random7_n6_exact

[1] ner train1:7.3830, rc_train1:1708.9121, ner train2:5.2987, rc_train2:366.4213, info:0.0726
[1] ner test1:3.7796, rc test1:10.6088, ner test2:3.0992, rc test2:3.1880, info:0.0086
[1] precision:0.6207, recall:0.5736, f1:0.5962
[*] saved best model!
[2] ner train1:3.6899, rc_train1:11.0168, ner train2:2.9782, rc_train2:2.9620, info:0.0095
[2] ner test1:3.4027, rc test1:11.6856, ner test2:2.6331, rc test2:2.5426, info:0.0075
[2] precision:0.6873, recall:0.6286, f1:0.6567
[*] saved best model!
[3] ner train1:3.3275, rc_train1:10.0201, ner train2:2.5809, rc_train2:2.4329, info:0.0082
[3] ner test1:3.1602, rc test1:9.4128, ner test2:2.3938, rc test2:2.2510, info:0.0072
[3] precision:0.7121, recall:0.6814, f1:0.6964
[*] saved best model!
[4] ner train1:3.1116, rc_train1:8.6838, ner train2:2.3222, rc_train2:2.1460, info:0.0132
[4] ner test1:3.1147, rc test1:8.2636, ner test2:2.2949, rc test2:2.0494, info:0.0122
[4] precision:0.7454, recall:0.7264, f1:0.7358
[*] saved best model!
[5] ner train1:2.9707, rc_train1:7.8050, ner train2:2.1474, rc_train2:1.9805, info:0.0233
[5] ner test1:3.0116, rc test1:7.8692, ner test2:2.1800, rc test2:1.9692, info:0.0079
[5] precision:0.7653, recall:0.7026, f1:0.7326
[6] ner train1:2.8692, rc_train1:7.2439, ner train2:2.0332, rc_train2:1.8499, info:0.0123
[6] ner test1:2.9053, rc test1:7.3678, ner test2:2.1148, rc test2:1.8338, info:0.0118
[6] precision:0.7773, recall:0.7097, f1:0.7420
[*] saved best model!
[7] ner train1:2.7471, rc_train1:6.9738, ner train2:1.9083, rc_train2:1.7113, info:0.0090
[7] ner test1:2.8543, rc test1:6.9701, ner test2:2.0918, rc test2:1.7876, info:0.0073
[7] precision:0.7835, recall:0.7273, f1:0.7543
[*] saved best model!
[8] ner train1:2.6470, rc_train1:6.7892, ner train2:1.7947, rc_train2:1.6075, info:0.0089
[8] ner test1:2.8003, rc test1:6.5540, ner test2:2.0658, rc test2:1.7933, info:0.0087
[8] precision:0.7429, recall:0.7783, f1:0.7602
[*] saved best model!
[9] ner train1:2.5768, rc_train1:6.7416, ner train2:1.6977, rc_train2:1.5148, info:0.0334
[9] ner test1:2.7938, rc test1:6.9350, ner test2:2.0741, rc test2:1.7095, info:0.0241
[9] precision:0.7605, recall:0.7751, f1:0.7677
[*] saved best model!
[10] ner train1:2.5126, rc_train1:6.7844, ner train2:1.6227, rc_train2:1.4447, info:0.0244
[10] ner test1:2.7353, rc test1:7.2928, ner test2:2.0034, rc test2:1.6770, info:0.0168
[10] precision:0.8305, recall:0.7125, f1:0.7670
[11] ner train1:2.4927, rc_train1:6.7860, ner train2:1.6006, rc_train2:1.4347, info:0.0327
[11] ner test1:2.7700, rc test1:7.1537, ner test2:2.0313, rc test2:1.6555, info:0.0121
[11] precision:0.7992, recall:0.7435, f1:0.7704
[*] saved best model!
[12] ner train1:2.4548, rc_train1:6.7253, ner train2:1.5661, rc_train2:1.3969, info:0.0213
[12] ner test1:2.7636, rc test1:6.8152, ner test2:2.0365, rc test2:1.6716, info:0.0286
[12] precision:0.8021, recall:0.7677, f1:0.7845
[*] saved best model!
[13] ner train1:2.4271, rc_train1:6.7673, ner train2:1.5185, rc_train2:1.3389, info:0.0303
[13] ner test1:2.7490, rc test1:6.7851, ner test2:1.9737, rc test2:1.6064, info:0.0106
[13] precision:0.7815, recall:0.7707, f1:0.7761
[14] ner train1:2.3800, rc_train1:6.7418, ner train2:1.4656, rc_train2:1.2936, info:0.0119
[14] ner test1:2.7221, rc test1:7.1177, ner test2:1.9703, rc test2:1.5806, info:0.0123
[14] precision:0.7947, recall:0.7787, f1:0.7866
[*] saved best model!
[15] ner train1:2.3417, rc_train1:6.7203, ner train2:1.4004, rc_train2:1.2277, info:0.0127
[15] ner test1:2.7873, rc test1:7.0053, ner test2:1.9824, rc test2:1.5396, info:0.0141
[15] precision:0.8085, recall:0.7857, f1:0.7969
[*] saved best model!
[16] ner train1:2.3063, rc_train1:6.6807, ner train2:1.3423, rc_train2:1.1656, info:0.0140
[16] ner test1:2.6970, rc test1:7.2058, ner test2:1.9931, rc test2:1.5323, info:0.0126
[16] precision:0.8043, recall:0.7959, f1:0.8001
[*] saved best model!
[17] ner train1:2.2676, rc_train1:6.7392, ner train2:1.2877, rc_train2:1.1275, info:0.0143
[17] ner test1:2.6986, rc test1:7.2877, ner test2:1.9907, rc test2:1.5031, info:0.0106
[17] precision:0.8255, recall:0.7789, f1:0.8015
[*] saved best model!
[18] ner train1:2.2478, rc_train1:6.7308, ner train2:1.2613, rc_train2:1.0981, info:0.0162
[18] ner test1:2.7147, rc test1:7.1875, ner test2:1.9677, rc test2:1.5171, info:0.0085
[18] precision:0.8031, recall:0.7927, f1:0.7979
[19] ner train1:2.2015, rc_train1:6.7015, ner train2:1.2079, rc_train2:1.0456, info:0.0189
[19] ner test1:2.7013, rc test1:7.1983, ner test2:2.0187, rc test2:1.4901, info:0.0170
[19] precision:0.8277, recall:0.7919, f1:0.8094
[*] saved best model!
[20] ner train1:2.1964, rc_train1:6.7577, ner train2:1.1891, rc_train2:1.0319, info:0.0313
[20] ner test1:2.7698, rc test1:7.0325, ner test2:1.9900, rc test2:1.5126, info:0.0231
[20] precision:0.8062, recall:0.8027, f1:0.8044
[21] ner train1:2.1721, rc_train1:6.6273, ner train2:1.1618, rc_train2:1.0188, info:0.0208
[21] ner test1:2.7038, rc test1:7.0217, ner test2:1.9704, rc test2:1.4634, info:0.0109
[21] precision:0.8355, recall:0.7842, f1:0.8091
[22] ner train1:2.1452, rc_train1:6.7675, ner train2:1.1311, rc_train2:0.9842, info:0.0195
[22] ner test1:2.7127, rc test1:6.8295, ner test2:2.1535, rc test2:1.5836, info:0.0220
[22] precision:0.7826, recall:0.8363, f1:0.8085
[23] ner train1:2.1259, rc_train1:6.6733, ner train2:1.1033, rc_train2:0.9496, info:0.0559
[23] ner test1:2.7084, rc test1:7.2402, ner test2:2.0805, rc test2:1.5537, info:0.0152
[23] precision:0.7941, recall:0.8200, f1:0.8068
[24] ner train1:2.1205, rc_train1:6.7919, ner train2:1.0829, rc_train2:0.9356, info:0.0161
[24] ner test1:2.7121, rc test1:7.3490, ner test2:2.0106, rc test2:1.5330, info:0.0110
[24] precision:0.8364, recall:0.7917, f1:0.8135
[*] saved best model!
[25] ner train1:2.0933, rc_train1:6.7155, ner train2:1.0500, rc_train2:0.8986, info:0.0129
[25] ner test1:2.7206, rc test1:6.9124, ner test2:2.0843, rc test2:1.5257, info:0.0261
[25] precision:0.8260, recall:0.8178, f1:0.8219
[*] saved best model!
[26] ner train1:2.0623, rc_train1:6.6535, ner train2:1.0162, rc_train2:0.8727, info:0.3574
[26] ner test1:2.7132, rc test1:7.2205, ner test2:2.0646, rc test2:1.4837, info:0.0107
[26] precision:0.8283, recall:0.8175, f1:0.8229
[*] saved best model!
[27] ner train1:2.0297, rc_train1:6.6862, ner train2:0.9803, rc_train2:0.8453, info:0.0277
[27] ner test1:2.7059, rc test1:7.2715, ner test2:2.0835, rc test2:1.4873, info:0.0275
[27] precision:0.8129, recall:0.8247, f1:0.8188
[28] ner train1:2.0149, rc_train1:6.7085, ner train2:0.9601, rc_train2:0.8353, info:0.0168
[28] ner test1:2.6611, rc test1:7.0432, ner test2:2.0963, rc test2:1.5079, info:0.0097
[28] precision:0.8279, recall:0.8144, f1:0.8211
[29] ner train1:1.9979, rc_train1:6.6584, ner train2:0.9428, rc_train2:0.8060, info:0.0211
[29] ner test1:2.6778, rc test1:6.9573, ner test2:2.0458, rc test2:1.5058, info:0.0143
[29] precision:0.8172, recall:0.7994, f1:0.8082
[30] ner train1:1.9814, rc_train1:6.6891, ner train2:0.9170, rc_train2:0.7883, info:0.0268
[30] ner test1:2.6948, rc test1:6.6645, ner test2:2.0832, rc test2:1.5072, info:0.0213
[30] precision:0.8002, recall:0.8068, f1:0.8035
[31] ner train1:1.9793, rc_train1:6.7285, ner train2:0.9168, rc_train2:0.7925, info:0.0238
[31] ner test1:2.8157, rc test1:7.0627, ner test2:2.2336, rc test2:1.6460, info:0.0206
[31] precision:0.7690, recall:0.8497, f1:0.8073
[32] ner train1:1.9615, rc_train1:6.6386, ner train2:0.8924, rc_train2:0.7802, info:0.0742
[32] ner test1:2.7402, rc test1:7.1401, ner test2:2.1319, rc test2:1.5580, info:0.0130
[32] precision:0.8093, recall:0.8236, f1:0.8164
[33] ner train1:1.9586, rc_train1:6.6552, ner train2:0.8907, rc_train2:0.7641, info:0.0256
[33] ner test1:2.6666, rc test1:7.1461, ner test2:2.1119, rc test2:1.5160, info:0.0208
[33] precision:0.8152, recall:0.8243, f1:0.8197
[34] ner train1:1.9437, rc_train1:6.6876, ner train2:0.8655, rc_train2:0.7478, info:0.0168
[34] ner test1:2.7402, rc test1:6.9496, ner test2:2.1147, rc test2:1.5166, info:0.0221
[34] precision:0.8214, recall:0.8222, f1:0.8218
[35] ner train1:1.9309, rc_train1:6.6458, ner train2:0.8410, rc_train2:0.7264, info:0.0288
[35] ner test1:2.6791, rc test1:6.6888, ner test2:2.1884, rc test2:1.5448, info:0.0131
[35] precision:0.8322, recall:0.8264, f1:0.8293
[*] saved best model!
[36] ner train1:1.9098, rc_train1:6.5969, ner train2:0.8239, rc_train2:0.7106, info:0.0526
[36] ner test1:2.7130, rc test1:7.2099, ner test2:2.1946, rc test2:1.5215, info:0.0102
[36] precision:0.8268, recall:0.8344, f1:0.8306
[*] saved best model!
[37] ner train1:1.9021, rc_train1:6.6301, ner train2:0.8082, rc_train2:0.7000, info:0.0308
[37] ner test1:2.6644, rc test1:7.0292, ner test2:2.1331, rc test2:1.5629, info:0.0306
[37] precision:0.8137, recall:0.8316, f1:0.8225
[38] ner train1:1.8866, rc_train1:6.6724, ner train2:0.7864, rc_train2:0.6863, info:0.2760
[38] ner test1:2.7118, rc test1:6.8635, ner test2:2.2062, rc test2:1.5543, info:0.0154
[38] precision:0.8034, recall:0.8232, f1:0.8132
[39] ner train1:1.8785, rc_train1:6.5539, ner train2:0.7891, rc_train2:0.6829, info:0.0146
[39] ner test1:2.6843, rc test1:6.7990, ner test2:2.2481, rc test2:1.5128, info:0.0190
[39] precision:0.8383, recall:0.8312, f1:0.8347
[*] saved best model!
[40] ner train1:1.8611, rc_train1:6.5524, ner train2:0.7683, rc_train2:0.6583, info:0.0190
[40] ner test1:2.6864, rc test1:7.1625, ner test2:2.2707, rc test2:1.5805, info:0.0159
[40] precision:0.8403, recall:0.8169, f1:0.8284
[41] ner train1:1.8602, rc_train1:6.5417, ner train2:0.7533, rc_train2:0.6499, info:0.0206
[41] ner test1:2.7593, rc test1:6.7988, ner test2:2.2487, rc test2:1.5456, info:0.0131
[41] precision:0.8267, recall:0.8218, f1:0.8243
[42] ner train1:1.8554, rc_train1:6.5097, ner train2:0.7436, rc_train2:0.6464, info:0.0303
[42] ner test1:2.7610, rc test1:6.8150, ner test2:2.1817, rc test2:1.5679, info:0.0145
[42] precision:0.8300, recall:0.8364, f1:0.8332
[43] ner train1:1.8404, rc_train1:6.5222, ner train2:0.7300, rc_train2:0.6409, info:0.1905
[43] ner test1:2.7154, rc test1:7.0136, ner test2:2.2399, rc test2:1.6172, info:0.0162
[43] precision:0.8332, recall:0.8224, f1:0.8278
[44] ner train1:1.8267, rc_train1:6.5072, ner train2:0.7104, rc_train2:0.6193, info:0.0173
[44] ner test1:2.6707, rc test1:6.9136, ner test2:2.2471, rc test2:1.5414, info:0.0089
[44] precision:0.8328, recall:0.8313, f1:0.8320
[45] ner train1:1.8195, rc_train1:6.3835, ner train2:0.7010, rc_train2:0.6001, info:0.0134
[45] ner test1:2.6972, rc test1:6.7882, ner test2:2.3215, rc test2:1.6840, info:0.0124
[45] precision:0.8129, recall:0.8353, f1:0.8239
[46] ner train1:1.8038, rc_train1:6.4253, ner train2:0.6769, rc_train2:0.5872, info:0.0766
[46] ner test1:2.7344, rc test1:6.8798, ner test2:2.2771, rc test2:1.6040, info:0.0199
[46] precision:0.8147, recall:0.8348, f1:0.8246
[47] ner train1:1.7993, rc_train1:6.4019, ner train2:0.6688, rc_train2:0.5871, info:0.0292
[47] ner test1:2.7081, rc test1:6.7910, ner test2:2.2811, rc test2:1.5605, info:0.0109
[47] precision:0.8301, recall:0.8222, f1:0.8261
[48] ner train1:1.7864, rc_train1:6.4180, ner train2:0.6605, rc_train2:0.5857, info:0.0186
[48] ner test1:2.7115, rc test1:6.5048, ner test2:2.3645, rc test2:1.6305, info:0.0164
[48] precision:0.8060, recall:0.8350, f1:0.8203
[49] ner train1:1.7782, rc_train1:6.3526, ner train2:0.6542, rc_train2:0.5741, info:0.0188
[49] ner test1:2.7537, rc test1:7.1125, ner test2:2.2688, rc test2:1.5760, info:0.0103
[49] precision:0.8344, recall:0.8191, f1:0.8267
[50] ner train1:1.7593, rc_train1:6.3408, ner train2:0.6394, rc_train2:0.5655, info:0.0157
[50] ner test1:2.7172, rc test1:6.7193, ner test2:2.3384, rc test2:1.6321, info:0.0074
[50] precision:0.8219, recall:0.8211, f1:0.8215
[51] ner train1:1.7532, rc_train1:6.2703, ner train2:0.6187, rc_train2:0.5471, info:0.0094
[51] ner test1:2.7189, rc test1:6.5988, ner test2:2.4257, rc test2:1.6561, info:0.0083
[51] precision:0.8114, recall:0.8470, f1:0.8288
[52] ner train1:1.7420, rc_train1:6.2211, ner train2:0.6004, rc_train2:0.5326, info:0.0148
[52] ner test1:2.7243, rc test1:6.7751, ner test2:2.4769, rc test2:1.6751, info:0.0082
[52] precision:0.8243, recall:0.8345, f1:0.8294
[53] ner train1:1.7265, rc_train1:6.2083, ner train2:0.5925, rc_train2:0.5210, info:0.0124
[53] ner test1:2.7068, rc test1:6.5892, ner test2:2.3713, rc test2:1.7093, info:0.0101
[53] precision:0.8092, recall:0.8346, f1:0.8217
[54] ner train1:1.7098, rc_train1:6.1836, ner train2:0.5884, rc_train2:0.5205, info:0.0105
[54] ner test1:2.6974, rc test1:6.4555, ner test2:2.3943, rc test2:1.6550, info:0.0082
[54] precision:0.8257, recall:0.8314, f1:0.8286
[55] ner train1:1.7038, rc_train1:6.2407, ner train2:0.5798, rc_train2:0.5054, info:0.0130
[55] ner test1:2.7188, rc test1:6.5571, ner test2:2.4311, rc test2:1.6909, info:0.0088
[55] precision:0.8419, recall:0.8266, f1:0.8342
[56] ner train1:1.6911, rc_train1:6.1655, ner train2:0.5558, rc_train2:0.4888, info:0.0158
[56] ner test1:2.7104, rc test1:6.6355, ner test2:2.4328, rc test2:1.7164, info:0.0099
[56] precision:0.8343, recall:0.8063, f1:0.8200
[57] ner train1:1.6866, rc_train1:6.1021, ner train2:0.5590, rc_train2:0.4900, info:0.0235
[57] ner test1:2.7353, rc test1:6.8494, ner test2:2.4716, rc test2:1.7353, info:0.0080
[57] precision:0.8237, recall:0.8403, f1:0.8319
[58] ner train1:1.6789, rc_train1:6.1722, ner train2:0.5547, rc_train2:0.4923, info:0.0151
[58] ner test1:2.6927, rc test1:6.4610, ner test2:2.4082, rc test2:1.6682, info:0.0270
[58] precision:0.8197, recall:0.8350, f1:0.8273
[59] ner train1:1.6848, rc_train1:6.0486, ner train2:0.5422, rc_train2:0.4826, info:0.0266
[59] ner test1:2.7182, rc test1:6.5955, ner test2:2.4351, rc test2:1.7318, info:0.0134
[59] precision:0.8446, recall:0.8205, f1:0.8324
[60] ner train1:1.6578, rc_train1:6.1003, ner train2:0.5303, rc_train2:0.4790, info:0.0115
[60] ner test1:2.7445, rc test1:6.5487, ner test2:2.5644, rc test2:1.7430, info:0.0074
[60] precision:0.8139, recall:0.8379, f1:0.8257
[61] ner train1:1.6586, rc_train1:6.0881, ner train2:0.5311, rc_train2:0.4721, info:0.0299
[61] ner test1:2.7252, rc test1:6.4583, ner test2:2.5062, rc test2:1.7003, info:0.0104
[61] precision:0.8212, recall:0.8306, f1:0.8258
[62] ner train1:1.6502, rc_train1:6.0331, ner train2:0.5222, rc_train2:0.4646, info:0.0188
[62] ner test1:2.7537, rc test1:6.4138, ner test2:2.4851, rc test2:1.7302, info:0.0072
[62] precision:0.8304, recall:0.8359, f1:0.8331
[63] ner train1:1.6378, rc_train1:5.9941, ner train2:0.5047, rc_train2:0.4498, info:0.0107
[63] ner test1:2.7487, rc test1:6.3576, ner test2:2.5960, rc test2:1.7672, info:0.0135
[63] precision:0.8095, recall:0.8391, f1:0.8240
[64] ner train1:1.6261, rc_train1:5.9961, ner train2:0.5049, rc_train2:0.4495, info:0.0240
[64] ner test1:2.7749, rc test1:6.4124, ner test2:2.5729, rc test2:1.7837, info:0.0078
[64] precision:0.8302, recall:0.8211, f1:0.8256
[65] ner train1:1.6251, rc_train1:5.9389, ner train2:0.4935, rc_train2:0.4364, info:0.0279
[65] ner test1:2.7565, rc test1:6.2792, ner test2:2.5302, rc test2:1.7518, info:0.0180
[65] precision:0.8263, recall:0.8254, f1:0.8259
[66] ner train1:1.6121, rc_train1:5.9325, ner train2:0.4930, rc_train2:0.4482, info:0.0127
[66] ner test1:2.7408, rc test1:6.2807, ner test2:2.6087, rc test2:1.7812, info:0.0071
[66] precision:0.8212, recall:0.8386, f1:0.8298
[67] ner train1:1.6135, rc_train1:5.8888, ner train2:0.4769, rc_train2:0.4243, info:0.0136
[67] ner test1:2.7480, rc test1:6.4375, ner test2:2.6707, rc test2:1.8631, info:0.0066
[67] precision:0.8146, recall:0.8395, f1:0.8269
[68] ner train1:1.6022, rc_train1:5.8724, ner train2:0.4689, rc_train2:0.4230, info:0.0078
[68] ner test1:2.7512, rc test1:6.3983, ner test2:2.6363, rc test2:1.8718, info:0.0066
[68] precision:0.8257, recall:0.8363, f1:0.8309
[69] ner train1:1.5965, rc_train1:5.7671, ner train2:0.4654, rc_train2:0.4155, info:0.0111
[69] ner test1:2.7647, rc test1:6.2205, ner test2:2.6211, rc test2:1.8217, info:0.0066
[69] precision:0.8151, recall:0.8449, f1:0.8297
[70] ner train1:1.6008, rc_train1:5.8203, ner train2:0.4702, rc_train2:0.4226, info:0.0105
[70] ner test1:2.7597, rc test1:6.4043, ner test2:2.6905, rc test2:1.8058, info:0.0063
[70] precision:0.8350, recall:0.8402, f1:0.8376
[*] saved best model!
[71] ner train1:1.5989, rc_train1:5.7756, ner train2:0.4576, rc_train2:0.4178, info:0.0071
[71] ner test1:2.7985, rc test1:6.4118, ner test2:2.6748, rc test2:1.8537, info:0.0063
[71] precision:0.8203, recall:0.8499, f1:0.8349
[72] ner train1:1.5742, rc_train1:5.7830, ner train2:0.4503, rc_train2:0.4134, info:0.0068
[72] ner test1:2.7388, rc test1:6.4086, ner test2:2.6225, rc test2:1.8598, info:0.0064
[72] precision:0.8264, recall:0.8255, f1:0.8260
[73] ner train1:1.5791, rc_train1:5.8025, ner train2:0.4484, rc_train2:0.4096, info:0.0096
[73] ner test1:2.7258, rc test1:6.1997, ner test2:2.6389, rc test2:1.8126, info:0.0066
[73] precision:0.8320, recall:0.8340, f1:0.8330
[74] ner train1:1.5618, rc_train1:5.7170, ner train2:0.4396, rc_train2:0.3986, info:0.0160
[74] ner test1:2.7495, rc test1:6.2608, ner test2:2.7226, rc test2:1.8642, info:0.0095
[74] precision:0.8203, recall:0.8401, f1:0.8300
[75] ner train1:1.5538, rc_train1:5.6532, ner train2:0.4251, rc_train2:0.3869, info:0.0163
[75] ner test1:2.7175, rc test1:6.2189, ner test2:2.6539, rc test2:1.8705, info:0.0134
[75] precision:0.8269, recall:0.8314, f1:0.8291
[76] ner train1:1.5508, rc_train1:5.6680, ner train2:0.4255, rc_train2:0.3883, info:0.0457
[76] ner test1:2.7647, rc test1:6.0835, ner test2:2.7129, rc test2:1.8495, info:0.0154
[76] precision:0.8159, recall:0.8439, f1:0.8297
[77] ner train1:1.5590, rc_train1:5.6864, ner train2:0.4335, rc_train2:0.3959, info:0.0096
[77] ner test1:2.7880, rc test1:6.1608, ner test2:2.6984, rc test2:1.8593, info:0.0160
[77] precision:0.8242, recall:0.8472, f1:0.8355
[78] ner train1:1.5494, rc_train1:5.6614, ner train2:0.4203, rc_train2:0.3796, info:0.0120
[78] ner test1:2.7500, rc test1:6.2267, ner test2:2.7855, rc test2:1.8720, info:0.0087
[78] precision:0.8236, recall:0.8358, f1:0.8296
[79] ner train1:1.5391, rc_train1:5.6567, ner train2:0.4102, rc_train2:0.3829, info:0.0162
[79] ner test1:2.7642, rc test1:6.0989, ner test2:2.7283, rc test2:1.9691, info:0.0080
[79] precision:0.8209, recall:0.8190, f1:0.8199
[80] ner train1:1.5258, rc_train1:5.5878, ner train2:0.4047, rc_train2:0.3766, info:0.0396
[80] ner test1:2.7659, rc test1:6.2538, ner test2:2.7329, rc test2:1.9123, info:0.0074
[80] precision:0.8294, recall:0.8382, f1:0.8338
[81] ner train1:1.5256, rc_train1:5.6484, ner train2:0.4090, rc_train2:0.3764, info:0.0107
[81] ner test1:2.7492, rc test1:6.1517, ner test2:2.7590, rc test2:1.9070, info:0.0084
[81] precision:0.8365, recall:0.8313, f1:0.8339
[82] ner train1:1.5201, rc_train1:5.6230, ner train2:0.3974, rc_train2:0.3719, info:0.0216
[82] ner test1:2.8129, rc test1:6.1259, ner test2:2.7475, rc test2:1.9190, info:0.0065
[82] precision:0.8185, recall:0.8302, f1:0.8243
[83] ner train1:1.5129, rc_train1:5.6290, ner train2:0.3972, rc_train2:0.3667, info:0.0216
[83] ner test1:2.7655, rc test1:6.2409, ner test2:2.7165, rc test2:1.9217, info:0.0062
[83] precision:0.8216, recall:0.8334, f1:0.8274
[84] ner train1:1.5162, rc_train1:5.5550, ner train2:0.3944, rc_train2:0.3683, info:0.0080
[84] ner test1:2.7576, rc test1:6.0312, ner test2:2.7360, rc test2:2.0003, info:0.0063
[84] precision:0.8236, recall:0.8324, f1:0.8280
[85] ner train1:1.5086, rc_train1:5.5213, ner train2:0.3838, rc_train2:0.3605, info:0.0244
[85] ner test1:2.7627, rc test1:5.9962, ner test2:2.7547, rc test2:1.9735, info:0.0080
[85] precision:0.8270, recall:0.8281, f1:0.8276
[86] ner train1:1.5116, rc_train1:5.5154, ner train2:0.3854, rc_train2:0.3599, info:0.0111
[86] ner test1:2.7885, rc test1:5.7619, ner test2:2.7701, rc test2:1.9316, info:0.1023
[86] precision:0.8289, recall:0.8263, f1:0.8276
[87] ner train1:1.4997, rc_train1:5.5230, ner train2:0.3840, rc_train2:0.3632, info:0.0121
[87] ner test1:2.7702, rc test1:5.9528, ner test2:2.7665, rc test2:1.9658, info:0.0482
[87] precision:0.8246, recall:0.8359, f1:0.8302
[88] ner train1:1.5022, rc_train1:5.4848, ner train2:0.3782, rc_train2:0.3601, info:0.0154
[88] ner test1:2.7856, rc test1:5.9472, ner test2:2.7655, rc test2:1.8937, info:0.0146
[88] precision:0.8161, recall:0.8349, f1:0.8254
[89] ner train1:1.4989, rc_train1:5.5228, ner train2:0.3840, rc_train2:0.3553, info:0.0151
[89] ner test1:2.7576, rc test1:6.0206, ner test2:2.7965, rc test2:1.9723, info:0.0131
[89] precision:0.8448, recall:0.8395, f1:0.8421
[*] saved best model!
[90] ner train1:1.4819, rc_train1:5.4682, ner train2:0.3694, rc_train2:0.3452, info:0.0142
[90] ner test1:2.7908, rc test1:6.0809, ner test2:2.8144, rc test2:1.9755, info:0.0080
[90] precision:0.8373, recall:0.8369, f1:0.8371
[91] ner train1:1.4910, rc_train1:5.4464, ner train2:0.3774, rc_train2:0.3597, info:0.0279
[91] ner test1:2.7633, rc test1:6.0410, ner test2:2.7869, rc test2:1.9165, info:0.0063
[91] precision:0.8292, recall:0.8333, f1:0.8312
[92] ner train1:1.4854, rc_train1:5.4672, ner train2:0.3671, rc_train2:0.3572, info:0.0068
[92] ner test1:2.8303, rc test1:6.0892, ner test2:2.9097, rc test2:1.9977, info:0.0063
[92] precision:0.8238, recall:0.8411, f1:0.8323
[93] ner train1:1.4719, rc_train1:5.4022, ner train2:0.3605, rc_train2:0.3389, info:0.0158
[93] ner test1:2.7770, rc test1:5.9655, ner test2:2.7642, rc test2:1.9418, info:0.0065
[93] precision:0.8247, recall:0.8346, f1:0.8296
[94] ner train1:1.4751, rc_train1:5.4348, ner train2:0.3524, rc_train2:0.3409, info:0.0142
[94] ner test1:2.7846, rc test1:5.8691, ner test2:2.9306, rc test2:2.0621, info:0.0091
[94] precision:0.8211, recall:0.8417, f1:0.8313
[95] ner train1:1.4706, rc_train1:5.3159, ner train2:0.3591, rc_train2:0.3427, info:0.0096
[95] ner test1:2.8529, rc test1:5.9117, ner test2:2.9633, rc test2:2.0416, info:0.0065
[95] precision:0.8204, recall:0.8506, f1:0.8352
[96] ner train1:1.4681, rc_train1:5.3611, ner train2:0.3502, rc_train2:0.3285, info:0.0118
[96] ner test1:2.8069, rc test1:5.7249, ner test2:2.8573, rc test2:1.9851, info:0.0113
[96] precision:0.8335, recall:0.8332, f1:0.8333
[97] ner train1:1.4630, rc_train1:5.2880, ner train2:0.3503, rc_train2:0.3341, info:0.4230
[97] ner test1:2.7903, rc test1:5.8071, ner test2:2.8156, rc test2:2.0534, info:0.0334
[97] precision:0.8321, recall:0.8182, f1:0.8251
[98] ner train1:1.4633, rc_train1:5.3090, ner train2:0.3433, rc_train2:0.3279, info:0.0144
[98] ner test1:2.8067, rc test1:5.8750, ner test2:2.9838, rc test2:2.0588, info:0.0067
[98] precision:0.8291, recall:0.8425, f1:0.8358
[99] ner train1:1.4600, rc_train1:5.3082, ner train2:0.3471, rc_train2:0.3415, info:0.0115
[99] ner test1:2.7819, rc test1:5.8777, ner test2:2.8859, rc test2:2.0031, info:0.0077
[99] precision:0.8164, recall:0.8371, f1:0.8266
[100] ner train1:1.4544, rc_train1:5.3414, ner train2:0.3421, rc_train2:0.3249, info:0.0091
[100] ner test1:2.8199, rc test1:5.9577, ner test2:3.0000, rc test2:2.0822, info:0.0074
[100] precision:0.8273, recall:0.8467, f1:0.8369