random2_n4_exact

[1] ner train1:7.0546, rc_train1:1593.8813, ner train2:5.2131, rc_train2:344.4852, info:1.6755
[1] ner test1:3.9580, rc test1:10.4616, ner test2:3.1916, rc test2:3.2151, info:0.1825
[1] precision:0.6130, recall:0.5884, f1:0.6004
[*] saved best model!
[2] ner train1:3.7039, rc_train1:11.4290, ner train2:2.9832, rc_train2:2.9360, info:0.1762
[2] ner test1:3.4942, rc test1:11.8040, ner test2:2.6630, rc test2:2.4853, info:0.1554
[2] precision:0.7316, recall:0.6129, f1:0.6670
[*] saved best model!
[3] ner train1:3.3649, rc_train1:11.1512, ner train2:2.5914, rc_train2:2.4330, info:0.1687
[3] ner test1:3.2918, rc test1:11.9371, ner test2:2.4194, rc test2:2.2027, info:0.2378
[3] precision:0.7422, recall:0.6158, f1:0.6731
[*] saved best model!
[4] ner train1:3.1917, rc_train1:10.3300, ner train2:2.3740, rc_train2:2.2874, info:9.5659
[4] ner test1:3.2095, rc test1:10.3037, ner test2:2.3359, rc test2:2.0834, info:8.1356
[4] precision:0.7425, recall:0.6822, f1:0.7111
[*] saved best model!
[5] ner train1:3.1812, rc_train1:10.1186, ner train2:2.3244, rc_train2:2.1746, info:4.7181
[5] ner test1:3.2672, rc test1:10.4434, ner test2:2.3846, rc test2:2.1247, info:0.1777
[5] precision:0.7637, recall:0.6388, f1:0.6957
[6] ner train1:3.1178, rc_train1:9.6038, ner train2:2.2812, rc_train2:2.0998, info:0.2492
[6] ner test1:3.1596, rc test1:9.5740, ner test2:2.3122, rc test2:2.0312, info:0.1815
[6] precision:0.7455, recall:0.6979, f1:0.7209
[*] saved best model!
[7] ner train1:2.9951, rc_train1:9.1499, ner train2:2.1305, rc_train2:1.9227, info:0.1770
[7] ner test1:3.0438, rc test1:9.5281, ner test2:2.2151, rc test2:1.8930, info:0.1617
[7] precision:0.7718, recall:0.7031, f1:0.7358
[*] saved best model!
[8] ner train1:2.8981, rc_train1:8.7308, ner train2:1.9833, rc_train2:1.7772, info:0.3011
[8] ner test1:3.0201, rc test1:8.8562, ner test2:2.1542, rc test2:1.8103, info:0.1275
[8] precision:0.7505, recall:0.7100, f1:0.7297
[9] ner train1:2.8059, rc_train1:8.4709, ner train2:1.8634, rc_train2:1.6565, info:0.5449
[9] ner test1:3.0295, rc test1:8.6736, ner test2:2.1393, rc test2:1.7711, info:0.3536
[9] precision:0.7783, recall:0.7360, f1:0.7566
[*] saved best model!
[10] ner train1:2.7152, rc_train1:8.4210, ner train2:1.7809, rc_train2:1.5694, info:0.2491
[10] ner test1:3.0228, rc test1:8.7720, ner test2:2.1004, rc test2:1.7378, info:0.1223
[10] precision:0.7662, recall:0.7335, f1:0.7495
[11] ner train1:2.6607, rc_train1:8.3785, ner train2:1.7017, rc_train2:1.5053, info:0.8736
[11] ner test1:2.9622, rc test1:8.3905, ner test2:2.1132, rc test2:1.7520, info:0.1923
[11] precision:0.7503, recall:0.7640, f1:0.7571
[*] saved best model!
[12] ner train1:2.6064, rc_train1:8.2435, ner train2:1.6560, rc_train2:1.4535, info:0.2235
[12] ner test1:2.8821, rc test1:8.0875, ner test2:2.0936, rc test2:1.7295, info:0.1206
[12] precision:0.7689, recall:0.7512, f1:0.7599
[*] saved best model!
[13] ner train1:2.5634, rc_train1:8.2428, ner train2:1.6007, rc_train2:1.3964, info:6.2488
[13] ner test1:2.9699, rc test1:8.8010, ner test2:2.0729, rc test2:1.6871, info:0.1536
[13] precision:0.7587, recall:0.7343, f1:0.7463
[14] ner train1:2.5490, rc_train1:8.2793, ner train2:1.5448, rc_train2:1.3578, info:13.8306
[14] ner test1:2.9774, rc test1:9.4626, ner test2:2.0841, rc test2:1.6703, info:0.2392
[14] precision:0.7805, recall:0.7539, f1:0.7670
[*] saved best model!
[15] ner train1:2.5355, rc_train1:8.2561, ner train2:1.5537, rc_train2:1.3865, info:0.2430
[15] ner test1:2.9181, rc test1:8.5892, ner test2:2.0746, rc test2:1.7531, info:0.4450
[15] precision:0.8780, recall:0.7011, f1:0.7797
[*] saved best model!
[16] ner train1:2.4871, rc_train1:8.4398, ner train2:1.4616, rc_train2:1.2887, info:6.7747
[16] ner test1:2.8409, rc test1:8.7200, ner test2:2.0588, rc test2:1.6232, info:2.6557
[16] precision:0.8185, recall:0.7460, f1:0.7805
[*] saved best model!
[17] ner train1:2.5137, rc_train1:8.2910, ner train2:1.5032, rc_train2:1.3336, info:1.7393
[17] ner test1:3.0007, rc test1:8.7070, ner test2:2.1574, rc test2:1.7870, info:0.4078
[17] precision:0.7452, recall:0.7771, f1:0.7608
[18] ner train1:2.5078, rc_train1:8.3255, ner train2:1.5003, rc_train2:1.3319, info:0.9035
[18] ner test1:2.9782, rc test1:8.7927, ner test2:2.0396, rc test2:1.6549, info:1.0273
[18] precision:0.7826, recall:0.7688, f1:0.7756
[19] ner train1:2.4732, rc_train1:8.2892, ner train2:1.4275, rc_train2:1.2527, info:0.4444
[19] ner test1:2.9088, rc test1:8.7669, ner test2:2.1365, rc test2:1.6800, info:0.2957
[19] precision:0.7690, recall:0.7874, f1:0.7781
[20] ner train1:2.4327, rc_train1:8.1519, ner train2:1.3674, rc_train2:1.1930, info:0.5432
[20] ner test1:2.8770, rc test1:8.2748, ner test2:2.1026, rc test2:1.5605, info:0.4915
[20] precision:0.7921, recall:0.7873, f1:0.7897
[*] saved best model!
[21] ner train1:2.3529, rc_train1:8.1839, ner train2:1.3157, rc_train2:1.1372, info:1.8526
[21] ner test1:2.8052, rc test1:8.4922, ner test2:2.0698, rc test2:1.5733, info:0.1476
[21] precision:0.7991, recall:0.7704, f1:0.7845
[22] ner train1:2.3830, rc_train1:8.1420, ner train2:1.3014, rc_train2:1.1365, info:0.6037
[22] ner test1:2.9165, rc test1:8.7536, ner test2:2.0550, rc test2:1.6176, info:0.1429
[22] precision:0.7998, recall:0.7651, f1:0.7821
[23] ner train1:2.3389, rc_train1:8.2504, ner train2:1.2714, rc_train2:1.1128, info:23.4996
[23] ner test1:2.8547, rc test1:8.7279, ner test2:2.0907, rc test2:1.5509, info:1.2655
[23] precision:0.8196, recall:0.7667, f1:0.7923
[*] saved best model!
[24] ner train1:2.3071, rc_train1:8.3244, ner train2:1.2260, rc_train2:1.0688, info:1.9353
[24] ner test1:2.8886, rc test1:8.2867, ner test2:2.0993, rc test2:1.5774, info:0.4552
[24] precision:0.8237, recall:0.7874, f1:0.8051
[*] saved best model!
[25] ner train1:2.3180, rc_train1:8.1677, ner train2:1.2231, rc_train2:1.0774, info:1.0022
[25] ner test1:2.8832, rc test1:8.5114, ner test2:2.0699, rc test2:1.5434, info:1.4695
[25] precision:0.8130, recall:0.7822, f1:0.7973
[26] ner train1:2.3481, rc_train1:8.2694, ner train2:1.2584, rc_train2:1.1028, info:2.4672
[26] ner test1:2.9230, rc test1:7.9458, ner test2:2.1598, rc test2:1.6611, info:0.3919
[26] precision:0.7870, recall:0.8042, f1:0.7955
[27] ner train1:2.3653, rc_train1:8.2202, ner train2:1.2619, rc_train2:1.1110, info:9.1899
[27] ner test1:2.8568, rc test1:8.2534, ner test2:2.0804, rc test2:1.5610, info:1.7152
[27] precision:0.8290, recall:0.7688, f1:0.7978
[28] ner train1:2.3739, rc_train1:8.1824, ner train2:1.2448, rc_train2:1.1172, info:7.6449
[28] ner test1:2.9525, rc test1:8.8679, ner test2:2.1140, rc test2:1.6836, info:2.4156
[28] precision:0.8389, recall:0.7309, f1:0.7812
[29] ner train1:2.4087, rc_train1:8.3632, ner train2:1.2747, rc_train2:1.1298, info:1.4108
[29] ner test1:2.9308, rc test1:9.0354, ner test2:2.1701, rc test2:1.6965, info:0.2217
[29] precision:0.7921, recall:0.7921, f1:0.7921
[30] ner train1:2.3414, rc_train1:8.2816, ner train2:1.2141, rc_train2:1.0622, info:0.4143
[30] ner test1:2.9575, rc test1:7.9823, ner test2:2.0680, rc test2:1.6359, info:0.2178
[30] precision:0.7842, recall:0.7852, f1:0.7847
[31] ner train1:2.2205, rc_train1:8.2424, ner train2:1.1321, rc_train2:0.9849, info:1.2285
[31] ner test1:2.8902, rc test1:7.8787, ner test2:2.1256, rc test2:1.5731, info:0.3716
[31] precision:0.8015, recall:0.7901, f1:0.7958
[32] ner train1:2.2497, rc_train1:8.1252, ner train2:1.1393, rc_train2:0.9921, info:1.1084
[32] ner test1:2.9616, rc test1:8.2606, ner test2:2.1121, rc test2:1.5927, info:3.1505
[32] precision:0.8006, recall:0.8173, f1:0.8088
[*] saved best model!
[33] ner train1:2.2296, rc_train1:8.1306, ner train2:1.0735, rc_train2:0.9269, info:2.4256
[33] ner test1:2.9170, rc test1:8.1863, ner test2:2.0957, rc test2:1.5526, info:0.9481
[33] precision:0.8140, recall:0.7926, f1:0.8031
[34] ner train1:2.2596, rc_train1:8.1449, ner train2:1.1012, rc_train2:0.9707, info:2.8133
[34] ner test1:2.9340, rc test1:7.9139, ner test2:2.1014, rc test2:1.5515, info:1.0576
[34] precision:0.8016, recall:0.8059, f1:0.8037
[35] ner train1:2.2100, rc_train1:8.1832, ner train2:1.1130, rc_train2:0.9749, info:3.3860
[35] ner test1:2.8694, rc test1:8.1432, ner test2:2.1229, rc test2:1.6055, info:1.2956
[35] precision:0.8108, recall:0.7953, f1:0.8030
[36] ner train1:2.2208, rc_train1:8.2275, ner train2:1.1307, rc_train2:0.9975, info:2.0236
[36] ner test1:3.0056, rc test1:8.7371, ner test2:2.1554, rc test2:1.6610, info:0.7163
[36] precision:0.7818, recall:0.7951, f1:0.7884
[37] ner train1:2.2716, rc_train1:8.1664, ner train2:1.1392, rc_train2:1.0108, info:1.7854
[37] ner test1:2.8265, rc test1:7.9580, ner test2:2.1579, rc test2:1.6091, info:0.5593
[37] precision:0.8082, recall:0.7746, f1:0.7910
[38] ner train1:2.2400, rc_train1:8.2475, ner train2:1.1406, rc_train2:1.0081, info:4.3931
[38] ner test1:2.8821, rc test1:8.6322, ner test2:2.2127, rc test2:1.6460, info:0.5895
[38] precision:0.8106, recall:0.7787, f1:0.7943
[39] ner train1:2.3294, rc_train1:8.4020, ner train2:1.2083, rc_train2:1.0805, info:17.9179
[39] ner test1:3.0522, rc test1:8.6202, ner test2:2.3769, rc test2:1.7067, info:0.6977
[39] precision:0.7680, recall:0.8367, f1:0.8009
[40] ner train1:2.2179, rc_train1:8.5193, ner train2:1.1449, rc_train2:1.0031, info:20.5565
[40] ner test1:2.8757, rc test1:8.2824, ner test2:2.1079, rc test2:1.5870, info:0.5225
[40] precision:0.8182, recall:0.7864, f1:0.8020
[41] ner train1:2.2356, rc_train1:8.2724, ner train2:1.1051, rc_train2:0.9680, info:3.0281
[41] ner test1:2.9020, rc test1:8.2420, ner test2:2.1105, rc test2:1.5564, info:0.6152
[41] precision:0.8029, recall:0.7896, f1:0.7962
[42] ner train1:2.1816, rc_train1:8.2468, ner train2:1.0772, rc_train2:0.9348, info:1.3754
[42] ner test1:2.9140, rc test1:7.7297, ner test2:2.1120, rc test2:1.5901, info:0.3905
[42] precision:0.7928, recall:0.8073, f1:0.8000
[43] ner train1:2.2031, rc_train1:8.1713, ner train2:1.0515, rc_train2:0.9177, info:1.7785
[43] ner test1:2.8738, rc test1:8.5763, ner test2:2.1554, rc test2:1.5589, info:0.5411
[43] precision:0.8044, recall:0.7831, f1:0.7936
[44] ner train1:2.2142, rc_train1:8.1012, ner train2:1.0549, rc_train2:0.9209, info:10.9919
[44] ner test1:2.8705, rc test1:7.8650, ner test2:2.1412, rc test2:1.6128, info:0.3569
[44] precision:0.7917, recall:0.8046, f1:0.7981
[45] ner train1:2.2106, rc_train1:8.2038, ner train2:1.0524, rc_train2:0.9153, info:0.7424
[45] ner test1:2.8759, rc test1:8.2439, ner test2:2.1597, rc test2:1.6268, info:0.6820
[45] precision:0.7835, recall:0.8173, f1:0.8000
[46] ner train1:2.1551, rc_train1:8.0004, ner train2:1.0346, rc_train2:0.9123, info:2.7231
[46] ner test1:2.8749, rc test1:8.2179, ner test2:2.1400, rc test2:1.5772, info:0.5379
[46] precision:0.8171, recall:0.8017, f1:0.8094
[*] saved best model!
[47] ner train1:2.1847, rc_train1:8.0770, ner train2:1.0318, rc_train2:0.8976, info:1.1082
[47] ner test1:3.0537, rc test1:8.0594, ner test2:2.1933, rc test2:1.5900, info:0.9271
[47] precision:0.8151, recall:0.8015, f1:0.8083
[48] ner train1:2.1466, rc_train1:8.0419, ner train2:0.9812, rc_train2:0.8534, info:4.1780
[48] ner test1:2.9347, rc test1:7.8297, ner test2:2.1293, rc test2:1.5592, info:0.6845
[48] precision:0.7971, recall:0.7933, f1:0.7952
[49] ner train1:2.1135, rc_train1:8.1853, ner train2:0.9917, rc_train2:0.8776, info:28.0771
[49] ner test1:2.8933, rc test1:8.3799, ner test2:2.1232, rc test2:1.6526, info:3.8456
[49] precision:0.8256, recall:0.7651, f1:0.7942
[50] ner train1:2.1555, rc_train1:7.9793, ner train2:0.9757, rc_train2:0.8519, info:2.6923
[50] ner test1:2.8523, rc test1:7.7181, ner test2:2.1527, rc test2:1.6483, info:1.7228
[50] precision:0.8200, recall:0.7857, f1:0.8025
[51] ner train1:2.1514, rc_train1:7.9403, ner train2:1.0271, rc_train2:0.9106, info:3.0923
[51] ner test1:2.8883, rc test1:8.2728, ner test2:2.2126, rc test2:1.6770, info:0.4028
[51] precision:0.8012, recall:0.7933, f1:0.7973
[52] ner train1:2.2003, rc_train1:8.1493, ner train2:1.0409, rc_train2:0.9067, info:1.3766
[52] ner test1:2.9761, rc test1:7.7907, ner test2:2.3095, rc test2:1.6114, info:0.8797
[52] precision:0.8109, recall:0.8219, f1:0.8164
[*] saved best model!
[53] ner train1:2.1355, rc_train1:7.9464, ner train2:0.9605, rc_train2:0.8264, info:13.8707
[53] ner test1:2.9705, rc test1:8.2907, ner test2:2.1970, rc test2:1.5823, info:1.1417
[53] precision:0.8049, recall:0.7978, f1:0.8013
[54] ner train1:2.1912, rc_train1:7.8361, ner train2:0.9722, rc_train2:0.8387, info:16.5236
[54] ner test1:3.0515, rc test1:8.1880, ner test2:2.1769, rc test2:1.6175, info:1.4300
[54] precision:0.8071, recall:0.7991, f1:0.8031
[55] ner train1:2.2352, rc_train1:8.0984, ner train2:1.0155, rc_train2:0.9046, info:4.6754
[55] ner test1:3.2946, rc test1:8.1076, ner test2:2.3390, rc test2:1.6509, info:3.1044
[55] precision:0.7768, recall:0.8100, f1:0.7930
[56] ner train1:2.2532, rc_train1:7.9559, ner train2:0.9758, rc_train2:0.8579, info:3.3173
[56] ner test1:3.0189, rc test1:8.0087, ner test2:2.2114, rc test2:1.6101, info:1.3790
[56] precision:0.8033, recall:0.7972, f1:0.8002
[57] ner train1:2.1206, rc_train1:7.9927, ner train2:0.9775, rc_train2:0.8428, info:2.7551
[57] ner test1:2.8712, rc test1:7.0906, ner test2:2.4093, rc test2:1.7378, info:1.3801
[57] precision:0.8026, recall:0.8069, f1:0.8047
[58] ner train1:2.2378, rc_train1:8.1067, ner train2:1.0128, rc_train2:0.9030, info:9.6657
[58] ner test1:3.0888, rc test1:8.4734, ner test2:2.3287, rc test2:1.6849, info:0.9400
[58] precision:0.8225, recall:0.7647, f1:0.7926
[59] ner train1:2.3357, rc_train1:8.0373, ner train2:1.0396, rc_train2:0.9219, info:3.6266
[59] ner test1:3.0183, rc test1:7.5561, ner test2:2.2255, rc test2:1.6516, info:0.7968
[59] precision:0.7890, recall:0.7782, f1:0.7835
[60] ner train1:2.1678, rc_train1:8.1408, ner train2:1.0052, rc_train2:0.8999, info:6.5080
[60] ner test1:2.9803, rc test1:7.9704, ner test2:2.2739, rc test2:1.8720, info:1.5274
[60] precision:0.7372, recall:0.8238, f1:0.7781
[61] ner train1:2.1104, rc_train1:8.0740, ner train2:1.0244, rc_train2:0.9311, info:1.4535
[61] ner test1:2.8513, rc test1:7.9482, ner test2:2.2596, rc test2:1.7380, info:0.6094
[61] precision:0.8212, recall:0.7785, f1:0.7993
[62] ner train1:2.1259, rc_train1:7.8820, ner train2:1.0072, rc_train2:0.8990, info:2.6293
[62] ner test1:3.1432, rc test1:8.6275, ner test2:2.3733, rc test2:1.7517, info:1.0906
[62] precision:0.7969, recall:0.8022, f1:0.7996
[63] ner train1:2.1653, rc_train1:8.0478, ner train2:0.9851, rc_train2:0.8684, info:1.9012
[63] ner test1:3.0220, rc test1:7.7472, ner test2:2.2171, rc test2:1.6107, info:0.9194
[63] precision:0.7980, recall:0.7873, f1:0.7926
[64] ner train1:2.2223, rc_train1:8.0688, ner train2:0.9561, rc_train2:0.8445, info:2.0650
[64] ner test1:3.0035, rc test1:8.4760, ner test2:2.1802, rc test2:1.6885, info:1.2916
[64] precision:0.8098, recall:0.7962, f1:0.8029
[65] ner train1:2.2292, rc_train1:8.0888, ner train2:0.9956, rc_train2:0.8752, info:1.6646
[65] ner test1:3.1946, rc test1:8.7746, ner test2:2.3830, rc test2:1.7585, info:3.9062
[65] precision:0.7866, recall:0.7757, f1:0.7811
[66] ner train1:2.1955, rc_train1:8.1112, ner train2:1.0075, rc_train2:0.8956, info:14.5433
[66] ner test1:2.8924, rc test1:7.9941, ner test2:2.1894, rc test2:1.6298, info:0.5499
[66] precision:0.8226, recall:0.7879, f1:0.8049
[67] ner train1:2.1989, rc_train1:7.9144, ner train2:0.9628, rc_train2:0.8566, info:4.1872
[67] ner test1:3.1482, rc test1:8.2387, ner test2:2.3353, rc test2:1.8242, info:1.3549
[67] precision:0.7717, recall:0.8222, f1:0.7961
[68] ner train1:2.2377, rc_train1:8.1183, ner train2:0.9466, rc_train2:0.8496, info:3.4917
[68] ner test1:3.0540, rc test1:8.4881, ner test2:2.3689, rc test2:1.8492, info:6.2087
[68] precision:0.7883, recall:0.8095, f1:0.7988
[69] ner train1:2.1846, rc_train1:7.9205, ner train2:0.9359, rc_train2:0.8348, info:2.3780
[69] ner test1:2.8953, rc test1:8.0457, ner test2:2.2425, rc test2:1.7285, info:1.0862
[69] precision:0.7927, recall:0.8032, f1:0.7979
[70] ner train1:2.1571, rc_train1:8.0328, ner train2:0.9359, rc_train2:0.8314, info:1.6452
[70] ner test1:3.0806, rc test1:8.4333, ner test2:2.3291, rc test2:1.6672, info:1.6724
[70] precision:0.7954, recall:0.8079, f1:0.8016
[71] ner train1:2.2370, rc_train1:7.9430, ner train2:0.8943, rc_train2:0.7878, info:11.6354
[71] ner test1:2.8986, rc test1:8.2831, ner test2:2.3382, rc test2:1.7380, info:1.1663
[71] precision:0.8111, recall:0.7679, f1:0.7890
[72] ner train1:2.2007, rc_train1:7.8727, ner train2:0.9609, rc_train2:0.8621, info:6.9853
[72] ner test1:3.1186, rc test1:8.4926, ner test2:2.3301, rc test2:1.7011, info:2.1504
[72] precision:0.8190, recall:0.7858, f1:0.8021
[73] ner train1:2.2358, rc_train1:8.3617, ner train2:0.9953, rc_train2:0.9047, info:6.7463
[73] ner test1:3.0792, rc test1:8.4063, ner test2:2.2496, rc test2:1.6575, info:1.7170
[73] precision:0.8176, recall:0.7910, f1:0.8041
[74] ner train1:2.1886, rc_train1:8.2770, ner train2:0.9679, rc_train2:0.8763, info:2.4591
[74] ner test1:3.0576, rc test1:7.7161, ner test2:2.3305, rc test2:1.7159, info:1.2274
[74] precision:0.7823, recall:0.8025, f1:0.7923
[75] ner train1:2.3154, rc_train1:8.2322, ner train2:1.0004, rc_train2:0.9321, info:1.9215
[75] ner test1:3.3095, rc test1:8.5938, ner test2:2.3004, rc test2:1.7810, info:2.2626
[75] precision:0.8205, recall:0.7639, f1:0.7912
[76] ner train1:2.3643, rc_train1:8.0054, ner train2:0.9732, rc_train2:0.8857, info:3.2513
[76] ner test1:3.0900, rc test1:7.7105, ner test2:2.4491, rc test2:1.7662, info:2.8008
[76] precision:0.8027, recall:0.8062, f1:0.8044
[77] ner train1:2.3742, rc_train1:8.0472, ner train2:0.9637, rc_train2:0.8856, info:3.4564
[77] ner test1:3.1919, rc test1:8.5158, ner test2:2.2896, rc test2:1.7705, info:1.5023
[77] precision:0.8279, recall:0.7801, f1:0.8033
[78] ner train1:2.4349, rc_train1:7.9469, ner train2:0.9883, rc_train2:0.8955, info:2.6709
[78] ner test1:3.5619, rc test1:8.3226, ner test2:2.2901, rc test2:1.7021, info:2.6359
[78] precision:0.8122, recall:0.7931, f1:0.8025
[79] ner train1:2.6404, rc_train1:7.9888, ner train2:0.9699, rc_train2:0.8908, info:2.8427
[79] ner test1:3.4528, rc test1:8.8810, ner test2:2.2939, rc test2:1.6946, info:3.7869
[79] precision:0.8033, recall:0.7954, f1:0.7993
[80] ner train1:3.9615, rc_train1:8.2621, ner train2:0.9814, rc_train2:0.8865, info:4.7804
[80] ner test1:6.6594, rc test1:8.7262, ner test2:2.2564, rc test2:1.6950, info:4.0957
[80] precision:0.8087, recall:0.7882, f1:0.7983
[81] ner train1:9.5819, rc_train1:8.2057, ner train2:0.8933, rc_train2:0.7897, info:7.3162
[81] ner test1:4.9265, rc test1:8.5289, ner test2:2.2818, rc test2:1.7522, info:3.3866
[81] precision:0.7975, recall:0.7835, f1:0.7904
[82] ner train1:3.2674, rc_train1:8.1330, ner train2:0.9487, rc_train2:0.8452, info:2.8498
[82] ner test1:3.8148, rc test1:8.2392, ner test2:2.5227, rc test2:1.9232, info:2.8270
[82] precision:0.7926, recall:0.7699, f1:0.7811
[83] ner train1:18.5618, rc_train1:8.0512, ner train2:0.9894, rc_train2:0.8745, info:5.8912
[83] ner test1:13.0124, rc test1:8.2539, ner test2:2.2915, rc test2:1.6952, info:2.2898
[83] precision:0.8066, recall:0.7926, f1:0.7996
[84] ner train1:3.6037, rc_train1:8.3545, ner train2:1.0530, rc_train2:0.9661, info:6.5979
[84] ner test1:3.3513, rc test1:9.1483, ner test2:2.6065, rc test2:2.1563, info:1.2796
[84] precision:0.8046, recall:0.7097, f1:0.7542
[85] ner train1:3.9224, rc_train1:8.2418, ner train2:1.0015, rc_train2:0.8922, info:3.0165
[85] ner test1:4.6604, rc test1:8.4258, ner test2:2.3128, rc test2:1.7637, info:2.2634
[85] precision:0.7997, recall:0.7946, f1:0.7971
[86] ner train1:2.8296, rc_train1:8.1609, ner train2:1.0912, rc_train2:0.9866, info:4.0186
[86] ner test1:3.0946, rc test1:8.5141, ner test2:2.3781, rc test2:1.8233, info:1.5183
[86] precision:0.7887, recall:0.7845, f1:0.7866
[87] ner train1:2.4380, rc_train1:8.2185, ner train2:1.0768, rc_train2:0.9894, info:2.7455
[87] ner test1:3.2766, rc test1:8.5552, ner test2:2.3164, rc test2:1.6995, info:1.9959
[87] precision:0.7935, recall:0.7951, f1:0.7943
[88] ner train1:2.3839, rc_train1:8.6332, ner train2:1.0221, rc_train2:0.9302, info:2.6814
[88] ner test1:3.3304, rc test1:8.6543, ner test2:2.4599, rc test2:1.7900, info:1.6025
[88] precision:0.8031, recall:0.7990, f1:0.8010
[89] ner train1:2.3041, rc_train1:8.0879, ner train2:0.9951, rc_train2:0.8866, info:3.5363
[89] ner test1:2.9065, rc test1:7.8262, ner test2:2.4400, rc test2:1.7454, info:0.7683
[89] precision:0.7878, recall:0.7724, f1:0.7800
[90] ner train1:2.1922, rc_train1:7.9875, ner train2:0.9504, rc_train2:0.8330, info:0.7195
[90] ner test1:3.2007, rc test1:8.0925, ner test2:2.3375, rc test2:1.6859, info:1.2508
[90] precision:0.8220, recall:0.7917, f1:0.8066
[91] ner train1:2.1733, rc_train1:7.9020, ner train2:0.9001, rc_train2:0.7922, info:2.9613
[91] ner test1:2.9430, rc test1:8.0357, ner test2:2.4048, rc test2:1.7864, info:0.5592
[91] precision:0.8238, recall:0.8000, f1:0.8117
[92] ner train1:2.1316, rc_train1:7.6433, ner train2:0.8647, rc_train2:0.7627, info:2.1455
[92] ner test1:3.0847, rc test1:7.9197, ner test2:2.3432, rc test2:1.6491, info:1.6879
[92] precision:0.8124, recall:0.8004, f1:0.8063
[93] ner train1:2.1368, rc_train1:7.8763, ner train2:0.8507, rc_train2:0.7507, info:7.3204
[93] ner test1:3.0516, rc test1:8.0506, ner test2:2.3224, rc test2:1.6864, info:4.2193
[93] precision:0.8140, recall:0.8138, f1:0.8139
[94] ner train1:2.0939, rc_train1:7.8260, ner train2:0.8491, rc_train2:0.7553, info:2.2426
[94] ner test1:3.2356, rc test1:7.7397, ner test2:2.4778, rc test2:1.8627, info:0.5953
[94] precision:0.7858, recall:0.8363, f1:0.8102
[95] ner train1:2.1644, rc_train1:7.9244, ner train2:0.8656, rc_train2:0.7731, info:1.2912
[95] ner test1:3.0121, rc test1:8.1526, ner test2:2.4431, rc test2:1.7512, info:0.7900
[95] precision:0.8220, recall:0.8142, f1:0.8181
[*] saved best model!
[96] ner train1:2.1151, rc_train1:7.9023, ner train2:0.8483, rc_train2:0.7545, info:4.3966
[96] ner test1:3.1040, rc test1:7.5151, ner test2:2.3355, rc test2:1.7690, info:0.9001
[96] precision:0.7985, recall:0.7977, f1:0.7981
[97] ner train1:2.1241, rc_train1:7.8761, ner train2:0.8741, rc_train2:0.7860, info:8.8211
[97] ner test1:2.9555, rc test1:8.6457, ner test2:2.2856, rc test2:1.7021, info:3.7812
[97] precision:0.8452, recall:0.7836, f1:0.8132
[98] ner train1:2.0359, rc_train1:7.8042, ner train2:0.8498, rc_train2:0.7492, info:4.2546
[98] ner test1:3.0918, rc test1:7.9854, ner test2:2.4843, rc test2:1.7628, info:1.2371
[98] precision:0.8147, recall:0.7983, f1:0.8064
[99] ner train1:2.1092, rc_train1:7.7313, ner train2:0.8364, rc_train2:0.7493, info:8.6333
[99] ner test1:2.8943, rc test1:8.6297, ner test2:2.4921, rc test2:1.8745, info:2.6059
[99] precision:0.8371, recall:0.7582, f1:0.7957
[100] ner train1:1.9849, rc_train1:7.7824, ner train2:0.8465, rc_train2:0.7628, info:8.8369
[100] ner test1:2.9813, rc test1:9.0267, ner test2:2.5668, rc test2:1.8697, info:3.1338
[100] precision:0.7923, recall:0.7935, f1:0.7929