generated_from_trainer

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8_roberta-large_train_korquad-1_aihub10_final

This model is a fine-tuned version of klue/roberta-large on the None dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training results

Training Loss Epoch Step Exact Match F1 Validation Loss
5.2079 0.09 1000 16.8859 22.6297 3.7930
1.6052 0.18 2000 69.8685 80.7048 1.0508
0.981 0.27 3000 74.8904 85.0342 0.8213
0.8183 0.36 4000 77.0028 86.6159 0.6270
0.6779 0.45 5000 77.8663 87.1009 0.6006
0.6535 0.54 6000 78.4775 87.4922 0.5645
0.6171 0.63 7000 78.9159 87.7604 0.5454
0.5953 0.72 8000 79.2082 87.9666 0.5474
0.5967 0.81 9000 79.1949 88.0747 0.5449
0.6081 0.9 10000 79.6067 88.4190 0.5483
0.5652 0.99 11000 79.8857 88.4750 0.5527
0.5434 1.08 12000 79.5005 88.3638 0.5483
0.5618 1.17 13000 79.8592 88.6312 0.5396
0.5301 1.26 14000 80.2312 88.7555 0.5229
0.5335 1.35 15000 80.4172 88.9265 0.5171
0.5 1.44 16000 80.4172 89.0081 0.5107
0.5207 1.53 17000 80.6563 89.1881 0.5117
0.4872 1.62 18000 80.5899 88.9920 0.5259
0.5139 1.71 19000 80.4703 88.8763 0.5186
0.5104 1.8 20000 80.2976 88.8165 0.5151
0.4917 1.89 21000 80.6032 88.9459 0.5312
0.5142 1.98 22000 80.6696 88.9736 0.5181
0.4655 2.07 23000 80.9619 89.1750 0.5112
0.4731 2.16 24000 81.0947 89.2199 0.4971
0.4563 2.25 25000 81.0416 89.1501 0.5015
0.4447 2.34 26000 81.1080 89.3572 0.5049
0.4751 2.43 27000 81.1612 89.2913 0.5020
0.4647 2.52 28000 81.2010 89.3781 0.4971
0.4672 2.61 29000 80.9220 89.1665 0.5073
0.4628 2.7 30000 81.2807 89.5747 0.5156
0.4397 2.79 31000 81.4269 89.5239 0.5098
0.4561 2.88 32000 81.5597 89.5456 0.4966
0.4449 2.97 33000 81.5730 89.6600 0.4973
0.435 3.06 34000 81.2143 89.4132 0.5020
0.4271 3.15 35000 81.5464 89.6768 0.4956
0.43 3.24 36000 81.5597 89.6178 0.5220
0.4348 3.33 37000 81.3737 89.5019 0.5107
0.4215 3.42 38000 81.5730 89.6766 0.4922
0.4287 3.51 39000 81.6660 89.7750 0.5166
0.4235 3.6 40000 81.5996 89.6392 0.4932
0.4314 3.69 41000 81.5996 89.7113 0.4995
0.4326 3.77 42000 81.5863 89.7004 0.4949
0.4175 3.86 43000 81.7324 89.6672 0.4966
0.4327 3.95 44000 81.5066 89.7201 0.4883
0.4266 4.04 45000 81.6660 89.8173 0.5186
0.4145 4.13 46000 81.2940 89.6006 0.4966
0.3995 4.22 47000 81.7191 89.6950 0.4963
0.3853 4.31 48000 81.8387 89.7038 0.5078
0.3893 4.4 49000 81.8254 89.7305 0.4932
0.3977 4.49 50000 81.7457 89.8541 0.5044
0.389 4.58 51000 81.7324 89.7325 0.5322
0.3921 4.67 52000 81.8387 89.8072 0.5122
0.4008 4.76 53000 81.6660 89.6847 0.5137
0.4053 4.85 54000 81.7723 89.8425 0.4932
0.3982 4.94 55000 82.0779 90.0053 0.5020
0.4107 5.03 56000 82.0114 89.9584 0.4983
0.3549 5.12 57000 82.1576 90.0642 0.5098
0.3996 5.21 58000 82.1974 90.1950 0.4907
0.3809 5.3 59000 82.0114 89.8741 0.5078
0.3706 5.39 60000 82.0646 89.9369 0.5249
0.3897 5.48 61000 81.9583 89.9503 0.5063
0.376 5.57 62000 81.9981 89.8234 0.5229
0.3666 5.66 63000 81.9317 89.9506 0.5332
0.3568 5.75 64000 81.9981 89.9200 0.5244
0.3677 5.84 65000 82.1443 89.9477 0.5259
0.3734 5.93 66000 82.5030 90.1477 0.5220
0.3622 6.02 67000 82.1177 90.0190 0.5557
0.3671 6.11 68000 81.8387 90.0083 0.5200
0.3563 6.2 69000 81.9849 89.9346 0.5405
0.3773 6.29 70000 81.8387 89.7463 0.4966
0.3662 6.38 71000 81.9716 89.9233 0.5225
0.3461 6.47 72000 82.1177 89.9910 0.5259
0.3412 6.56 73000 82.2240 90.1485 0.5234
0.3695 6.65 74000 82.1177 89.9749 0.5166
0.3462 6.74 75000 81.9184 89.9214 0.5234
0.3696 6.83 76000 82.3568 90.1955 0.5220
0.3651 6.92 77000 81.8520 90.0530 0.5215
0.3595 7.01 78000 82.1443 90.0629 0.5186
0.3284 7.1 79000 82.4100 90.0797 0.5601
0.3288 7.19 80000 82.2506 90.1146 0.5518
0.3268 7.28 81000 82.1841 90.1520 0.5557
0.3291 7.37 82000 82.0779 90.0713 0.5557
0.3355 7.46 83000 82.2373 90.1808 0.5483
0.3382 7.55 84000 82.1576 89.9746 0.5361
0.3377 7.64 85000 82.3303 90.0823 0.5459
0.3614 7.73 86000 82.2373 90.0396 0.5093
0.3522 7.82 87000 82.2107 90.0806 0.5225
0.326 7.91 88000 81.9583 89.9022 0.5400
0.3395 8.0 89000 82.3436 90.1668 0.5249
0.3279 8.09 90000 82.1974 90.0841 0.5278
0.3129 8.18 91000 81.9051 89.9340 0.5474
0.3304 8.27 92000 82.2240 90.0328 0.5547
0.3208 8.36 93000 82.2373 90.1196 0.5566
0.3282 8.45 94000 82.1974 89.9974 0.5591
0.324 8.54 95000 82.2506 90.0156 0.5737
0.3314 8.63 96000 82.1841 90.0330 0.5503
0.3279 8.72 97000 82.4233 90.1501 0.5625
0.3106 8.81 98000 82.1841 90.0287 0.5889
0.3384 8.9 99000 82.1974 89.9639 0.5513
0.3298 8.99 100000 82.2506 90.0830 0.5752
0.3021 9.08 101000 82.1044 90.0205 0.5850
0.3079 9.17 102000 82.2639 90.1064 0.5840
0.2917 9.26 103000 82.2107 90.0571 0.5859
0.3225 9.35 104000 82.4366 90.2246 0.5654
0.2892 9.44 105000 82.2639 90.1516 0.5898
0.3063 9.53 106000 82.4233 90.1390 0.5879
0.3045 9.62 107000 82.2506 90.0909 0.5938
0.3058 9.71 108000 82.3037 90.0736 0.6152
0.2946 9.8 109000 82.1974 90.0984 0.5742
0.3143 9.89 110000 81.8919 89.8635 0.5840
0.3078 9.98 111000 82.1177 89.9754 0.5859
0.2861 10.07 112000 82.1443 89.9743 0.6001
0.2902 10.16 113000 82.1576 89.9842 0.6035
0.2766 10.25 114000 82.2771 90.1896 0.6001
0.3008 10.34 115000 82.2904 90.1552 0.6001
0.2898 10.43 116000 82.0779 90.0833 0.6162
0.2916 10.52 117000 81.9450 89.8734 0.6318
0.2964 10.61 118000 82.1044 89.9503 0.5957
0.2821 10.7 119000 82.2107 90.0871 0.5908
0.2883 10.79 120000 82.3568 90.1520 0.5947
0.2929 10.88 121000 82.2639 90.0949 0.5786
0.2939 10.97 122000 82.0779 90.0528 0.5542
0.2705 11.06 123000 82.1841 89.9515 0.6396
0.2618 11.15 124000 82.1709 90.0294 0.6494
0.2762 11.23 125000 82.1841 89.9757 0.6289
0.2781 11.32 126000 82.2639 90.1137 0.6274
0.276 11.41 127000 82.4897 90.2755 0.6245
0.2828 11.5 128000 82.2107 90.0683 0.6147
0.285 11.59 129000 82.1974 89.9957 0.6240
0.271 11.68 130000 82.3037 90.0343 0.6279
0.2925 11.77 131000 82.0779 89.9050 0.6260
0.2824 11.86 132000 82.0911 90.0220 0.6416
0.3018 11.95 133000 82.3436 89.9673 0.6294
0.2762 12.04 134000 82.2506 90.1139 0.6479
0.246 12.13 135000 82.2240 90.0564 0.6738
0.2612 12.22 136000 82.2107 90.0322 0.6680
0.2491 12.31 137000 82.0513 89.8680 0.6592
0.2648 12.4 138000 82.0513 90.0025 0.6338
0.2596 12.49 139000 82.2373 90.1338 0.6558
0.2696 12.58 140000 82.2107 90.1526 0.6523
0.2778 12.67 141000 82.3303 90.1032 0.6650
0.2784 12.76 142000 82.0114 89.9141 0.6289
0.2639 12.85 143000 82.0114 89.8360 0.6680
0.2584 12.94 144000 82.1841 89.9868 0.6484
0.2271 13.03 145000 82.0114 89.9543 0.6748
0.2373 13.12 146000 82.1443 90.0159 0.6807
0.2576 13.21 147000 82.2771 90.1674 0.6699
0.2483 13.3 148000 82.2107 89.9238 0.6602
0.2728 13.39 149000 81.8387 89.8637 0.6338
0.2632 13.48 150000 81.8520 89.9056 0.6240
0.2484 13.57 151000 82.1310 89.9535 0.6768
0.2428 13.66 152000 82.2240 90.1379 0.6812
0.255 13.75 153000 82.2639 90.0505 0.6836
0.2632 13.84 154000 82.1841 90.0297 0.6680
0.2366 13.93 155000 82.0380 89.9797 0.6953
0.2571 14.02 156000 82.2373 90.0496 0.6763
0.2332 14.11 157000 82.1974 89.9976 0.7007
0.2333 14.2 158000 82.1709 90.1054 0.7109
0.245 14.29 159000 82.0114 90.0034 0.6748
0.2281 14.38 160000 82.1576 90.0718 0.7031
0.2323 14.47 161000 82.0380 90.0130 0.7012
0.2264 14.56 162000 82.1310 90.0605 0.7090
0.2365 14.65 163000 81.9317 89.7701 0.7334
0.2421 14.74 164000 82.1443 89.9842 0.6992
0.2397 14.83 165000 82.1841 89.9864 0.7256
0.2607 14.92 166000 82.0646 89.8532 0.6855

Framework versions