:0.3959, rc_train2:0.3740, info:0.0451
[92] ner test1:2.8960, rc test1:7.2142, ner test2:3.1982, rc test2:2.1005, info:0.0234
[92] precision:0.8710, recall:0.8547, f1:0.8628
[93] ner train1:1.4632, rc_train1:6.3903, ner train2:0.3972, rc_train2:0.3756, info:0.2313
[93] ner test1:2.8630, rc test1:6.9449, ner test2:3.0259, rc test2:2.0705, info:0.0150
[93] precision:0.8602, recall:0.8719, f1:0.8660