random10_n9_exact

[1] ner train1:8.2224, rc_train1:1486.6686, ner train2:5.0233, rc_train2:334.4553, info:0.0001
[1] ner test1:3.9375, rc test1:11.1671, ner test2:3.1077, rc test2:3.2128, info:0.0000
[1] precision:0.6272, recall:0.5831, f1:0.6043
[*] saved best model!
[2] ner train1:3.8343, rc_train1:10.5061, ner train2:2.9603, rc_train2:2.9698, info:0.0000
[2] ner test1:3.4551, rc test1:10.3788, ner test2:2.6231, rc test2:2.5376, info:0.0000
[2] precision:0.7446, recall:0.5577, f1:0.6378
[*] saved best model!
[3] ner train1:3.4083, rc_train1:9.0018, ner train2:2.6034, rc_train2:2.4754, info:0.0000
[3] ner test1:3.1706, rc test1:8.4031, ner test2:2.3965, rc test2:2.2143, info:0.0000
[3] precision:0.7467, recall:0.6678, f1:0.7051
[*] saved best model!
[4] ner train1:3.1369, rc_train1:7.9555, ner train2:2.3641, rc_train2:2.1945, info:0.0000
[4] ner test1:3.0283, rc test1:7.7047, ner test2:2.3070, rc test2:2.0410, info:0.0000
[4] precision:0.7673, recall:0.6894, f1:0.7262
[*] saved best model!
[5] ner train1:2.9490, rc_train1:7.5662, ner train2:2.1829, rc_train2:1.9880, info:0.0000
[5] ner test1:2.9490, rc test1:7.7481, ner test2:2.2128, rc test2:1.9250, info:0.0000
[5] precision:0.8013, recall:0.6708, f1:0.7303
[*] saved best model!
[6] ner train1:2.7988, rc_train1:7.2985, ner train2:2.0433, rc_train2:1.8383, info:0.0000
[6] ner test1:2.8641, rc test1:7.7166, ner test2:2.1545, rc test2:1.8867, info:0.0000
[6] precision:0.8081, recall:0.6943, f1:0.7469
[*] saved best model!
[7] ner train1:2.6737, rc_train1:7.1176, ner train2:1.9182, rc_train2:1.7203, info:0.0000
[7] ner test1:2.8197, rc test1:7.5452, ner test2:2.1095, rc test2:1.7977, info:0.0000
[7] precision:0.7623, recall:0.7395, f1:0.7507
[*] saved best model!
[8] ner train1:2.5708, rc_train1:7.1381, ner train2:1.8037, rc_train2:1.6096, info:0.0000
[8] ner test1:2.7666, rc test1:7.4831, ner test2:2.0721, rc test2:1.7112, info:0.0000
[8] precision:0.7916, recall:0.7192, f1:0.7537
[*] saved best model!
[9] ner train1:2.5033, rc_train1:7.1790, ner train2:1.7290, rc_train2:1.5256, info:0.0000
[9] ner test1:2.7275, rc test1:7.2929, ner test2:2.0398, rc test2:1.6907, info:0.0000
[9] precision:0.8082, recall:0.7338, f1:0.7692
[*] saved best model!
[10] ner train1:2.4238, rc_train1:7.2139, ner train2:1.6332, rc_train2:1.4543, info:0.0000
[10] ner test1:2.6973, rc test1:7.2254, ner test2:2.0449, rc test2:1.6396, info:0.0000
[10] precision:0.8132, recall:0.7422, f1:0.7760
[*] saved best model!
[11] ner train1:2.3704, rc_train1:7.2610, ner train2:1.5667, rc_train2:1.3702, info:0.0000
[11] ner test1:2.6955, rc test1:7.3775, ner test2:2.0570, rc test2:1.6360, info:0.0000
[11] precision:0.8161, recall:0.7440, f1:0.7784
[*] saved best model!
[12] ner train1:2.3156, rc_train1:7.1748, ner train2:1.4981, rc_train2:1.2974, info:0.0000
[12] ner test1:2.6814, rc test1:7.4666, ner test2:2.0644, rc test2:1.6330, info:0.0000
[12] precision:0.7815, recall:0.7640, f1:0.7727
[13] ner train1:2.2762, rc_train1:7.2646, ner train2:1.4366, rc_train2:1.2551, info:0.0000
[13] ner test1:2.7270, rc test1:7.6980, ner test2:2.0814, rc test2:1.6182, info:0.0000
[13] precision:0.7762, recall:0.7693, f1:0.7727
[14] ner train1:2.2324, rc_train1:7.2247, ner train2:1.3838, rc_train2:1.2029, info:0.0000
[14] ner test1:2.6953, rc test1:7.0422, ner test2:2.0677, rc test2:1.6342, info:0.0000
[14] precision:0.7720, recall:0.7877, f1:0.7797
[*] saved best model!
[15] ner train1:2.1914, rc_train1:7.2462, ner train2:1.3299, rc_train2:1.1540, info:0.0000
[15] ner test1:2.6849, rc test1:7.6255, ner test2:2.0481, rc test2:1.6268, info:0.0000
[15] precision:0.7729, recall:0.7926, f1:0.7826
[*] saved best model!
[16] ner train1:2.1478, rc_train1:7.3299, ner train2:1.2696, rc_train2:1.1019, info:0.0000
[16] ner test1:2.6472, rc test1:7.5406, ner test2:2.0671, rc test2:1.5502, info:0.0000
[16] precision:0.7868, recall:0.7744, f1:0.7805
[17] ner train1:2.1262, rc_train1:7.2761, ner train2:1.2351, rc_train2:1.0592, info:0.0000
[17] ner test1:2.6433, rc test1:7.4490, ner test2:2.0498, rc test2:1.5997, info:0.0000
[17] precision:0.7669, recall:0.8014, f1:0.7838
[*] saved best model!
[18] ner train1:2.0996, rc_train1:7.2726, ner train2:1.1951, rc_train2:1.0173, info:0.0000
[18] ner test1:2.6496, rc test1:7.3361, ner test2:2.0674, rc test2:1.5846, info:0.0000
[18] precision:0.7804, recall:0.8060, f1:0.7930
[*] saved best model!
[19] ner train1:2.0728, rc_train1:7.3119, ner train2:1.1594, rc_train2:0.9865, info:0.0000
[19] ner test1:2.6350, rc test1:7.4756, ner test2:2.0794, rc test2:1.5795, info:0.0000
[19] precision:0.7875, recall:0.8030, f1:0.7952
[*] saved best model!
[20] ner train1:2.0368, rc_train1:7.3069, ner train2:1.1085, rc_train2:0.9515, info:0.0000
[20] ner test1:2.6268, rc test1:7.5777, ner test2:2.1004, rc test2:1.5313, info:0.0000
[20] precision:0.7916, recall:0.7832, f1:0.7874
[21] ner train1:2.0186, rc_train1:7.3334, ner train2:1.0836, rc_train2:0.9195, info:0.0000
[21] ner test1:2.6666, rc test1:7.4595, ner test2:2.1313, rc test2:1.5783, info:0.0000
[21] precision:0.7933, recall:0.7973, f1:0.7953
[*] saved best model!
[22] ner train1:1.9942, rc_train1:7.2935, ner train2:1.0497, rc_train2:0.9041, info:0.0000
[22] ner test1:2.6650, rc test1:7.7038, ner test2:2.1313, rc test2:1.5447, info:0.0000
[22] precision:0.7903, recall:0.8025, f1:0.7963
[*] saved best model!
[23] ner train1:1.9606, rc_train1:7.3642, ner train2:1.0102, rc_train2:0.8665, info:0.0000
[23] ner test1:2.6615, rc test1:7.4688, ner test2:2.1203, rc test2:1.5520, info:0.0000
[23] precision:0.7799, recall:0.7917, f1:0.7858
[24] ner train1:1.9493, rc_train1:7.2783, ner train2:0.9890, rc_train2:0.8437, info:0.0002
[24] ner test1:2.6330, rc test1:7.5033, ner test2:2.1414, rc test2:1.5708, info:0.0000
[24] precision:0.8057, recall:0.7977, f1:0.8017
[*] saved best model!
[25] ner train1:1.9244, rc_train1:7.2880, ner train2:0.9674, rc_train2:0.8266, info:0.0000
[25] ner test1:2.6325, rc test1:7.1165, ner test2:2.1800, rc test2:1.5731, info:0.0000
[25] precision:0.8039, recall:0.7956, f1:0.7997
[26] ner train1:1.9134, rc_train1:7.2281, ner train2:0.9369, rc_train2:0.7969, info:0.0001
[26] ner test1:2.6245, rc test1:7.5123, ner test2:2.2532, rc test2:1.6232, info:0.0000
[26] precision:0.8003, recall:0.8159, f1:0.8080
[*] saved best model!
[27] ner train1:1.8877, rc_train1:7.1880, ner train2:0.9081, rc_train2:0.7785, info:0.0000
[27] ner test1:2.7179, rc test1:7.4227, ner test2:2.2346, rc test2:1.6352, info:0.0000
[27] precision:0.7956, recall:0.8097, f1:0.8026
[28] ner train1:1.8725, rc_train1:7.1868, ner train2:0.8844, rc_train2:0.7569, info:0.0000
[28] ner test1:2.6396, rc test1:7.4326, ner test2:2.2432, rc test2:1.6307, info:0.0000
[28] precision:0.7961, recall:0.8206, f1:0.8082
[*] saved best model!
[29] ner train1:1.8549, rc_train1:7.1135, ner train2:0.8619, rc_train2:0.7336, info:0.0000
[29] ner test1:2.6480, rc test1:7.3755, ner test2:2.1619, rc test2:1.5888, info:0.0000
[29] precision:0.8142, recall:0.8080, f1:0.8111
[*] saved best model!
[30] ner train1:1.8387, rc_train1:7.1562, ner train2:0.8399, rc_train2:0.7217, info:0.0000
[30] ner test1:2.6257, rc test1:7.5737, ner test2:2.2424, rc test2:1.5958, info:0.0000
[30] precision:0.8264, recall:0.7965, f1:0.8112
[*] saved best model!
[31] ner train1:1.8166, rc_train1:7.1471, ner train2:0.8156, rc_train2:0.7071, info:0.0000
[31] ner test1:2.6766, rc test1:7.1973, ner test2:2.3031, rc test2:1.6254, info:0.0000
[31] precision:0.8347, recall:0.8192, f1:0.8269
[*] saved best model!
[32] ner train1:1.8134, rc_train1:7.0851, ner train2:0.8016, rc_train2:0.6845, info:0.0000
[32] ner test1:2.6735, rc test1:7.1877, ner test2:2.2338, rc test2:1.6008, info:0.0000
[32] precision:0.7984, recall:0.8100, f1:0.8041
[33] ner train1:1.7871, rc_train1:7.0413, ner train2:0.7860, rc_train2:0.6847, info:0.0000
[33] ner test1:2.6248, rc test1:7.2175, ner test2:2.3936, rc test2:1.7246, info:0.0000
[33] precision:0.7858, recall:0.8302, f1:0.8074
[34] ner train1:1.7583, rc_train1:7.0400, ner train2:0.7613, rc_train2:0.6610, info:0.0000
[34] ner test1:2.6492, rc test1:7.0894, ner test2:2.2757, rc test2:1.6387, info:0.0000
[34] precision:0.8112, recall:0.8047, f1:0.8079
[35] ner train1:1.7617, rc_train1:6.9873, ner train2:0.7463, rc_train2:0.6444, info:0.0000
[35] ner test1:2.6713, rc test1:7.2022, ner test2:2.3221, rc test2:1.6631, info:0.0000
[35] precision:0.8064, recall:0.8074, f1:0.8069
[36] ner train1:1.7490, rc_train1:7.0305, ner train2:0.7231, rc_train2:0.6336, info:0.0000
[36] ner test1:2.6595, rc test1:7.3802, ner test2:2.3774, rc test2:1.6368, info:0.0001
[36] precision:0.8197, recall:0.8152, f1:0.8174
[37] ner train1:1.7402, rc_train1:6.9434, ner train2:0.7110, rc_train2:0.6113, info:0.0000
[37] ner test1:2.6779, rc test1:7.0920, ner test2:2.4328, rc test2:1.6291, info:0.0000
[37] precision:0.8132, recall:0.8148, f1:0.8140
[38] ner train1:1.7215, rc_train1:6.9397, ner train2:0.6935, rc_train2:0.6087, info:0.0000
[38] ner test1:2.6652, rc test1:7.2110, ner test2:2.4243, rc test2:1.6828, info:0.0000
[38] precision:0.8102, recall:0.8239, f1:0.8170
[39] ner train1:1.7105, rc_train1:6.8843, ner train2:0.6919, rc_train2:0.5989, info:0.0000
[39] ner test1:2.6832, rc test1:6.9269, ner test2:2.3062, rc test2:1.6242, info:0.0000
[39] precision:0.8222, recall:0.8182, f1:0.8202
[40] ner train1:1.6958, rc_train1:6.8345, ner train2:0.6637, rc_train2:0.5904, info:0.0001
[40] ner test1:2.6746, rc test1:7.1811, ner test2:2.4022, rc test2:1.6699, info:0.0000
[40] precision:0.8323, recall:0.8165, f1:0.8243
[41] ner train1:1.6870, rc_train1:6.7835, ner train2:0.6516, rc_train2:0.5734, info:0.0000
[41] ner test1:2.6920, rc test1:6.9107, ner test2:2.4406, rc test2:1.7012, info:0.0000
[41] precision:0.8236, recall:0.8232, f1:0.8234
[42] ner train1:1.6794, rc_train1:6.7332, ner train2:0.6487, rc_train2:0.5684, info:0.0000
[42] ner test1:2.7127, rc test1:6.8731, ner test2:2.4489, rc test2:1.6891, info:0.0000
[42] precision:0.8058, recall:0.8334, f1:0.8194
[43] ner train1:1.6634, rc_train1:6.7328, ner train2:0.6270, rc_train2:0.5439, info:0.0000
[43] ner test1:2.6696, rc test1:6.9063, ner test2:2.5167, rc test2:1.7399, info:0.0001
[43] precision:0.8227, recall:0.8264, f1:0.8246
[44] ner train1:1.6657, rc_train1:6.6829, ner train2:0.6233, rc_train2:0.5402, info:0.0000
[44] ner test1:2.7097, rc test1:6.8344, ner test2:2.4350, rc test2:1.7214, info:0.0000
[44] precision:0.8168, recall:0.8157, f1:0.8162
[45] ner train1:1.6404, rc_train1:6.6402, ner train2:0.5987, rc_train2:0.5268, info:0.0001
[45] ner test1:2.6750, rc test1:7.0345, ner test2:2.5015, rc test2:1.7419, info:0.0001
[45] precision:0.8198, recall:0.8162, f1:0.8180
[46] ner train1:1.6391, rc_train1:6.5930, ner train2:0.5948, rc_train2:0.5214, info:0.0000
[46] ner test1:2.6691, rc test1:6.8691, ner test2:2.4446, rc test2:1.7028, info:0.0000
[46] precision:0.8103, recall:0.8131, f1:0.8117
[47] ner train1:1.6290, rc_train1:6.5995, ner train2:0.5789, rc_train2:0.5112, info:0.0000
[47] ner test1:2.6526, rc test1:6.9624, ner test2:2.4944, rc test2:1.6920, info:0.0000
[47] precision:0.8171, recall:0.8295, f1:0.8232
[48] ner train1:1.6211, rc_train1:6.6081, ner train2:0.5632, rc_train2:0.5038, info:0.0000
[48] ner test1:2.7244, rc test1:6.9902, ner test2:2.5309, rc test2:1.7747, info:0.0000
[48] precision:0.8151, recall:0.8281, f1:0.8216
[49] ner train1:1.6165, rc_train1:6.5391, ner train2:0.5649, rc_train2:0.4993, info:0.0001
[49] ner test1:2.7023, rc test1:6.7681, ner test2:2.4840, rc test2:1.7280, info:0.0000
[49] precision:0.8396, recall:0.8178, f1:0.8285
[*] saved best model!
[50] ner train1:1.6042, rc_train1:6.5095, ner train2:0.5541, rc_train2:0.4905, info:0.0000
[50] ner test1:2.6847, rc test1:6.6597, ner test2:2.5098, rc test2:1.7881, info:0.0000
[50] precision:0.8080, recall:0.8286, f1:0.8182
[51] ner train1:1.5989, rc_train1:6.4489, ner train2:0.5467, rc_train2:0.4933, info:0.0000
[51] ner test1:2.7196, rc test1:6.8674, ner test2:2.5722, rc test2:1.7727, info:0.0000
[51] precision:0.8237, recall:0.8205, f1:0.8221
[52] ner train1:1.5933, rc_train1:6.4546, ner train2:0.5476, rc_train2:0.4808, info:0.0000
[52] ner test1:2.7024, rc test1:6.7522, ner test2:2.5398, rc test2:1.7166, info:0.0000
[52] precision:0.8218, recall:0.8281, f1:0.8250
[53] ner train1:1.5848, rc_train1:6.4530, ner train2:0.5348, rc_train2:0.4690, info:0.0000
[53] ner test1:2.7129, rc test1:6.7127, ner test2:2.4634, rc test2:1.7821, info:0.0000
[53] precision:0.8249, recall:0.8266, f1:0.8258
[54] ner train1:1.5827, rc_train1:6.4238, ner train2:0.5269, rc_train2:0.4734, info:0.0000
[54] ner test1:2.7131, rc test1:6.6152, ner test2:2.6106, rc test2:1.7773, info:0.0000
[54] precision:0.8068, recall:0.8367, f1:0.8215
[55] ner train1:1.5830, rc_train1:6.3422, ner train2:0.5218, rc_train2:0.4627, info:0.0000
[55] ner test1:2.7100, rc test1:6.5189, ner test2:2.5617, rc test2:1.7613, info:0.0000
[55] precision:0.8187, recall:0.8248, f1:0.8217
[56] ner train1:1.5689, rc_train1:6.3143, ner train2:0.5044, rc_train2:0.4563, info:0.0000
[56] ner test1:2.7298, rc test1:6.9547, ner test2:2.6248, rc test2:1.7772, info:0.0000
[56] precision:0.8221, recall:0.8150, f1:0.8186
[57] ner train1:1.5601, rc_train1:6.3431, ner train2:0.4962, rc_train2:0.4409, info:0.0000
[57] ner test1:2.7238, rc test1:6.7798, ner test2:2.7792, rc test2:1.8568, info:0.0000
[57] precision:0.8226, recall:0.8349, f1:0.8287
[*] saved best model!
[58] ner train1:1.5440, rc_train1:6.3014, ner train2:0.4800, rc_train2:0.4379, info:0.0000
[58] ner test1:2.7219, rc test1:6.6257, ner test2:2.7029, rc test2:1.8147, info:0.0000
[58] precision:0.8336, recall:0.8218, f1:0.8277
[59] ner train1:1.5382, rc_train1:6.2357, ner train2:0.4821, rc_train2:0.4357, info:0.0000
[59] ner test1:2.7021, rc test1:6.7469, ner test2:2.6605, rc test2:1.8509, info:0.0000
[59] precision:0.8395, recall:0.8250, f1:0.8322
[*] saved best model!
[60] ner train1:1.5365, rc_train1:6.2450, ner train2:0.4756, rc_train2:0.4221, info:0.0000
[60] ner test1:2.7455, rc test1:6.5229, ner test2:2.6695, rc test2:1.8236, info:0.0000
[60] precision:0.8275, recall:0.8360, f1:0.8317
[61] ner train1:1.5293, rc_train1:6.1942, ner train2:0.4700, rc_train2:0.4241, info:0.0000
[61] ner test1:2.7137, rc test1:6.5712, ner test2:2.7252, rc test2:1.8688, info:0.0000
[61] precision:0.8042, recall:0.8392, f1:0.8213
[62] ner train1:1.5252, rc_train1:6.2405, ner train2:0.4641, rc_train2:0.4231, info:0.0001
[62] ner test1:2.7374, rc test1:6.5677, ner test2:2.6125, rc test2:1.8284, info:0.0000
[62] precision:0.8264, recall:0.8201, f1:0.8232
[63] ner train1:1.5203, rc_train1:6.2024, ner train2:0.4515, rc_train2:0.4147, info:0.0001
[63] ner test1:2.7612, rc test1:6.6778, ner test2:2.7030, rc test2:1.8566, info:0.0000
[63] precision:0.8109, recall:0.8383, f1:0.8244
[64] ner train1:1.5213, rc_train1:6.1329, ner train2:0.4542, rc_train2:0.4150, info:0.0000
[64] ner test1:2.7139, rc test1:6.3884, ner test2:2.6951, rc test2:1.8998, info:0.0000
[64] precision:0.8300, recall:0.8208, f1:0.8254
[65] ner train1:1.5144, rc_train1:6.1485, ner train2:0.4463, rc_train2:0.4252, info:0.0004
[65] ner test1:2.7468, rc test1:6.5386, ner test2:2.8356, rc test2:1.9192, info:0.0000
[65] precision:0.8241, recall:0.8414, f1:0.8327
[*] saved best model!
[66] ner train1:1.5153, rc_train1:6.1489, ner train2:0.4457, rc_train2:0.4108, info:0.0000
[66] ner test1:2.7168, rc test1:6.6422, ner test2:2.6802, rc test2:1.8726, info:0.0000
[66] precision:0.8145, recall:0.8358, f1:0.8250
[67] ner train1:1.4907, rc_train1:6.0267, ner train2:0.4289, rc_train2:0.3912, info:0.0000
[67] ner test1:2.7433, rc test1:6.4099, ner test2:2.7211, rc test2:1.8999, info:0.0000
[67] precision:0.8301, recall:0.8245, f1:0.8273
[68] ner train1:1.4902, rc_train1:6.0840, ner train2:0.4259, rc_train2:0.3909, info:0.0000
[68] ner test1:2.7370, rc test1:6.4744, ner test2:2.7332, rc test2:1.8637, info:0.0000
[68] precision:0.8262, recall:0.8307, f1:0.8285
[69] ner train1:1.4862, rc_train1:6.0691, ner train2:0.4202, rc_train2:0.3958, info:0.0000
[69] ner test1:2.7592, rc test1:6.4309, ner test2:2.8131, rc test2:1.9223, info:0.0000
[69] precision:0.8241, recall:0.8379, f1:0.8309
[70] ner train1:1.4834, rc_train1:6.0221, ner train2:0.4168, rc_train2:0.3941, info:0.0000
[70] ner test1:2.7346, rc test1:6.3563, ner test2:2.8027, rc test2:1.9494, info:0.0000
[70] precision:0.8214, recall:0.8306, f1:0.8259
[71] ner train1:1.4908, rc_train1:6.0255, ner train2:0.4189, rc_train2:0.3938, info:0.0000
[71] ner test1:2.7399, rc test1:6.3883, ner test2:2.6690, rc test2:1.8256, info:0.0000
[71] precision:0.8359, recall:0.8302, f1:0.8330
[*] saved best model!
[72] ner train1:1.4717, rc_train1:5.9913, ner train2:0.4104, rc_train2:0.3796, info:0.0000
[72] ner test1:2.7464, rc test1:6.3905, ner test2:2.7598, rc test2:1.9249, info:0.0000
[72] precision:0.8420, recall:0.8215, f1:0.8316
[73] ner train1:1.4697, rc_train1:5.9474, ner train2:0.4003, rc_train2:0.3733, info:0.0000
[73] ner test1:2.7350, rc test1:6.4053, ner test2:2.7308, rc test2:1.8622, info:0.0000
[73] precision:0.8266, recall:0.8296, f1:0.8281
[74] ner train1:1.4683, rc_train1:5.9413, ner train2:0.3970, rc_train2:0.3722, info:0.0000
[74] ner test1:2.7719, rc test1:6.3647, ner test2:2.8950, rc test2:1.9022, info:0.0000
[74] precision:0.8356, recall:0.8383, f1:0.8370
[*] saved best model!
[75] ner train1:1.4555, rc_train1:5.9312, ner train2:0.3899, rc_train2:0.3675, info:0.0000
[75] ner test1:2.7614, rc test1:6.5890, ner test2:2.7804, rc test2:1.8806, info:0.0000
[75] precision:0.8249, recall:0.8407, f1:0.8327
[76] ner train1:1.4474, rc_train1:5.9298, ner train2:0.3820, rc_train2:0.3623, info:0.0000
[76] ner test1:2.7569, rc test1:6.3092, ner test2:2.8632, rc test2:1.9799, info:0.0000
[76] precision:0.8353, recall:0.8340, f1:0.8346
[77] ner train1:1.4541, rc_train1:5.9041, ner train2:0.3943, rc_train2:0.3717, info:0.0000
[77] ner test1:2.7776, rc test1:6.0821, ner test2:2.8951, rc test2:2.0531, info:0.0000
[77] precision:0.7866, recall:0.8525, f1:0.8182
[78] ner train1:1.4429, rc_train1:5.8765, ner train2:0.3802, rc_train2:0.3603, info:0.0001
[78] ner test1:2.7518, rc test1:6.1412, ner test2:2.7343, rc test2:1.9579, info:0.0000
[78] precision:0.8423, recall:0.8280, f1:0.8351
[79] ner train1:1.4457, rc_train1:5.8289, ner train2:0.3793, rc_train2:0.3650, info:0.0001
[79] ner test1:2.7301, rc test1:6.2783, ner test2:2.8797, rc test2:2.0106, info:0.0000
[79] precision:0.8454, recall:0.8353, f1:0.8403
[*] saved best model!
[80] ner train1:1.4302, rc_train1:5.8473, ner train2:0.3716, rc_train2:0.3495, info:0.0000
[80] ner test1:2.7396, rc test1:6.2097, ner test2:2.7210, rc test2:1.8323, info:0.0000
[80] precision:0.8364, recall:0.8279, f1:0.8321
[81] ner train1:1.4281, rc_train1:5.8147, ner train2:0.3634, rc_train2:0.3389, info:0.0000
[81] ner test1:2.7370, rc test1:6.1697, ner test2:2.8158, rc test2:1.9431, info:0.0000
[81] precision:0.8199, recall:0.8277, f1:0.8238
[82] ner train1:1.4315, rc_train1:5.8022, ner train2:0.3683, rc_train2:0.3456, info:0.0000
[82] ner test1:2.7382, rc test1:6.0143, ner test2:2.8329, rc test2:1.9933, info:0.0000
[82] precision:0.8283, recall:0.8274, f1:0.8278
[83] ner train1:1.4192, rc_train1:5.7537, ner train2:0.3571, rc_train2:0.3405, info:0.0000
[83] ner test1:2.7623, rc test1:6.0850, ner test2:2.9373, rc test2:2.0136, info:0.0000
[83] precision:0.8207, recall:0.8457, f1:0.8330
[84] ner train1:1.4133, rc_train1:5.7217, ner train2:0.3636, rc_train2:0.3511, info:0.0000
[84] ner test1:2.7368, rc test1:6.1723, ner test2:2.8523, rc test2:1.9677, info:0.0000
[84] precision:0.8307, recall:0.8302, f1:0.8305
[85] ner train1:1.4113, rc_train1:5.7391, ner train2:0.3561, rc_train2:0.3393, info:0.0001
[85] ner test1:2.7512, rc test1:6.2987, ner test2:2.8822, rc test2:1.9911, info:0.0000
[85] precision:0.8217, recall:0.8349, f1:0.8283
[86] ner train1:1.4110, rc_train1:5.7209, ner train2:0.3512, rc_train2:0.3440, info:0.0000
[86] ner test1:2.7768, rc test1:5.9374, ner test2:2.8694, rc test2:1.9574, info:0.0000
[86] precision:0.8290, recall:0.8253, f1:0.8271
[87] ner train1:1.4057, rc_train1:5.6300, ner train2:0.3520, rc_train2:0.3402, info:0.0000
[87] ner test1:2.7760, rc test1:5.8815, ner test2:2.9614, rc test2:1.9896, info:0.0000
[87] precision:0.8206, recall:0.8343, f1:0.8274
[88] ner train1:1.3932, rc_train1:5.6233, ner train2:0.3459, rc_train2:0.3361, info:0.0000
[88] ner test1:2.7748, rc test1:6.1054, ner test2:2.9583, rc test2:1.9980, info:0.0000
[88] precision:0.8225, recall:0.8376, f1:0.8300
[89] ner train1:1.3952, rc_train1:5.6209, ner train2:0.3422, rc_train2:0.3391, info:0.0000
[89] ner test1:2.7826, rc test1:5.9787, ner test2:2.8606, rc test2:2.0184, info:0.0001
[89] precision:0.8072, recall:0.8186, f1:0.8129
[90] ner train1:1.3881, rc_train1:5.5810, ner train2:0.3359, rc_train2:0.3282, info:0.0000
[90] ner test1:2.8171, rc test1:5.9677, ner test2:2.9251, rc test2:1.9375, info:0.0000
[90] precision:0.8148, recall:0.8237, f1:0.8192
[91] ner train1:1.3847, rc_train1:5.6021, ner train2:0.3345, rc_train2:0.3251, info:0.0000
[91] ner test1:2.7931, rc test1:6.1859, ner test2:2.9583, rc test2:2.0318, info:0.0000
[91] precision:0.8123, recall:0.8445, f1:0.8281
[92] ner train1:1.3904, rc_train1:5.5733, ner train2:0.3382, rc_train2:0.3302, info:0.0000
[92] ner test1:2.8347, rc test1:6.1582, ner test2:3.0290, rc test2:2.0674, info:0.0000
[92] precision:0.8273, recall:0.8253, f1:0.8263
[93] ner train1:1.3815, rc_train1:5.5624, ner train2:0.3321, rc_train2:0.3245, info:0.0000
[93] ner test1:2.8014, rc test1:5.8228, ner test2:2.9963, rc test2:2.0901, info:0.0000
[93] precision:0.8063, recall:0.8375, f1:0.8216
[94] ner train1:1.3786, rc_train1:5.5410, ner train2:0.3253, rc_train2:0.3191, info:0.0000
[94] ner test1:2.7921, rc test1:5.9118, ner test2:3.0067, rc test2:2.0463, info:0.0000
[94] precision:0.8320, recall:0.8293, f1:0.8307
[95] ner train1:1.3615, rc_train1:5.5776, ner train2:0.3185, rc_train2:0.3083, info:0.0000
[95] ner test1:2.8125, rc test1:5.8642, ner test2:2.9721, rc test2:2.0765, info:0.0000
[95] precision:0.8203, recall:0.8274, f1:0.8238
[96] ner train1:1.3680, rc_train1:5.5492, ner train2:0.3209, rc_train2:0.3144, info:0.0000
[96] ner test1:2.8436, rc test1:5.9784, ner test2:3.0297, rc test2:2.0738, info:0.0000
[96] precision:0.8216, recall:0.8212, f1:0.8214
[97] ner train1:1.3678, rc_train1:5.5392, ner train2:0.3171, rc_train2:0.3154, info:0.0000
[97] ner test1:2.8073, rc test1:5.9500, ner test2:3.0050, rc test2:2.0678, info:0.0000
[97] precision:0.8249, recall:0.8293, f1:0.8271
[98] ner train1:1.3583, rc_train1:5.4913, ner train2:0.3154, rc_train2:0.3139, info:0.0000
[98] ner test1:2.8010, rc test1:5.9743, ner test2:2.9637, rc test2:1.9779, info:0.0000
[98] precision:0.8363, recall:0.8244, f1:0.8303
[99] ner train1:1.3571, rc_train1:5.5348, ner train2:0.3154, rc_train2:0.3142, info:0.0000
[99] ner test1:2.9385, rc test1:5.9302, ner test2:3.0095, rc test2:2.1162, info:0.0000
[99] precision:0.7962, recall:0.8359, f1:0.8156
[100] ner train1:1.3500, rc_train1:5.4855, ner train2:0.3108, rc_train2:0.3092, info:0.0000
[100] ner test1:2.8060, rc test1:5.8971, ner test2:3.0888, rc test2:2.1153, info:0.0000
[100] precision:0.8285, recall:0.8290, f1:0.8287