random9_n8_exact

[1] ner train1:9.6169, rc_train1:1513.7231, ner train2:5.1130, rc_train2:382.1946, info:0.0003
[1] ner test1:4.1664, rc test1:12.3386, ner test2:3.0986, rc test2:3.2282, info:0.0001
[1] precision:0.6142, recall:0.5639, f1:0.5880
[*] saved best model!
[2] ner train1:3.9654, rc_train1:11.1530, ner train2:2.9925, rc_train2:3.0368, info:0.0001
[2] ner test1:3.5784, rc test1:10.5650, ner test2:2.7011, rc test2:2.6149, info:0.0001
[2] precision:0.7240, recall:0.6081, f1:0.6610
[*] saved best model!
[3] ner train1:3.4927, rc_train1:9.9903, ner train2:2.6572, rc_train2:2.5512, info:0.0001
[3] ner test1:3.2486, rc test1:10.4215, ner test2:2.4535, rc test2:2.2898, info:0.0001
[3] precision:0.7213, recall:0.6705, f1:0.6950
[*] saved best model!
[4] ner train1:3.1752, rc_train1:9.5834, ner train2:2.3811, rc_train2:2.2252, info:0.0001
[4] ner test1:3.0581, rc test1:9.9823, ner test2:2.2832, rc test2:2.0720, info:0.0001
[4] precision:0.7624, recall:0.6792, f1:0.7184
[*] saved best model!
[5] ner train1:2.9580, rc_train1:9.0323, ner train2:2.1693, rc_train2:2.0094, info:0.0002
[5] ner test1:2.9192, rc test1:9.4755, ner test2:2.1733, rc test2:1.9342, info:0.0002
[5] precision:0.7787, recall:0.6972, f1:0.7357
[*] saved best model!
[6] ner train1:2.7971, rc_train1:8.4629, ner train2:2.0100, rc_train2:1.8448, info:0.0002
[6] ner test1:2.8434, rc test1:9.2864, ner test2:2.1142, rc test2:1.8739, info:0.0002
[6] precision:0.7687, recall:0.7080, f1:0.7371
[*] saved best model!
[7] ner train1:2.6860, rc_train1:8.1744, ner train2:1.8869, rc_train2:1.7128, info:0.0002
[7] ner test1:2.8606, rc test1:8.9441, ner test2:2.0998, rc test2:1.7867, info:0.0002
[7] precision:0.7878, recall:0.7221, f1:0.7535
[*] saved best model!
[8] ner train1:2.5952, rc_train1:7.8925, ner train2:1.7861, rc_train2:1.6062, info:0.0002
[8] ner test1:2.8152, rc test1:8.3444, ner test2:2.0693, rc test2:1.7490, info:0.0002
[8] precision:0.7838, recall:0.7186, f1:0.7498
[9] ner train1:2.5082, rc_train1:7.7617, ner train2:1.6983, rc_train2:1.5214, info:0.0002
[9] ner test1:2.7547, rc test1:7.9043, ner test2:2.0225, rc test2:1.6883, info:0.0011
[9] precision:0.7957, recall:0.7316, f1:0.7623
[*] saved best model!
[10] ner train1:2.4436, rc_train1:7.7568, ner train2:1.6071, rc_train2:1.4390, info:0.0006
[10] ner test1:2.7109, rc test1:8.2466, ner test2:2.0184, rc test2:1.6639, info:0.0004
[10] precision:0.7998, recall:0.7455, f1:0.7717
[*] saved best model!
[11] ner train1:2.4010, rc_train1:7.7614, ner train2:1.5544, rc_train2:1.3756, info:0.0003
[11] ner test1:2.7107, rc test1:8.2197, ner test2:2.0168, rc test2:1.6253, info:0.0008
[11] precision:0.7886, recall:0.7555, f1:0.7717
[12] ner train1:2.3393, rc_train1:7.6866, ner train2:1.4798, rc_train2:1.3020, info:0.0004
[12] ner test1:2.7024, rc test1:8.1842, ner test2:2.0116, rc test2:1.6036, info:0.0003
[12] precision:0.7888, recall:0.7317, f1:0.7592
[13] ner train1:2.3015, rc_train1:7.6995, ner train2:1.4223, rc_train2:1.2386, info:0.0003
[13] ner test1:2.6967, rc test1:8.0551, ner test2:2.0197, rc test2:1.6194, info:0.0002
[13] precision:0.7785, recall:0.7757, f1:0.7771
[*] saved best model!
[14] ner train1:2.2572, rc_train1:7.7226, ner train2:1.3535, rc_train2:1.1813, info:0.0003
[14] ner test1:2.6777, rc test1:7.9953, ner test2:2.0525, rc test2:1.6461, info:0.0002
[14] precision:0.7765, recall:0.7891, f1:0.7828
[*] saved best model!
[15] ner train1:2.2080, rc_train1:7.7714, ner train2:1.3031, rc_train2:1.1304, info:0.0006
[15] ner test1:2.7404, rc test1:8.3729, ner test2:1.9974, rc test2:1.5744, info:0.0002
[15] precision:0.7943, recall:0.7861, f1:0.7902
[*] saved best model!
[16] ner train1:2.1813, rc_train1:7.7377, ner train2:1.2678, rc_train2:1.1016, info:0.0003
[16] ner test1:2.6861, rc test1:7.9725, ner test2:2.0651, rc test2:1.5882, info:0.0002
[16] precision:0.7883, recall:0.7914, f1:0.7898
[17] ner train1:2.1490, rc_train1:7.6990, ner train2:1.2218, rc_train2:1.0586, info:0.0002
[17] ner test1:2.6505, rc test1:7.9769, ner test2:2.0493, rc test2:1.6140, info:0.0002
[17] precision:0.7763, recall:0.8010, f1:0.7884
[18] ner train1:2.1219, rc_train1:7.6439, ner train2:1.1754, rc_train2:1.0183, info:0.0010
[18] ner test1:2.7013, rc test1:7.9344, ner test2:2.0796, rc test2:1.6638, info:0.0002
[18] precision:0.7637, recall:0.8191, f1:0.7905
[*] saved best model!
[19] ner train1:2.0979, rc_train1:7.6491, ner train2:1.1358, rc_train2:0.9722, info:0.0002
[19] ner test1:2.6526, rc test1:8.1161, ner test2:2.0671, rc test2:1.6227, info:0.0002
[19] precision:0.7591, recall:0.8028, f1:0.7804
[20] ner train1:2.0590, rc_train1:7.5341, ner train2:1.1011, rc_train2:0.9500, info:0.0002
[20] ner test1:2.6304, rc test1:7.7401, ner test2:2.1054, rc test2:1.5535, info:0.0002
[20] precision:0.7925, recall:0.7678, f1:0.7800
[21] ner train1:2.0457, rc_train1:7.5342, ner train2:1.0707, rc_train2:0.9136, info:0.0002
[21] ner test1:2.6708, rc test1:7.9592, ner test2:2.1149, rc test2:1.5863, info:0.0002
[21] precision:0.7980, recall:0.7899, f1:0.7939
[*] saved best model!
[22] ner train1:2.0280, rc_train1:7.4970, ner train2:1.0430, rc_train2:0.8898, info:0.0003
[22] ner test1:2.6896, rc test1:8.0933, ner test2:2.1717, rc test2:1.6139, info:0.0002
[22] precision:0.7837, recall:0.8046, f1:0.7940
[*] saved best model!
[23] ner train1:1.9972, rc_train1:7.5445, ner train2:1.0042, rc_train2:0.8605, info:0.0003
[23] ner test1:2.6696, rc test1:7.8525, ner test2:2.1903, rc test2:1.6199, info:0.0003
[23] precision:0.7895, recall:0.8109, f1:0.8000
[*] saved best model!
[24] ner train1:1.9841, rc_train1:7.4206, ner train2:0.9799, rc_train2:0.8395, info:0.0002
[24] ner test1:2.6604, rc test1:7.7095, ner test2:2.1657, rc test2:1.6355, info:0.0003
[24] precision:0.7892, recall:0.8079, f1:0.7984
[25] ner train1:1.9612, rc_train1:7.3967, ner train2:0.9548, rc_train2:0.8209, info:0.0003
[25] ner test1:2.6859, rc test1:7.9274, ner test2:2.2227, rc test2:1.6125, info:0.0003
[25] precision:0.7939, recall:0.8131, f1:0.8034
[*] saved best model!
[26] ner train1:1.9352, rc_train1:7.4513, ner train2:0.9279, rc_train2:0.7962, info:0.0003
[26] ner test1:2.6739, rc test1:7.8952, ner test2:2.2839, rc test2:1.6635, info:0.0003
[26] precision:0.7860, recall:0.7910, f1:0.7885
[27] ner train1:1.9266, rc_train1:7.3739, ner train2:0.9065, rc_train2:0.7742, info:0.0003
[27] ner test1:2.6739, rc test1:7.7535, ner test2:2.2402, rc test2:1.6367, info:0.0003
[27] precision:0.7932, recall:0.8128, f1:0.8029
[28] ner train1:1.9149, rc_train1:7.3419, ner train2:0.8909, rc_train2:0.7547, info:0.0002
[28] ner test1:2.6961, rc test1:7.7938, ner test2:2.2287, rc test2:1.6128, info:0.0002
[28] precision:0.8068, recall:0.8074, f1:0.8071
[*] saved best model!
[29] ner train1:1.8974, rc_train1:7.3616, ner train2:0.8651, rc_train2:0.7354, info:0.0002
[29] ner test1:2.7306, rc test1:7.7282, ner test2:2.3361, rc test2:1.6631, info:0.0002
[29] precision:0.7832, recall:0.8149, f1:0.7988
[30] ner train1:1.8857, rc_train1:7.2627, ner train2:0.8416, rc_train2:0.7132, info:0.0003
[30] ner test1:2.7141, rc test1:7.8752, ner test2:2.3061, rc test2:1.6471, info:0.0002
[30] precision:0.7909, recall:0.8089, f1:0.7998
[31] ner train1:1.8569, rc_train1:7.3233, ner train2:0.8165, rc_train2:0.6972, info:0.0002
[31] ner test1:2.6658, rc test1:7.7161, ner test2:2.3007, rc test2:1.6287, info:0.0002
[31] precision:0.8176, recall:0.8039, f1:0.8107
[*] saved best model!
[32] ner train1:1.8405, rc_train1:7.2652, ner train2:0.8108, rc_train2:0.6839, info:0.0002
[32] ner test1:2.6953, rc test1:7.6161, ner test2:2.3067, rc test2:1.6842, info:0.0002
[32] precision:0.7982, recall:0.8047, f1:0.8014
[33] ner train1:1.8391, rc_train1:7.2496, ner train2:0.7832, rc_train2:0.6626, info:0.0002
[33] ner test1:2.7001, rc test1:7.5875, ner test2:2.3526, rc test2:1.6460, info:0.0002
[33] precision:0.8116, recall:0.8081, f1:0.8099
[34] ner train1:1.8183, rc_train1:7.2198, ner train2:0.7601, rc_train2:0.6574, info:0.0003
[34] ner test1:2.7402, rc test1:7.6763, ner test2:2.3526, rc test2:1.6954, info:0.0002
[34] precision:0.7972, recall:0.7979, f1:0.7976
[35] ner train1:1.8110, rc_train1:7.1423, ner train2:0.7533, rc_train2:0.6381, info:0.0002
[35] ner test1:2.7098, rc test1:7.3785, ner test2:2.4308, rc test2:1.6899, info:0.0002
[35] precision:0.8095, recall:0.8095, f1:0.8095
[36] ner train1:1.7914, rc_train1:7.0884, ner train2:0.7237, rc_train2:0.6255, info:0.0003
[36] ner test1:2.7398, rc test1:7.5368, ner test2:2.4337, rc test2:1.6535, info:0.0004
[36] precision:0.8093, recall:0.8268, f1:0.8179
[*] saved best model!
[37] ner train1:1.7854, rc_train1:7.0251, ner train2:0.7232, rc_train2:0.6229, info:0.0003
[37] ner test1:2.7045, rc test1:7.4725, ner test2:2.4259, rc test2:1.7150, info:0.0003
[37] precision:0.7950, recall:0.8100, f1:0.8024
[38] ner train1:1.7720, rc_train1:7.0069, ner train2:0.7037, rc_train2:0.6001, info:0.0003
[38] ner test1:2.7116, rc test1:7.3426, ner test2:2.4066, rc test2:1.7576, info:0.0002
[38] precision:0.7951, recall:0.8115, f1:0.8032
[39] ner train1:1.7665, rc_train1:7.0203, ner train2:0.6886, rc_train2:0.5868, info:0.0003
[39] ner test1:2.7542, rc test1:7.6446, ner test2:2.4255, rc test2:1.7203, info:0.0003
[39] precision:0.8180, recall:0.8065, f1:0.8122
[40] ner train1:1.7507, rc_train1:7.0388, ner train2:0.6752, rc_train2:0.5751, info:0.0003
[40] ner test1:2.7193, rc test1:7.6153, ner test2:2.4447, rc test2:1.7745, info:0.0002
[40] precision:0.8136, recall:0.8190, f1:0.8163
[41] ner train1:1.7556, rc_train1:7.0288, ner train2:0.6711, rc_train2:0.5779, info:0.0002
[41] ner test1:2.7388, rc test1:7.3542, ner test2:2.5019, rc test2:1.7656, info:0.0002
[41] precision:0.7964, recall:0.8303, f1:0.8130
[42] ner train1:1.7399, rc_train1:6.9371, ner train2:0.6587, rc_train2:0.5641, info:0.0002
[42] ner test1:2.7446, rc test1:7.3110, ner test2:2.4969, rc test2:1.7818, info:0.0002
[42] precision:0.8081, recall:0.8211, f1:0.8146
[43] ner train1:1.7226, rc_train1:6.9610, ner train2:0.6330, rc_train2:0.5460, info:0.0003
[43] ner test1:2.7907, rc test1:7.6989, ner test2:2.5669, rc test2:1.8194, info:0.0002
[43] precision:0.8037, recall:0.8277, f1:0.8155
[44] ner train1:1.7182, rc_train1:6.9159, ner train2:0.6245, rc_train2:0.5491, info:0.0003
[44] ner test1:2.7507, rc test1:7.6388, ner test2:2.5324, rc test2:1.7370, info:0.0002
[44] precision:0.8087, recall:0.8141, f1:0.8114
[45] ner train1:1.7097, rc_train1:6.9240, ner train2:0.6166, rc_train2:0.5427, info:0.0002
[45] ner test1:2.7810, rc test1:7.6133, ner test2:2.5697, rc test2:1.7665, info:0.0003
[45] precision:0.8105, recall:0.8297, f1:0.8200
[*] saved best model!
[46] ner train1:1.7083, rc_train1:6.9080, ner train2:0.6046, rc_train2:0.5253, info:0.0003
[46] ner test1:2.7456, rc test1:7.5204, ner test2:2.5741, rc test2:1.8020, info:0.0011
[46] precision:0.8182, recall:0.8258, f1:0.8220
[*] saved best model!
[47] ner train1:1.6917, rc_train1:6.8745, ner train2:0.5873, rc_train2:0.5111, info:0.0004
[47] ner test1:2.7395, rc test1:7.4793, ner test2:2.5500, rc test2:1.8228, info:0.0003
[47] precision:0.8132, recall:0.8074, f1:0.8103
[48] ner train1:1.6771, rc_train1:6.8064, ner train2:0.5750, rc_train2:0.5073, info:0.0004
[48] ner test1:2.7748, rc test1:7.1091, ner test2:2.6025, rc test2:1.7991, info:0.0003
[48] precision:0.8242, recall:0.8162, f1:0.8201
[49] ner train1:1.6732, rc_train1:6.7829, ner train2:0.5766, rc_train2:0.5150, info:0.0003
[49] ner test1:2.7764, rc test1:7.3158, ner test2:2.6473, rc test2:1.8198, info:0.0003
[49] precision:0.7977, recall:0.8099, f1:0.8037
[50] ner train1:1.6539, rc_train1:6.7933, ner train2:0.5663, rc_train2:0.4986, info:0.0004
[50] ner test1:2.7667, rc test1:7.2027, ner test2:2.6489, rc test2:1.8482, info:0.0007
[50] precision:0.7981, recall:0.8268, f1:0.8122
[51] ner train1:1.6511, rc_train1:6.7904, ner train2:0.5548, rc_train2:0.4861, info:0.0003
[51] ner test1:2.7788, rc test1:7.1668, ner test2:2.6430, rc test2:1.8958, info:0.0002
[51] precision:0.8098, recall:0.8166, f1:0.8132
[52] ner train1:1.6436, rc_train1:6.7785, ner train2:0.5441, rc_train2:0.4808, info:0.0003
[52] ner test1:2.7505, rc test1:7.2618, ner test2:2.6437, rc test2:1.8378, info:0.0003
[52] precision:0.8233, recall:0.8132, f1:0.8182
[53] ner train1:1.6397, rc_train1:6.7102, ner train2:0.5370, rc_train2:0.4738, info:0.0003
[53] ner test1:2.7441, rc test1:7.3150, ner test2:2.6395, rc test2:1.8742, info:0.0003
[53] precision:0.8155, recall:0.8253, f1:0.8204
[54] ner train1:1.6293, rc_train1:6.7012, ner train2:0.5205, rc_train2:0.4717, info:0.0003
[54] ner test1:2.7937, rc test1:7.2583, ner test2:2.7082, rc test2:1.8632, info:0.0003
[54] precision:0.8031, recall:0.8296, f1:0.8161
[55] ner train1:1.6304, rc_train1:6.7218, ner train2:0.5152, rc_train2:0.4552, info:0.0003
[55] ner test1:2.7721, rc test1:7.2237, ner test2:2.6960, rc test2:1.9666, info:0.0003
[55] precision:0.8068, recall:0.8247, f1:0.8156
[56] ner train1:1.6184, rc_train1:6.6376, ner train2:0.5131, rc_train2:0.4602, info:0.0003
[56] ner test1:2.7758, rc test1:7.2699, ner test2:2.6750, rc test2:1.9007, info:0.0003
[56] precision:0.8123, recall:0.8227, f1:0.8174
[57] ner train1:1.6063, rc_train1:6.6260, ner train2:0.5063, rc_train2:0.4532, info:0.0003
[57] ner test1:2.7941, rc test1:7.0564, ner test2:2.7274, rc test2:1.9391, info:0.0003
[57] precision:0.7975, recall:0.8197, f1:0.8085
[58] ner train1:1.5966, rc_train1:6.5950, ner train2:0.4948, rc_train2:0.4447, info:0.0003
[58] ner test1:2.7865, rc test1:7.0053, ner test2:2.7776, rc test2:1.9327, info:0.0003
[58] precision:0.8113, recall:0.8282, f1:0.8197
[59] ner train1:1.5972, rc_train1:6.5077, ner train2:0.4856, rc_train2:0.4382, info:0.0003
[59] ner test1:2.7995, rc test1:7.0654, ner test2:2.7818, rc test2:1.9464, info:0.0002
[59] precision:0.8142, recall:0.8199, f1:0.8170
[60] ner train1:1.5856, rc_train1:6.5575, ner train2:0.4816, rc_train2:0.4329, info:0.0003
[60] ner test1:2.8171, rc test1:6.9567, ner test2:2.8294, rc test2:1.9371, info:0.0003
[60] precision:0.8101, recall:0.8189, f1:0.8144
[61] ner train1:1.5769, rc_train1:6.5243, ner train2:0.4710, rc_train2:0.4338, info:0.0006
[61] ner test1:2.8095, rc test1:7.0847, ner test2:2.8493, rc test2:1.9570, info:0.0003
[61] precision:0.8002, recall:0.8244, f1:0.8121
[62] ner train1:1.5745, rc_train1:6.5740, ner train2:0.4628, rc_train2:0.4133, info:0.0004
[62] ner test1:2.7864, rc test1:7.1378, ner test2:2.8630, rc test2:2.0569, info:0.0003
[62] precision:0.8001, recall:0.8388, f1:0.8190
[63] ner train1:1.5730, rc_train1:6.5878, ner train2:0.4670, rc_train2:0.4168, info:0.0003
[63] ner test1:2.8515, rc test1:7.0031, ner test2:2.8443, rc test2:2.0353, info:0.0006
[63] precision:0.7903, recall:0.8302, f1:0.8097
[64] ner train1:1.5731, rc_train1:6.5432, ner train2:0.4566, rc_train2:0.4171, info:0.0009
[64] ner test1:2.7724, rc test1:7.0408, ner test2:2.7703, rc test2:1.9380, info:0.0003
[64] precision:0.8256, recall:0.8234, f1:0.8245
[*] saved best model!
[65] ner train1:1.5634, rc_train1:6.5284, ner train2:0.4530, rc_train2:0.4109, info:0.0004
[65] ner test1:2.8526, rc test1:7.0869, ner test2:2.8592, rc test2:2.0345, info:0.0003
[65] precision:0.8196, recall:0.8314, f1:0.8255
[*] saved best model!
[66] ner train1:1.5558, rc_train1:6.4986, ner train2:0.4456, rc_train2:0.4099, info:0.0003
[66] ner test1:2.7876, rc test1:6.8567, ner test2:2.8802, rc test2:1.9856, info:0.0003
[66] precision:0.8160, recall:0.8282, f1:0.8221
[67] ner train1:1.5528, rc_train1:6.4451, ner train2:0.4423, rc_train2:0.4039, info:0.0003
[67] ner test1:2.8076, rc test1:7.0616, ner test2:2.9354, rc test2:1.9730, info:0.0003
[67] precision:0.8183, recall:0.8380, f1:0.8280
[*] saved best model!
[68] ner train1:1.5437, rc_train1:6.4922, ner train2:0.4271, rc_train2:0.3928, info:0.0003
[68] ner test1:2.8056, rc test1:7.2494, ner test2:2.8491, rc test2:2.0277, info:0.0003
[68] precision:0.8087, recall:0.8228, f1:0.8157
[69] ner train1:1.5443, rc_train1:6.4622, ner train2:0.4257, rc_train2:0.3983, info:0.0003
[69] ner test1:2.8285, rc test1:7.0864, ner test2:2.8294, rc test2:1.9985, info:0.0003
[69] precision:0.8254, recall:0.8136, f1:0.8194
[70] ner train1:1.5349, rc_train1:6.4076, ner train2:0.4257, rc_train2:0.3945, info:0.0003
[70] ner test1:2.7945, rc test1:6.9921, ner test2:2.8214, rc test2:1.9931, info:0.0003
[70] precision:0.8007, recall:0.8274, f1:0.8138
[71] ner train1:1.5234, rc_train1:6.3862, ner train2:0.4092, rc_train2:0.3874, info:0.0004
[71] ner test1:2.7583, rc test1:6.9669, ner test2:2.8797, rc test2:1.9765, info:0.0003
[71] precision:0.8114, recall:0.8258, f1:0.8185
[72] ner train1:1.5218, rc_train1:6.3843, ner train2:0.4126, rc_train2:0.3798, info:0.0005
[72] ner test1:2.7949, rc test1:6.8159, ner test2:2.9419, rc test2:1.9820, info:0.0003
[72] precision:0.8117, recall:0.8256, f1:0.8186
[73] ner train1:1.5187, rc_train1:6.3303, ner train2:0.4105, rc_train2:0.3806, info:0.0003
[73] ner test1:2.8435, rc test1:6.9020, ner test2:2.9126, rc test2:1.9986, info:0.0003
[73] precision:0.8008, recall:0.8206, f1:0.8106
[74] ner train1:1.5087, rc_train1:6.3403, ner train2:0.4033, rc_train2:0.3830, info:0.0003
[74] ner test1:2.8400, rc test1:6.9196, ner test2:2.8725, rc test2:2.0474, info:0.0003
[74] precision:0.8212, recall:0.8242, f1:0.8227
[75] ner train1:1.5069, rc_train1:6.2721, ner train2:0.4018, rc_train2:0.3744, info:0.0003
[75] ner test1:2.8334, rc test1:6.6716, ner test2:2.9127, rc test2:2.0504, info:0.0003
[75] precision:0.8137, recall:0.8301, f1:0.8218
[76] ner train1:1.5073, rc_train1:6.2590, ner train2:0.4058, rc_train2:0.3835, info:0.0006
[76] ner test1:2.8550, rc test1:7.1218, ner test2:2.9305, rc test2:2.1057, info:0.0003
[76] precision:0.8258, recall:0.8239, f1:0.8248
[77] ner train1:1.5005, rc_train1:6.2693, ner train2:0.3847, rc_train2:0.3524, info:0.0006
[77] ner test1:2.7875, rc test1:6.7845, ner test2:2.9020, rc test2:2.0500, info:0.0003
[77] precision:0.8020, recall:0.8255, f1:0.8136
[78] ner train1:1.4916, rc_train1:6.2772, ner train2:0.3804, rc_train2:0.3596, info:0.0003
[78] ner test1:2.7920, rc test1:6.7644, ner test2:2.9964, rc test2:2.0722, info:0.0003
[78] precision:0.8032, recall:0.8300, f1:0.8164
[79] ner train1:1.4938, rc_train1:6.2275, ner train2:0.3891, rc_train2:0.3652, info:0.0003
[79] ner test1:2.8328, rc test1:7.0097, ner test2:2.9602, rc test2:2.0823, info:0.0003
[79] precision:0.8145, recall:0.8133, f1:0.8139
[80] ner train1:1.4743, rc_train1:6.2405, ner train2:0.3800, rc_train2:0.3657, info:0.0003
[80] ner test1:2.8149, rc test1:6.7709, ner test2:3.0538, rc test2:2.1185, info:0.0003
[80] precision:0.8022, recall:0.8296, f1:0.8157
[81] ner train1:1.4726, rc_train1:6.1836, ner train2:0.3779, rc_train2:0.3492, info:0.0003
[81] ner test1:2.8790, rc test1:6.6099, ner test2:2.9641, rc test2:2.1090, info:0.0004
[81] precision:0.8033, recall:0.8281, f1:0.8155
[82] ner train1:1.4797, rc_train1:6.2351, ner train2:0.3789, rc_train2:0.3545, info:0.0007
[82] ner test1:2.8461, rc test1:6.8830, ner test2:2.9649, rc test2:2.0958, info:0.0003
[82] precision:0.8120, recall:0.8247, f1:0.8183
[83] ner train1:1.4740, rc_train1:6.2313, ner train2:0.3661, rc_train2:0.3481, info:0.0004
[83] ner test1:2.8452, rc test1:6.7850, ner test2:2.9985, rc test2:2.1124, info:0.0003
[83] precision:0.8052, recall:0.8106, f1:0.8079
[84] ner train1:1.4621, rc_train1:6.1679, ner train2:0.3627, rc_train2:0.3532, info:0.0003
[84] ner test1:2.8411, rc test1:6.8200, ner test2:3.0256, rc test2:2.1103, info:0.0003
[84] precision:0.8004, recall:0.8223, f1:0.8112
[85] ner train1:1.4624, rc_train1:6.0775, ner train2:0.3600, rc_train2:0.3386, info:0.0003
[85] ner test1:2.8700, rc test1:6.6938, ner test2:3.1077, rc test2:2.1973, info:0.0050
[85] precision:0.8187, recall:0.8253, f1:0.8220
[86] ner train1:1.4545, rc_train1:6.0653, ner train2:0.3599, rc_train2:0.3509, info:0.0004
[86] ner test1:2.8584, rc test1:6.7364, ner test2:3.0969, rc test2:2.1728, info:0.0003
[86] precision:0.8200, recall:0.8323, f1:0.8261
[87] ner train1:1.4548, rc_train1:6.1800, ner train2:0.3531, rc_train2:0.3399, info:0.0004
[87] ner test1:2.8843, rc test1:6.4924, ner test2:3.1400, rc test2:2.1535, info:0.0002
[87] precision:0.8055, recall:0.8407, f1:0.8227
[88] ner train1:1.4431, rc_train1:6.0546, ner train2:0.3548, rc_train2:0.3361, info:0.0003
[88] ner test1:2.8528, rc test1:6.7483, ner test2:3.0708, rc test2:2.2170, info:0.0002
[88] precision:0.8174, recall:0.8202, f1:0.8188
[89] ner train1:1.4441, rc_train1:6.1228, ner train2:0.3445, rc_train2:0.3312, info:0.0003
[89] ner test1:2.8920, rc test1:6.7362, ner test2:3.1937, rc test2:2.2039, info:0.0003
[89] precision:0.8059, recall:0.8330, f1:0.8193
[90] ner train1:1.4381, rc_train1:6.0808, ner train2:0.3460, rc_train2:0.3331, info:0.0003
[90] ner test1:2.9074, rc test1:6.6059, ner test2:3.1323, rc test2:2.2323, info:0.0003
[90] precision:0.8209, recall:0.8123, f1:0.8166
[91] ner train1:1.4346, rc_train1:5.9670, ner train2:0.3439, rc_train2:0.3348, info:0.0003
[91] ner test1:2.9030, rc test1:6.5990, ner test2:3.1993, rc test2:2.1720, info:0.0002
[91] precision:0.8042, recall:0.8392, f1:0.8213
[92] ner train1:1.4395, rc_train1:5.9883, ner train2:0.3488, rc_train2:0.3367, info:0.0003
[92] ner test1:2.8828, rc test1:6.5738, ner test2:3.0592, rc test2:2.2213, info:0.0003
[92] precision:0.8005, recall:0.8195, f1:0.8099
[93] ner train1:1.4284, rc_train1:5.9960, ner train2:0.3384, rc_train2:0.3372, info:0.0003
[93] ner test1:2.8506, rc test1:6.4963, ner test2:3.1328, rc test2:2.2021, info:0.0003
[93] precision:0.7927, recall:0.8305, f1:0.8112
[94] ner train1:1.4197, rc_train1:6.0297, ner train2:0.3319, rc_train2:0.3275, info:0.0003
[94] ner test1:2.9212, rc test1:6.5431, ner test2:3.2488, rc test2:2.1897, info:0.0003
[94] precision:0.8145, recall:0.8261, f1:0.8203
[95] ner train1:1.4263, rc_train1:5.9425, ner train2:0.3287, rc_train2:0.3193, info:0.0003
[95] ner test1:2.8670, rc test1:6.5793, ner test2:3.1773, rc test2:2.2236, info:0.0002
[95] precision:0.8273, recall:0.8152, f1:0.8212
[96] ner train1:1.4177, rc_train1:5.9353, ner train2:0.3281, rc_train2:0.3182, info:0.0003
[96] ner test1:2.8686, rc test1:6.4609, ner test2:3.1006, rc test2:2.1895, info:0.0003
[96] precision:0.8119, recall:0.8171, f1:0.8145
[97] ner train1:1.4120, rc_train1:5.9400, ner train2:0.3212, rc_train2:0.3107, info:0.0003
[97] ner test1:2.8708, rc test1:6.5283, ner test2:3.1936, rc test2:2.2421, info:0.0003
[97] precision:0.8087, recall:0.8316, f1:0.8200
[98] ner train1:1.4023, rc_train1:5.8723, ner train2:0.3100, rc_train2:0.3104, info:0.0003
[98] ner test1:2.8557, rc test1:6.3597, ner test2:3.0607, rc test2:2.1616, info:0.0002
[98] precision:0.8012, recall:0.8250, f1:0.8130
[99] ner train1:1.4172, rc_train1:5.8949, ner train2:0.3208, rc_train2:0.3068, info:0.0003
[99] ner test1:2.8682, rc test1:6.5260, ner test2:3.1862, rc test2:2.2254, info:0.0003
[99] precision:0.8122, recall:0.8210, f1:0.8166
[100] ner train1:1.4025, rc_train1:5.8239, ner train2:0.3101, rc_train2:0.3143, info:0.0003
[100] ner test1:2.9231, rc test1:6.4442, ner test2:3.2731, rc test2:2.2417, info:0.0003
[100] precision:0.8086, recall:0.8300, f1:0.8192