random3_n9_partial

[1] ner train1:8.1027, rc_train1:1572.3087, ner train2:5.1172, rc_train2:338.4254, info:0.0001
[1] ner test1:3.9077, rc test1:10.7615, ner test2:3.1130, rc test2:3.2355, info:0.0000
[1] precision:0.6419, recall:0.6249, f1:0.6333
[*] saved best model!
[2] ner train1:3.7722, rc_train1:10.9430, ner train2:2.9753, rc_train2:2.9655, info:0.0000
[2] ner test1:3.4384, rc test1:10.2412, ner test2:2.6697, rc test2:2.5000, info:0.0000
[2] precision:0.7279, recall:0.6885, f1:0.7077
[*] saved best model!
[3] ner train1:3.3744, rc_train1:10.5494, ner train2:2.5750, rc_train2:2.4425, info:0.0000
[3] ner test1:3.2125, rc test1:10.4447, ner test2:2.3884, rc test2:2.1654, info:0.0000
[3] precision:0.7838, recall:0.7117, f1:0.7460
[*] saved best model!
[4] ner train1:3.1771, rc_train1:10.3261, ner train2:2.3267, rc_train2:2.1433, info:0.0000
[4] ner test1:3.1026, rc test1:10.6008, ner test2:2.2613, rc test2:2.0896, info:0.0000
[4] precision:0.7526, recall:0.7855, f1:0.7687
[*] saved best model!
[5] ner train1:3.0372, rc_train1:9.8100, ner train2:2.1503, rc_train2:1.9606, info:0.0000
[5] ner test1:3.1124, rc test1:10.8635, ner test2:2.1555, rc test2:1.9048, info:0.0000
[5] precision:0.8558, recall:0.7074, f1:0.7745
[*] saved best model!
[6] ner train1:2.9141, rc_train1:9.3899, ner train2:2.0097, rc_train2:1.8178, info:0.0000
[6] ner test1:2.9965, rc test1:9.7990, ner test2:2.1232, rc test2:1.8229, info:0.0001
[6] precision:0.8258, recall:0.7570, f1:0.7899
[*] saved best model!
[7] ner train1:2.8373, rc_train1:9.1278, ner train2:1.8961, rc_train2:1.6972, info:0.0000
[7] ner test1:2.9543, rc test1:9.8475, ner test2:2.0891, rc test2:1.7765, info:0.0000
[7] precision:0.8231, recall:0.7746, f1:0.7981
[*] saved best model!
[8] ner train1:2.7540, rc_train1:9.0899, ner train2:1.7867, rc_train2:1.5994, info:0.0000
[8] ner test1:2.9492, rc test1:9.6963, ner test2:2.0683, rc test2:1.7243, info:0.0000
[8] precision:0.8204, recall:0.7932, f1:0.8066
[*] saved best model!
[9] ner train1:2.6654, rc_train1:8.8747, ner train2:1.6995, rc_train2:1.5124, info:0.0000
[9] ner test1:2.9247, rc test1:9.1974, ner test2:2.0512, rc test2:1.6768, info:0.0000
[9] precision:0.8259, recall:0.8015, f1:0.8135
[*] saved best model!
[10] ner train1:2.6196, rc_train1:8.8462, ner train2:1.6223, rc_train2:1.4273, info:0.0000
[10] ner test1:2.8862, rc test1:9.2337, ner test2:2.0287, rc test2:1.6638, info:0.0000
[10] precision:0.8133, recall:0.8224, f1:0.8179
[*] saved best model!
[11] ner train1:2.5635, rc_train1:8.8059, ner train2:1.5452, rc_train2:1.3514, info:0.0000
[11] ner test1:2.9098, rc test1:9.3543, ner test2:2.0398, rc test2:1.6424, info:0.0000
[11] precision:0.8343, recall:0.8142, f1:0.8241
[*] saved best model!
[12] ner train1:2.5252, rc_train1:8.9059, ner train2:1.4773, rc_train2:1.2860, info:0.0000
[12] ner test1:2.9021, rc test1:9.7104, ner test2:2.0023, rc test2:1.5740, info:0.0000
[12] precision:0.8440, recall:0.8179, f1:0.8307
[*] saved best model!
[13] ner train1:2.4852, rc_train1:8.8750, ner train2:1.4254, rc_train2:1.2354, info:0.0000
[13] ner test1:2.8977, rc test1:9.7934, ner test2:2.0156, rc test2:1.5657, info:0.0000
[13] precision:0.8404, recall:0.8312, f1:0.8358
[*] saved best model!
[14] ner train1:2.4403, rc_train1:8.7910, ner train2:1.3639, rc_train2:1.1680, info:0.0000
[14] ner test1:2.8735, rc test1:9.4084, ner test2:2.0511, rc test2:1.6007, info:0.0000
[14] precision:0.8268, recall:0.8472, f1:0.8369
[*] saved best model!
[15] ner train1:2.4180, rc_train1:8.7466, ner train2:1.3306, rc_train2:1.1303, info:0.0000
[15] ner test1:2.8784, rc test1:9.2731, ner test2:2.0256, rc test2:1.5559, info:0.0000
[15] precision:0.8501, recall:0.8318, f1:0.8408
[*] saved best model!
[16] ner train1:2.3782, rc_train1:8.8231, ner train2:1.2712, rc_train2:1.0770, info:0.0001
[16] ner test1:2.9041, rc test1:9.6298, ner test2:2.1048, rc test2:1.5526, info:0.0000
[16] precision:0.8677, recall:0.8202, f1:0.8433
[*] saved best model!
[17] ner train1:2.3543, rc_train1:8.6978, ner train2:1.2268, rc_train2:1.0296, info:0.0000
[17] ner test1:2.8651, rc test1:9.1366, ner test2:2.1156, rc test2:1.5810, info:0.0000
[17] precision:0.8379, recall:0.8492, f1:0.8435
[*] saved best model!
[18] ner train1:2.3197, rc_train1:8.7839, ner train2:1.1857, rc_train2:1.0042, info:0.0001
[18] ner test1:2.8667, rc test1:9.0669, ner test2:2.1160, rc test2:1.5875, info:0.0001
[18] precision:0.8515, recall:0.8404, f1:0.8459
[*] saved best model!
[19] ner train1:2.2909, rc_train1:8.7288, ner train2:1.1562, rc_train2:0.9677, info:0.0001
[19] ner test1:2.8725, rc test1:9.2049, ner test2:2.0715, rc test2:1.5109, info:0.0000
[19] precision:0.8580, recall:0.8420, f1:0.8500
[*] saved best model!
[20] ner train1:2.2745, rc_train1:8.5686, ner train2:1.1194, rc_train2:0.9338, info:0.0000
[20] ner test1:2.8692, rc test1:8.8690, ner test2:2.1366, rc test2:1.5522, info:0.0000
[20] precision:0.8383, recall:0.8596, f1:0.8488
[21] ner train1:2.2475, rc_train1:8.4915, ner train2:1.0724, rc_train2:0.8936, info:0.0000
[21] ner test1:2.8734, rc test1:8.9069, ner test2:2.1148, rc test2:1.5500, info:0.0000
[21] precision:0.8677, recall:0.8482, f1:0.8578
[*] saved best model!
[22] ner train1:2.2282, rc_train1:8.4875, ner train2:1.0589, rc_train2:0.8896, info:0.0000
[22] ner test1:2.9060, rc test1:8.6981, ner test2:2.1910, rc test2:1.5624, info:0.0000
[22] precision:0.8371, recall:0.8647, f1:0.8507
[23] ner train1:2.2036, rc_train1:8.4671, ner train2:1.0157, rc_train2:0.8487, info:0.0001
[23] ner test1:2.8817, rc test1:9.1477, ner test2:2.1654, rc test2:1.5447, info:0.0000
[23] precision:0.8734, recall:0.8376, f1:0.8551
[24] ner train1:2.1860, rc_train1:8.4981, ner train2:0.9896, rc_train2:0.8226, info:0.0000
[24] ner test1:2.8966, rc test1:9.3420, ner test2:2.2453, rc test2:1.6131, info:0.0000
[24] precision:0.8644, recall:0.8396, f1:0.8518
[25] ner train1:2.1633, rc_train1:8.4093, ner train2:0.9612, rc_train2:0.7996, info:0.0000
[25] ner test1:2.8895, rc test1:8.9201, ner test2:2.2418, rc test2:1.5759, info:0.0000
[25] precision:0.8624, recall:0.8519, f1:0.8571
[26] ner train1:2.1577, rc_train1:8.4141, ner train2:0.9354, rc_train2:0.7766, info:0.0000
[26] ner test1:2.9115, rc test1:8.8500, ner test2:2.3061, rc test2:1.5753, info:0.0000
[26] precision:0.8484, recall:0.8610, f1:0.8547
[27] ner train1:2.1392, rc_train1:8.3791, ner train2:0.8991, rc_train2:0.7453, info:0.0001
[27] ner test1:2.8784, rc test1:8.7560, ner test2:2.2214, rc test2:1.6011, info:0.0001
[27] precision:0.8658, recall:0.8531, f1:0.8594
[*] saved best model!
[28] ner train1:2.1180, rc_train1:8.2914, ner train2:0.8798, rc_train2:0.7449, info:0.0001
[28] ner test1:2.8710, rc test1:8.4875, ner test2:2.2959, rc test2:1.5857, info:0.0001
[28] precision:0.8538, recall:0.8581, f1:0.8559
[29] ner train1:2.1067, rc_train1:8.3113, ner train2:0.8588, rc_train2:0.7215, info:0.0000
[29] ner test1:2.8943, rc test1:8.5786, ner test2:2.2900, rc test2:1.6082, info:0.0000
[29] precision:0.8784, recall:0.8470, f1:0.8624
[*] saved best model!
[30] ner train1:2.0842, rc_train1:8.2624, ner train2:0.8324, rc_train2:0.7078, info:0.0000
[30] ner test1:2.9066, rc test1:8.8342, ner test2:2.3754, rc test2:1.6298, info:0.0001
[30] precision:0.8568, recall:0.8613, f1:0.8591
[31] ner train1:2.0843, rc_train1:8.2592, ner train2:0.8158, rc_train2:0.6895, info:0.0000
[31] ner test1:2.8888, rc test1:8.7288, ner test2:2.3191, rc test2:1.6483, info:0.0000
[31] precision:0.8617, recall:0.8582, f1:0.8599
[32] ner train1:2.0699, rc_train1:8.1512, ner train2:0.7988, rc_train2:0.6776, info:0.0000
[32] ner test1:2.9121, rc test1:8.6088, ner test2:2.3061, rc test2:1.6377, info:0.0000
[32] precision:0.8822, recall:0.8407, f1:0.8610
[33] ner train1:2.0556, rc_train1:8.1084, ner train2:0.7742, rc_train2:0.6578, info:0.0000
[33] ner test1:2.9316, rc test1:8.6858, ner test2:2.3639, rc test2:1.6455, info:0.0000
[33] precision:0.8656, recall:0.8559, f1:0.8607
[34] ner train1:2.0332, rc_train1:8.0528, ner train2:0.7612, rc_train2:0.6497, info:0.0000
[34] ner test1:2.8858, rc test1:8.7786, ner test2:2.3633, rc test2:1.6875, info:0.0000
[34] precision:0.8593, recall:0.8599, f1:0.8596
[35] ner train1:2.0226, rc_train1:8.1127, ner train2:0.7445, rc_train2:0.6259, info:0.0000
[35] ner test1:2.9228, rc test1:8.6932, ner test2:2.4427, rc test2:1.6695, info:0.0000
[35] precision:0.8613, recall:0.8594, f1:0.8604
[36] ner train1:2.0105, rc_train1:7.9964, ner train2:0.7229, rc_train2:0.6158, info:0.0000
[36] ner test1:2.9213, rc test1:8.7440, ner test2:2.4211, rc test2:1.6926, info:0.0000
[36] precision:0.8682, recall:0.8592, f1:0.8637
[*] saved best model!
[37] ner train1:1.9985, rc_train1:7.9781, ner train2:0.7085, rc_train2:0.6097, info:0.0000
[37] ner test1:2.8871, rc test1:8.2613, ner test2:2.4796, rc test2:1.7239, info:0.0000
[37] precision:0.8534, recall:0.8695, f1:0.8614
[38] ner train1:1.9899, rc_train1:8.0049, ner train2:0.6914, rc_train2:0.5912, info:0.0000
[38] ner test1:2.8973, rc test1:8.7923, ner test2:2.3978, rc test2:1.6865, info:0.0001
[38] precision:0.8749, recall:0.8540, f1:0.8643
[*] saved best model!
[39] ner train1:1.9684, rc_train1:7.9004, ner train2:0.6771, rc_train2:0.5865, info:0.0000
[39] ner test1:2.9102, rc test1:8.4852, ner test2:2.4342, rc test2:1.7234, info:0.0000
[39] precision:0.8670, recall:0.8575, f1:0.8622
[40] ner train1:1.9605, rc_train1:7.8271, ner train2:0.6536, rc_train2:0.5644, info:0.0000
[40] ner test1:2.8999, rc test1:8.3218, ner test2:2.5061, rc test2:1.7682, info:0.0000
[40] precision:0.8605, recall:0.8620, f1:0.8613
[41] ner train1:1.9611, rc_train1:7.8152, ner train2:0.6579, rc_train2:0.5610, info:0.0000
[41] ner test1:2.9303, rc test1:8.2633, ner test2:2.4629, rc test2:1.7980, info:0.0000
[41] precision:0.8545, recall:0.8621, f1:0.8583
[42] ner train1:1.9490, rc_train1:7.8066, ner train2:0.6420, rc_train2:0.5542, info:0.0000
[42] ner test1:2.9579, rc test1:8.1497, ner test2:2.5434, rc test2:1.7510, info:0.0000
[42] precision:0.8597, recall:0.8674, f1:0.8636
[43] ner train1:1.9357, rc_train1:7.7425, ner train2:0.6235, rc_train2:0.5427, info:0.0000
[43] ner test1:2.9596, rc test1:8.5544, ner test2:2.5858, rc test2:1.7847, info:0.0000
[43] precision:0.8595, recall:0.8744, f1:0.8669
[*] saved best model!
[44] ner train1:1.9236, rc_train1:7.7665, ner train2:0.6196, rc_train2:0.5367, info:0.0000
[44] ner test1:2.9581, rc test1:8.3970, ner test2:2.5296, rc test2:1.7859, info:0.0000
[44] precision:0.8559, recall:0.8687, f1:0.8622
[45] ner train1:1.9184, rc_train1:7.7293, ner train2:0.6034, rc_train2:0.5214, info:0.0000
[45] ner test1:2.9848, rc test1:8.3024, ner test2:2.6272, rc test2:1.8752, info:0.0000
[45] precision:0.8421, recall:0.8764, f1:0.8589
[46] ner train1:1.8999, rc_train1:7.6558, ner train2:0.5926, rc_train2:0.5211, info:0.0000
[46] ner test1:2.9746, rc test1:8.1649, ner test2:2.5441, rc test2:1.8183, info:0.0000
[46] precision:0.8449, recall:0.8707, f1:0.8576
[47] ner train1:1.8967, rc_train1:7.6192, ner train2:0.5725, rc_train2:0.5092, info:0.0000
[47] ner test1:3.0028, rc test1:8.0579, ner test2:2.6142, rc test2:1.7999, info:0.0000
[47] precision:0.8437, recall:0.8753, f1:0.8592
[48] ner train1:1.8905, rc_train1:7.5661, ner train2:0.5755, rc_train2:0.5047, info:0.0000
[48] ner test1:2.9433, rc test1:7.9060, ner test2:2.6060, rc test2:1.8154, info:0.0000
[48] precision:0.8598, recall:0.8707, f1:0.8652
[49] ner train1:1.8788, rc_train1:7.5547, ner train2:0.5577, rc_train2:0.4925, info:0.0000
[49] ner test1:2.9960, rc test1:8.1375, ner test2:2.6079, rc test2:1.8249, info:0.0000
[49] precision:0.8558, recall:0.8778, f1:0.8666
[50] ner train1:1.8782, rc_train1:7.5551, ner train2:0.5500, rc_train2:0.4862, info:0.0000
[50] ner test1:3.0237, rc test1:8.1086, ner test2:2.7151, rc test2:1.8401, info:0.0000
[50] precision:0.8501, recall:0.8768, f1:0.8632
[51] ner train1:1.8721, rc_train1:7.4947, ner train2:0.5431, rc_train2:0.4819, info:0.0000
[51] ner test1:2.9951, rc test1:7.7617, ner test2:2.6706, rc test2:1.8034, info:0.0000
[51] precision:0.8617, recall:0.8714, f1:0.8665
[52] ner train1:1.8629, rc_train1:7.4681, ner train2:0.5301, rc_train2:0.4687, info:0.0000
[52] ner test1:3.0129, rc test1:7.8488, ner test2:2.7010, rc test2:1.8439, info:0.0000
[52] precision:0.8547, recall:0.8744, f1:0.8644
[53] ner train1:1.8516, rc_train1:7.4305, ner train2:0.5246, rc_train2:0.4699, info:0.0000
[53] ner test1:3.0180, rc test1:7.9475, ner test2:2.7055, rc test2:1.8725, info:0.0000
[53] precision:0.8610, recall:0.8748, f1:0.8679
[*] saved best model!
[54] ner train1:1.8302, rc_train1:7.4386, ner train2:0.5096, rc_train2:0.4489, info:0.0000
[54] ner test1:3.0293, rc test1:7.7379, ner test2:2.8443, rc test2:1.8991, info:0.0000
[54] precision:0.8480, recall:0.8772, f1:0.8624
[55] ner train1:1.8266, rc_train1:7.3537, ner train2:0.5092, rc_train2:0.4480, info:0.0000
[55] ner test1:3.0168, rc test1:7.8119, ner test2:2.7590, rc test2:1.9015, info:0.0000
[55] precision:0.8521, recall:0.8761, f1:0.8639
[56] ner train1:1.8148, rc_train1:7.3313, ner train2:0.4991, rc_train2:0.4507, info:0.0000
[56] ner test1:3.0203, rc test1:7.7860, ner test2:2.8225, rc test2:1.9101, info:0.0000
[56] precision:0.8378, recall:0.8787, f1:0.8577
[57] ner train1:1.8210, rc_train1:7.2931, ner train2:0.4911, rc_train2:0.4487, info:0.0000
[57] ner test1:3.0267, rc test1:7.8886, ner test2:2.7604, rc test2:1.8814, info:0.0000
[57] precision:0.8572, recall:0.8704, f1:0.8638
[58] ner train1:1.8103, rc_train1:7.3082, ner train2:0.4991, rc_train2:0.4509, info:0.0000
[58] ner test1:3.0162, rc test1:7.7487, ner test2:2.7222, rc test2:1.8374, info:0.0000
[58] precision:0.8585, recall:0.8745, f1:0.8664
[59] ner train1:1.8002, rc_train1:7.2951, ner train2:0.4799, rc_train2:0.4399, info:0.0000
[59] ner test1:3.0025, rc test1:7.5553, ner test2:2.8202, rc test2:1.9196, info:0.0000
[59] precision:0.8401, recall:0.8799, f1:0.8596
[60] ner train1:1.7856, rc_train1:7.2309, ner train2:0.4681, rc_train2:0.4183, info:0.0000
[60] ner test1:3.0474, rc test1:7.5577, ner test2:2.9184, rc test2:1.9953, info:0.0000
[60] precision:0.8243, recall:0.8878, f1:0.8549
[61] ner train1:1.7846, rc_train1:7.1937, ner train2:0.4653, rc_train2:0.4221, info:0.0000
[61] ner test1:2.9967, rc test1:7.3619, ner test2:2.7915, rc test2:1.9109, info:0.0000
[61] precision:0.8423, recall:0.8797, f1:0.8606
[62] ner train1:1.7822, rc_train1:7.1817, ner train2:0.4584, rc_train2:0.4153, info:0.0000
[62] ner test1:2.9806, rc test1:7.8508, ner test2:2.7634, rc test2:1.9823, info:0.0000
[62] precision:0.8607, recall:0.8687, f1:0.8647
[63] ner train1:1.7763, rc_train1:7.2449, ner train2:0.4539, rc_train2:0.4069, info:0.0000
[63] ner test1:3.0367, rc test1:7.5443, ner test2:2.8107, rc test2:1.9075, info:0.0000
[63] precision:0.8479, recall:0.8806, f1:0.8640
[64] ner train1:1.7667, rc_train1:7.1701, ner train2:0.4417, rc_train2:0.4094, info:0.0000
[64] ner test1:3.0398, rc test1:7.3411, ner test2:2.9261, rc test2:2.0805, info:0.0000
[64] precision:0.8187, recall:0.8906, f1:0.8532
[65] ner train1:1.7846, rc_train1:7.0840, ner train2:0.4516, rc_train2:0.4121, info:0.0000
[65] ner test1:3.0567, rc test1:7.6327, ner test2:2.8384, rc test2:2.0245, info:0.0000
[65] precision:0.8416, recall:0.8789, f1:0.8598
[66] ner train1:1.7598, rc_train1:7.0471, ner train2:0.4365, rc_train2:0.3956, info:0.0000
[66] ner test1:3.0355, rc test1:7.4103, ner test2:2.9043, rc test2:1.9864, info:0.0000
[66] precision:0.8497, recall:0.8764, f1:0.8629
[67] ner train1:1.7556, rc_train1:6.9767, ner train2:0.4384, rc_train2:0.3970, info:0.0000
[67] ner test1:3.0450, rc test1:7.3777, ner test2:2.8726, rc test2:1.9978, info:0.0000
[67] precision:0.8415, recall:0.8745, f1:0.8577
[68] ner train1:1.7332, rc_train1:6.9535, ner train2:0.4217, rc_train2:0.3951, info:0.0000
[68] ner test1:3.0608, rc test1:7.2739, ner test2:2.9865, rc test2:1.9983, info:0.0000
[68] precision:0.8414, recall:0.8827, f1:0.8616
[69] ner train1:1.7367, rc_train1:6.9438, ner train2:0.4240, rc_train2:0.4022, info:0.0000
[69] ner test1:3.0168, rc test1:7.3907, ner test2:2.9106, rc test2:2.0038, info:0.0000
[69] precision:0.8467, recall:0.8811, f1:0.8636
[70] ner train1:1.7368, rc_train1:6.9438, ner train2:0.4174, rc_train2:0.3862, info:0.0000
[70] ner test1:3.0286, rc test1:7.1900, ner test2:2.9995, rc test2:2.0504, info:0.0000
[70] precision:0.8493, recall:0.8787, f1:0.8638
[71] ner train1:1.7262, rc_train1:6.9293, ner train2:0.4095, rc_train2:0.3748, info:0.0000
[71] ner test1:3.0003, rc test1:7.3599, ner test2:2.9761, rc test2:2.0351, info:0.0000
[71] precision:0.8463, recall:0.8831, f1:0.8643
[72] ner train1:1.7135, rc_train1:6.8441, ner train2:0.4070, rc_train2:0.3836, info:0.0000
[72] ner test1:3.0379, rc test1:7.1657, ner test2:2.9594, rc test2:2.0280, info:0.0000
[72] precision:0.8440, recall:0.8816, f1:0.8624
[73] ner train1:1.7203, rc_train1:6.8769, ner train2:0.4043, rc_train2:0.3682, info:0.0000
[73] ner test1:3.0332, rc test1:7.1636, ner test2:3.0472, rc test2:2.0222, info:0.0000
[73] precision:0.8461, recall:0.8795, f1:0.8625
[74] ner train1:1.7146, rc_train1:6.8493, ner train2:0.4001, rc_train2:0.3776, info:0.0000
[74] ner test1:3.0592, rc test1:7.3262, ner test2:3.0273, rc test2:2.0891, info:0.0000
[74] precision:0.8478, recall:0.8777, f1:0.8625
[75] ner train1:1.7073, rc_train1:6.8065, ner train2:0.3956, rc_train2:0.3637, info:0.0000
[75] ner test1:3.0883, rc test1:7.2275, ner test2:2.9452, rc test2:2.0399, info:0.0000
[75] precision:0.8579, recall:0.8763, f1:0.8670
[76] ner train1:1.7090, rc_train1:6.7694, ner train2:0.3933, rc_train2:0.3678, info:0.0000
[76] ner test1:3.0401, rc test1:7.2203, ner test2:2.9665, rc test2:2.0368, info:0.0000
[76] precision:0.8457, recall:0.8804, f1:0.8627
[77] ner train1:1.6947, rc_train1:6.7708, ner train2:0.3854, rc_train2:0.3595, info:0.0000
[77] ner test1:3.0603, rc test1:7.3894, ner test2:3.0725, rc test2:2.0806, info:0.0000
[77] precision:0.8542, recall:0.8752, f1:0.8646
[78] ner train1:1.6904, rc_train1:6.7120, ner train2:0.3852, rc_train2:0.3561, info:0.0000
[78] ner test1:3.0117, rc test1:7.2318, ner test2:2.9657, rc test2:1.9822, info:0.0000
[78] precision:0.8722, recall:0.8670, f1:0.8696
[*] saved best model!
[79] ner train1:1.6829, rc_train1:6.7041, ner train2:0.3809, rc_train2:0.3542, info:0.0000
[79] ner test1:3.0715, rc test1:7.3745, ner test2:3.0723, rc test2:2.1235, info:0.0000
[79] precision:0.8378, recall:0.8838, f1:0.8602