random2_n8_partial

[1] ner train1:9.2927, rc_train1:1504.1340, ner train2:4.9864, rc_train2:324.5660, info:0.0009
[1] ner test1:4.0231, rc test1:9.7221, ner test2:3.1474, rc test2:3.2150, info:0.0001
[1] precision:0.6558, recall:0.6171, f1:0.6359
[*] saved best model!
[2] ner train1:3.8610, rc_train1:10.3965, ner train2:2.9646, rc_train2:2.9716, info:0.0001
[2] ner test1:3.5144, rc test1:10.0687, ner test2:2.6390, rc test2:2.4651, info:0.0001
[2] precision:0.7598, recall:0.6575, f1:0.7049
[*] saved best model!
[3] ner train1:3.4418, rc_train1:9.1025, ner train2:2.5713, rc_train2:2.4376, info:0.0001
[3] ner test1:3.2477, rc test1:8.7968, ner test2:2.3698, rc test2:2.1830, info:0.0001
[3] precision:0.8051, recall:0.6941, f1:0.7455
[*] saved best model!
[4] ner train1:3.1660, rc_train1:8.2081, ner train2:2.3282, rc_train2:2.1738, info:0.0002
[4] ner test1:3.0897, rc test1:8.7312, ner test2:2.2441, rc test2:2.0311, info:0.0002
[4] precision:0.8303, recall:0.7063, f1:0.7633
[*] saved best model!
[5] ner train1:2.9871, rc_train1:7.7940, ner train2:2.1490, rc_train2:1.9715, info:0.0002
[5] ner test1:2.9892, rc test1:8.0263, ner test2:2.1852, rc test2:1.9250, info:0.0002
[5] precision:0.8229, recall:0.7403, f1:0.7794
[*] saved best model!
[6] ner train1:2.8416, rc_train1:7.4748, ner train2:2.0078, rc_train2:1.8282, info:0.0002
[6] ner test1:2.8853, rc test1:7.7105, ner test2:2.1236, rc test2:1.8413, info:0.0002
[6] precision:0.8404, recall:0.7482, f1:0.7917
[*] saved best model!
[7] ner train1:2.7066, rc_train1:7.1647, ner train2:1.8789, rc_train2:1.7091, info:0.0002
[7] ner test1:2.8385, rc test1:7.8672, ner test2:2.0982, rc test2:1.7678, info:0.0002
[7] precision:0.8420, recall:0.7566, f1:0.7970
[*] saved best model!
[8] ner train1:2.6140, rc_train1:7.1059, ner train2:1.7845, rc_train2:1.6004, info:0.0002
[8] ner test1:2.7700, rc test1:7.4893, ner test2:2.0590, rc test2:1.7131, info:0.0002
[8] precision:0.8424, recall:0.7841, f1:0.8122
[*] saved best model!
[9] ner train1:2.5240, rc_train1:6.9834, ner train2:1.6802, rc_train2:1.5099, info:0.0005
[9] ner test1:2.7535, rc test1:7.0604, ner test2:2.0460, rc test2:1.6798, info:0.0003
[9] precision:0.8423, recall:0.7927, f1:0.8167
[*] saved best model!
[10] ner train1:2.4556, rc_train1:6.9185, ner train2:1.6093, rc_train2:1.4274, info:0.0005
[10] ner test1:2.7420, rc test1:7.2593, ner test2:2.0497, rc test2:1.6584, info:0.0004
[10] precision:0.8316, recall:0.8112, f1:0.8213
[*] saved best model!
[11] ner train1:2.3887, rc_train1:6.8423, ner train2:1.5228, rc_train2:1.3474, info:0.0013
[11] ner test1:2.7322, rc test1:6.9844, ner test2:2.0350, rc test2:1.6311, info:0.0009
[11] precision:0.8495, recall:0.8089, f1:0.8287
[*] saved best model!
[12] ner train1:2.3538, rc_train1:6.7634, ner train2:1.4812, rc_train2:1.3057, info:0.0008
[12] ner test1:2.7027, rc test1:6.9471, ner test2:2.0654, rc test2:1.6528, info:0.0006
[12] precision:0.8205, recall:0.8365, f1:0.8284
[13] ner train1:2.3098, rc_train1:6.7407, ner train2:1.4295, rc_train2:1.2487, info:0.0004
[13] ner test1:2.7340, rc test1:7.4141, ner test2:1.9958, rc test2:1.5901, info:0.0002
[13] precision:0.8447, recall:0.8287, f1:0.8366
[*] saved best model!
[14] ner train1:2.2688, rc_train1:6.7329, ner train2:1.3678, rc_train2:1.1798, info:0.0004
[14] ner test1:2.7234, rc test1:7.0322, ner test2:2.0508, rc test2:1.5947, info:0.0004
[14] precision:0.8206, recall:0.8475, f1:0.8338
[15] ner train1:2.2249, rc_train1:6.7432, ner train2:1.3107, rc_train2:1.1389, info:0.0004
[15] ner test1:2.6819, rc test1:7.1597, ner test2:2.0314, rc test2:1.5612, info:0.0003
[15] precision:0.8361, recall:0.8417, f1:0.8389
[*] saved best model!
[16] ner train1:2.1816, rc_train1:6.6823, ner train2:1.2639, rc_train2:1.0769, info:0.0003
[16] ner test1:2.6898, rc test1:6.9048, ner test2:2.0200, rc test2:1.5318, info:0.0003
[16] precision:0.8436, recall:0.8438, f1:0.8437
[*] saved best model!
[17] ner train1:2.1541, rc_train1:6.6057, ner train2:1.2101, rc_train2:1.0280, info:0.0003
[17] ner test1:2.6867, rc test1:7.4776, ner test2:2.0237, rc test2:1.5270, info:0.0003
[17] precision:0.8449, recall:0.8513, f1:0.8481
[*] saved best model!
[18] ner train1:2.1234, rc_train1:6.6529, ner train2:1.1784, rc_train2:1.0083, info:0.0003
[18] ner test1:2.6434, rc test1:7.1343, ner test2:2.0311, rc test2:1.5330, info:0.0004
[18] precision:0.8731, recall:0.8282, f1:0.8501
[*] saved best model!
[19] ner train1:2.0940, rc_train1:6.7665, ner train2:1.1348, rc_train2:0.9583, info:0.0004
[19] ner test1:2.6481, rc test1:7.1694, ner test2:2.0164, rc test2:1.5084, info:0.0004
[19] precision:0.8770, recall:0.8296, f1:0.8526
[*] saved best model!
[20] ner train1:2.0643, rc_train1:6.6837, ner train2:1.0985, rc_train2:0.9339, info:0.0004
[20] ner test1:2.6468, rc test1:7.1834, ner test2:2.0460, rc test2:1.4919, info:0.0003
[20] precision:0.8599, recall:0.8477, f1:0.8538
[*] saved best model!
[21] ner train1:2.0497, rc_train1:6.6758, ner train2:1.0665, rc_train2:0.9066, info:0.0003
[21] ner test1:2.6816, rc test1:7.0142, ner test2:2.0619, rc test2:1.5033, info:0.0003
[21] precision:0.8616, recall:0.8506, f1:0.8560
[*] saved best model!
[22] ner train1:2.0271, rc_train1:6.6592, ner train2:1.0315, rc_train2:0.8800, info:0.0003
[22] ner test1:2.6633, rc test1:6.8758, ner test2:2.1882, rc test2:1.5599, info:0.0002
[22] precision:0.8336, recall:0.8679, f1:0.8504
[23] ner train1:1.9817, rc_train1:6.6782, ner train2:0.9870, rc_train2:0.8458, info:0.0009
[23] ner test1:2.6519, rc test1:6.7189, ner test2:2.1043, rc test2:1.5349, info:0.0002
[23] precision:0.8717, recall:0.8406, f1:0.8559
[24] ner train1:1.9747, rc_train1:6.6133, ner train2:0.9582, rc_train2:0.8146, info:0.0003
[24] ner test1:2.6781, rc test1:6.7906, ner test2:2.1225, rc test2:1.4831, info:0.0003
[24] precision:0.8602, recall:0.8555, f1:0.8578
[*] saved best model!
[25] ner train1:1.9457, rc_train1:6.6207, ner train2:0.9293, rc_train2:0.7918, info:0.0003
[25] ner test1:2.6902, rc test1:7.3107, ner test2:2.1104, rc test2:1.4976, info:0.0003
[25] precision:0.8828, recall:0.8430, f1:0.8624
[*] saved best model!
[26] ner train1:1.9265, rc_train1:6.6267, ner train2:0.9086, rc_train2:0.7752, info:0.0003
[26] ner test1:2.6720, rc test1:6.8842, ner test2:2.1091, rc test2:1.5051, info:0.0003
[26] precision:0.8711, recall:0.8527, f1:0.8618
[27] ner train1:1.9136, rc_train1:6.5788, ner train2:0.8848, rc_train2:0.7566, info:0.0004
[27] ner test1:2.7046, rc test1:6.8287, ner test2:2.2081, rc test2:1.5392, info:0.0002
[27] precision:0.8516, recall:0.8676, f1:0.8595
[28] ner train1:1.8896, rc_train1:6.5843, ner train2:0.8663, rc_train2:0.7371, info:0.0004
[28] ner test1:2.6955, rc test1:6.8664, ner test2:2.1802, rc test2:1.5555, info:0.0003
[28] precision:0.8645, recall:0.8597, f1:0.8621
[29] ner train1:1.8861, rc_train1:6.5057, ner train2:0.8539, rc_train2:0.7178, info:0.0003
[29] ner test1:2.7162, rc test1:6.6859, ner test2:2.2429, rc test2:1.5559, info:0.0003
[29] precision:0.8584, recall:0.8630, f1:0.8607
[30] ner train1:1.8578, rc_train1:6.5169, ner train2:0.8187, rc_train2:0.7056, info:0.0008
[30] ner test1:2.7010, rc test1:6.6377, ner test2:2.2063, rc test2:1.5408, info:0.0003
[30] precision:0.8588, recall:0.8686, f1:0.8637
[*] saved best model!
[31] ner train1:1.8542, rc_train1:6.5414, ner train2:0.7947, rc_train2:0.6859, info:0.0003
[31] ner test1:2.6933, rc test1:6.8974, ner test2:2.2484, rc test2:1.5739, info:0.0002
[31] precision:0.8570, recall:0.8689, f1:0.8629
[32] ner train1:1.8371, rc_train1:6.4775, ner train2:0.7845, rc_train2:0.6783, info:0.0003
[32] ner test1:2.6750, rc test1:6.7669, ner test2:2.2333, rc test2:1.5687, info:0.0003
[32] precision:0.8532, recall:0.8748, f1:0.8639
[*] saved best model!
[33] ner train1:1.8195, rc_train1:6.3596, ner train2:0.7676, rc_train2:0.6551, info:0.0003
[33] ner test1:2.7279, rc test1:6.8567, ner test2:2.3279, rc test2:1.5643, info:0.0003
[33] precision:0.8744, recall:0.8564, f1:0.8653
[*] saved best model!
[34] ner train1:1.8149, rc_train1:6.4211, ner train2:0.7507, rc_train2:0.6397, info:0.0003
[34] ner test1:2.7271, rc test1:6.6323, ner test2:2.3748, rc test2:1.6524, info:0.0003
[34] precision:0.8479, recall:0.8727, f1:0.8601
[35] ner train1:1.8006, rc_train1:6.4261, ner train2:0.7306, rc_train2:0.6279, info:0.0003
[35] ner test1:2.7527, rc test1:6.7503, ner test2:2.3441, rc test2:1.6129, info:0.0003
[35] precision:0.8661, recall:0.8667, f1:0.8664
[*] saved best model!
[36] ner train1:1.7771, rc_train1:6.3587, ner train2:0.7092, rc_train2:0.6045, info:0.0003
[36] ner test1:2.6908, rc test1:6.5057, ner test2:2.3039, rc test2:1.6081, info:0.0004
[36] precision:0.8871, recall:0.8527, f1:0.8695
[*] saved best model!
[37] ner train1:1.7742, rc_train1:6.3398, ner train2:0.7077, rc_train2:0.6069, info:0.0003
[37] ner test1:2.7256, rc test1:6.7126, ner test2:2.3563, rc test2:1.6148, info:0.0004
[37] precision:0.8682, recall:0.8661, f1:0.8672
[38] ner train1:1.7553, rc_train1:6.3264, ner train2:0.6780, rc_train2:0.5934, info:0.0003
[38] ner test1:2.7412, rc test1:6.7562, ner test2:2.3873, rc test2:1.6369, info:0.0005
[38] precision:0.8683, recall:0.8652, f1:0.8668
[39] ner train1:1.7549, rc_train1:6.3340, ner train2:0.6754, rc_train2:0.5921, info:0.0003
[39] ner test1:2.7174, rc test1:6.7501, ner test2:2.4015, rc test2:1.6162, info:0.0003
[39] precision:0.8671, recall:0.8644, f1:0.8658
[40] ner train1:1.7349, rc_train1:6.3274, ner train2:0.6568, rc_train2:0.5651, info:0.0003
[40] ner test1:2.7662, rc test1:6.5470, ner test2:2.4614, rc test2:1.6324, info:0.0003
[40] precision:0.8485, recall:0.8782, f1:0.8631
[41] ner train1:1.7223, rc_train1:6.3094, ner train2:0.6383, rc_train2:0.5603, info:0.0003
[41] ner test1:2.8137, rc test1:6.9348, ner test2:2.4772, rc test2:1.6716, info:0.0003
[41] precision:0.8559, recall:0.8752, f1:0.8655
[42] ner train1:1.7171, rc_train1:6.3066, ner train2:0.6317, rc_train2:0.5527, info:0.0003
[42] ner test1:2.7347, rc test1:6.5089, ner test2:2.4972, rc test2:1.6780, info:0.0003
[42] precision:0.8472, recall:0.8808, f1:0.8637
[43] ner train1:1.7102, rc_train1:6.2375, ner train2:0.6176, rc_train2:0.5386, info:0.0008
[43] ner test1:2.7803, rc test1:6.8494, ner test2:2.4999, rc test2:1.6825, info:0.0003
[43] precision:0.8554, recall:0.8751, f1:0.8651
[44] ner train1:1.6911, rc_train1:6.2349, ner train2:0.6032, rc_train2:0.5274, info:0.0003
[44] ner test1:2.7640, rc test1:6.6861, ner test2:2.5117, rc test2:1.6844, info:0.0003
[44] precision:0.8560, recall:0.8755, f1:0.8656
[45] ner train1:1.6823, rc_train1:6.2944, ner train2:0.5931, rc_train2:0.5253, info:0.0003
[45] ner test1:2.7124, rc test1:6.4476, ner test2:2.4681, rc test2:1.6294, info:0.0003
[45] precision:0.8748, recall:0.8668, f1:0.8708
[*] saved best model!
[46] ner train1:1.6693, rc_train1:6.1777, ner train2:0.5782, rc_train2:0.5099, info:0.0003
[46] ner test1:2.7660, rc test1:6.6984, ner test2:2.5017, rc test2:1.6667, info:0.0003
[46] precision:0.8591, recall:0.8726, f1:0.8658
[47] ner train1:1.6667, rc_train1:6.1236, ner train2:0.5668, rc_train2:0.4997, info:0.0003
[47] ner test1:2.7368, rc test1:6.4281, ner test2:2.5130, rc test2:1.6900, info:0.0003
[47] precision:0.8607, recall:0.8756, f1:0.8681
[48] ner train1:1.6664, rc_train1:6.1639, ner train2:0.5662, rc_train2:0.5051, info:0.0003
[48] ner test1:2.7941, rc test1:6.4932, ner test2:2.6271, rc test2:1.7264, info:0.0003
[48] precision:0.8530, recall:0.8789, f1:0.8658
[49] ner train1:1.6522, rc_train1:6.1283, ner train2:0.5593, rc_train2:0.4876, info:0.0003
[49] ner test1:2.7493, rc test1:6.6429, ner test2:2.4589, rc test2:1.6961, info:0.0003
[49] precision:0.8747, recall:0.8658, f1:0.8702
[50] ner train1:1.6406, rc_train1:6.1255, ner train2:0.5402, rc_train2:0.4898, info:0.0003
[50] ner test1:2.7671, rc test1:6.5052, ner test2:2.4877, rc test2:1.7177, info:0.0002
[50] precision:0.8801, recall:0.8580, f1:0.8689
[51] ner train1:1.6447, rc_train1:6.0869, ner train2:0.5414, rc_train2:0.4801, info:0.0004
[51] ner test1:2.7776, rc test1:6.5012, ner test2:2.5465, rc test2:1.6785, info:0.0003
[51] precision:0.8632, recall:0.8762, f1:0.8697
[52] ner train1:1.6281, rc_train1:6.0790, ner train2:0.5255, rc_train2:0.4650, info:0.0005
[52] ner test1:2.7964, rc test1:6.5150, ner test2:2.6470, rc test2:1.7349, info:0.0003
[52] precision:0.8715, recall:0.8640, f1:0.8677
[53] ner train1:1.6328, rc_train1:6.0587, ner train2:0.5317, rc_train2:0.4676, info:0.0003
[53] ner test1:2.8739, rc test1:6.5748, ner test2:2.6133, rc test2:1.7093, info:0.0003
[53] precision:0.8657, recall:0.8702, f1:0.8679
[54] ner train1:1.6116, rc_train1:6.0215, ner train2:0.5120, rc_train2:0.4566, info:0.0003
[54] ner test1:2.7773, rc test1:6.5544, ner test2:2.6592, rc test2:1.7851, info:0.0003
[54] precision:0.8615, recall:0.8716, f1:0.8666
[55] ner train1:1.6034, rc_train1:6.0391, ner train2:0.4979, rc_train2:0.4499, info:0.0003
[55] ner test1:2.8041, rc test1:6.6667, ner test2:2.6327, rc test2:1.7794, info:0.0003
[55] precision:0.8679, recall:0.8709, f1:0.8694
[56] ner train1:1.5942, rc_train1:6.0009, ner train2:0.4966, rc_train2:0.4343, info:0.0003
[56] ner test1:2.7770, rc test1:6.4249, ner test2:2.6733, rc test2:1.7800, info:0.0003
[56] precision:0.8645, recall:0.8773, f1:0.8709
[*] saved best model!
[57] ner train1:1.5874, rc_train1:6.0051, ner train2:0.4979, rc_train2:0.4501, info:0.0003
[57] ner test1:2.7692, rc test1:6.2995, ner test2:2.6151, rc test2:1.7506, info:0.0003
[57] precision:0.8836, recall:0.8651, f1:0.8743
[*] saved best model!
[58] ner train1:1.5678, rc_train1:5.9259, ner train2:0.4713, rc_train2:0.4232, info:0.0003
[58] ner test1:2.7978, rc test1:6.2116, ner test2:2.6696, rc test2:1.8287, info:0.0003
[58] precision:0.8683, recall:0.8707, f1:0.8695
[59] ner train1:1.5732, rc_train1:5.9015, ner train2:0.4679, rc_train2:0.4223, info:0.0003
[59] ner test1:2.7853, rc test1:6.3529, ner test2:2.6923, rc test2:1.8030, info:0.0003
[59] precision:0.8662, recall:0.8748, f1:0.8705
[60] ner train1:1.5691, rc_train1:5.8610, ner train2:0.4736, rc_train2:0.4211, info:0.0003
[60] ner test1:2.8115, rc test1:6.2039, ner test2:2.7639, rc test2:1.8449, info:0.0003
[60] precision:0.8580, recall:0.8826, f1:0.8701
[61] ner train1:1.5594, rc_train1:5.8626, ner train2:0.4632, rc_train2:0.4188, info:0.0003
[61] ner test1:2.8012, rc test1:6.4776, ner test2:2.7229, rc test2:1.8227, info:0.0003
[61] precision:0.8590, recall:0.8734, f1:0.8661
[62] ner train1:1.5609, rc_train1:5.8833, ner train2:0.4584, rc_train2:0.4187, info:0.0003
[62] ner test1:2.8066, rc test1:6.5707, ner test2:2.7605, rc test2:1.8174, info:0.0003
[62] precision:0.8771, recall:0.8684, f1:0.8727
[63] ner train1:1.5503, rc_train1:5.8896, ner train2:0.4545, rc_train2:0.4119, info:0.0003
[63] ner test1:2.7824, rc test1:6.2095, ner test2:2.7691, rc test2:1.8503, info:0.0003
[63] precision:0.8571, recall:0.8795, f1:0.8682
[64] ner train1:1.5372, rc_train1:5.8181, ner train2:0.4421, rc_train2:0.3940, info:0.0003
[64] ner test1:2.8393, rc test1:6.2774, ner test2:2.7144, rc test2:1.8373, info:0.0003
[64] precision:0.8815, recall:0.8665, f1:0.8739
[65] ner train1:1.5323, rc_train1:5.8147, ner train2:0.4373, rc_train2:0.3985, info:0.0003
[65] ner test1:2.8184, rc test1:6.5291, ner test2:2.8414, rc test2:1.8837, info:0.0003
[65] precision:0.8627, recall:0.8741, f1:0.8684
[66] ner train1:1.5212, rc_train1:5.8130, ner train2:0.4241, rc_train2:0.3901, info:0.0003
[66] ner test1:2.8139, rc test1:6.3851, ner test2:2.8019, rc test2:1.8944, info:0.0002
[66] precision:0.8636, recall:0.8758, f1:0.8697
[67] ner train1:1.5177, rc_train1:5.8861, ner train2:0.4245, rc_train2:0.3974, info:0.0003
[67] ner test1:2.8526, rc test1:6.4235, ner test2:2.9067, rc test2:1.8912, info:0.0003
[67] precision:0.8672, recall:0.8773, f1:0.8722
[68] ner train1:1.5095, rc_train1:5.7698, ner train2:0.4148, rc_train2:0.3770, info:0.0003
[68] ner test1:2.8317, rc test1:6.4705, ner test2:2.8999, rc test2:1.8813, info:0.0003
[68] precision:0.8683, recall:0.8768, f1:0.8725
[69] ner train1:1.5220, rc_train1:5.7785, ner train2:0.4186, rc_train2:0.3915, info:0.0003
[69] ner test1:2.8021, rc test1:6.3432, ner test2:2.8086, rc test2:1.8156, info:0.0006
[69] precision:0.8618, recall:0.8811, f1:0.8714
[70] ner train1:1.5073, rc_train1:5.8209, ner train2:0.4041, rc_train2:0.3822, info:0.0003
[70] ner test1:2.8351, rc test1:6.4004, ner test2:2.9047, rc test2:1.8425, info:0.0003
[70] precision:0.8677, recall:0.8785, f1:0.8731
[71] ner train1:1.5048, rc_train1:5.8031, ner train2:0.4150, rc_train2:0.3784, info:0.0003
[71] ner test1:2.8294, rc test1:6.3473, ner test2:2.8989, rc test2:1.8896, info:0.0003
[71] precision:0.8749, recall:0.8708, f1:0.8728
[72] ner train1:1.5005, rc_train1:5.7664, ner train2:0.3913, rc_train2:0.3674, info:0.0003
[72] ner test1:2.8795, rc test1:6.3444, ner test2:2.9289, rc test2:1.8925, info:0.0003
[72] precision:0.8640, recall:0.8785, f1:0.8712
[73] ner train1:1.4903, rc_train1:5.6625, ner train2:0.3978, rc_train2:0.3685, info:0.0003
[73] ner test1:2.8759, rc test1:6.2049, ner test2:2.9647, rc test2:2.0102, info:0.0003
[73] precision:0.8507, recall:0.8793, f1:0.8647
[74] ner train1:1.4940, rc_train1:5.6860, ner train2:0.3962, rc_train2:0.3750, info:0.0003
[74] ner test1:2.9045, rc test1:6.4154, ner test2:3.0166, rc test2:1.8925, info:0.0002
[74] precision:0.8627, recall:0.8803, f1:0.8714
[75] ner train1:1.4884, rc_train1:5.7202, ner train2:0.3890, rc_train2:0.3605, info:0.0003
[75] ner test1:2.8731, rc test1:6.0509, ner test2:2.9087, rc test2:1.8875, info:0.0003
[75] precision:0.8777, recall:0.8695, f1:0.8736
[76] ner train1:1.4936, rc_train1:5.6653, ner train2:0.3791, rc_train2:0.3510, info:0.0003
[76] ner test1:2.9128, rc test1:6.3828, ner test2:2.9839, rc test2:1.9127, info:0.0003
[76] precision:0.8657, recall:0.8810, f1:0.8733
[77] ner train1:1.4815, rc_train1:5.6859, ner train2:0.3786, rc_train2:0.3597, info:0.0003
[77] ner test1:2.8604, rc test1:6.1969, ner test2:2.9503, rc test2:1.9723, info:0.0003
[77] precision:0.8737, recall:0.8695, f1:0.8716
[78] ner train1:1.4768, rc_train1:5.6518, ner train2:0.3789, rc_train2:0.3575, info:0.0003
[78] ner test1:2.8592, rc test1:6.2240, ner test2:2.8495, rc test2:1.9346, info:0.0003
[78] precision:0.8650, recall:0.8756, f1:0.8703
[79] ner train1:1.4755, rc_train1:5.6644, ner train2:0.3754, rc_train2:0.3611, info:0.0003
[79] ner test1:2.8608, rc test1:6.0093, ner test2:2.9411, rc test2:1.9387, info:0.0003
[79] precision:0.8668, recall:0.8724, f1:0.8696
[80] ner train1:1.4682, rc_train1:5.6029, ner train2:0.3707, rc_train2:0.3521, info:0.0003
[80] ner test1:2.8650, rc test1:6.0626, ner test2:2.8038, rc test2:1.8679, info:0.0003
[80] precision:0.8734, recall:0.8708, f1:0.8721
[81] ner train1:1.4660, rc_train1:5.6462, ner train2:0.3655, rc_train2:0.3463, info:0.0004
[81] ner test1:2.8610, rc test1:6.2740, ner test2:2.8859, rc test2:1.8972, info:0.0003
[81] precision:0.8675, recall:0.8736, f1:0.8706
[82] ner train1:1.4625, rc_train1:5.6012, ner train2:0.3645, rc_train2:0.3520, info:0.0004
[82] ner test1:2.8956, rc test1:6.1769, ner test2:2.9095, rc test2:1.9500, info:0.0003
[82] precision:0.8643, recall:0.8778, f1:0.8710
[83] ner train1:1.4547, rc_train1:5.6299, ner train2:0.3641, rc_train2:0.3420, info:0.0004
[83] ner test1:2.8685, rc test1:6.2379, ner test2:2.9647, rc test2:1.9682, info:0.0004
[83] precision:0.8559, recall:0.8768, f1:0.8662
[84] ner train1:1.4447, rc_train1:5.5801, ner train2:0.3658, rc_train2:0.3503, info:0.0004
[84] ner test1:2.9125, rc test1:6.3880, ner test2:2.9368, rc test2:1.9925, info:0.0003
[84] precision:0.8655, recall:0.8734, f1:0.8694
[85] ner train1:1.4387, rc_train1:5.6059, ner train2:0.3499, rc_train2:0.3369, info:0.0003
[85] ner test1:2.8803, rc test1:6.0651, ner test2:3.0673, rc test2:1.9854, info:0.0003
[85] precision:0.8491, recall:0.8875, f1:0.8679
[86] ner train1:1.4480, rc_train1:5.5536, ner train2:0.3517, rc_train2:0.3353, info:0.0005
[86] ner test1:2.8682, rc test1:6.2763, ner test2:2.9974, rc test2:1.9691, info:0.0003
[86] precision:0.8696, recall:0.8746, f1:0.8721
[87] ner train1:1.4399, rc_train1:5.5060, ner train2:0.3474, rc_train2:0.3404, info:0.0003
[87] ner test1:2.8622, rc test1:6.0825, ner test2:3.0670, rc test2:1.9572, info:0.0003
[87] precision:0.8554, recall:0.8790, f1:0.8671
[88] ner train1:1.4298, rc_train1:5.5338, ner train2:0.3416, rc_train2:0.3306, info:0.0003
[88] ner test1:2.9279, rc test1:6.2144, ner test2:3.0303, rc test2:2.0372, info:0.0003
[88] precision:0.8653, recall:0.8719, f1:0.8686