generated_from_trainer

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TSE_XLNET_5E

This model is a fine-tuned version of xlnet-base-cased on an unknown 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 Validation Loss Accuracy
0.6717 0.06 50 0.4377 0.8533
0.3989 0.12 100 0.4525 0.84
0.3433 0.17 150 0.3348 0.9133
0.2646 0.23 200 0.3722 0.9
0.3052 0.29 250 0.3306 0.8933
0.2583 0.35 300 0.3129 0.92
0.2712 0.4 350 0.3147 0.9
0.2708 0.46 400 0.2680 0.9
0.2443 0.52 450 0.2261 0.9133
0.2463 0.58 500 0.2583 0.9067
0.2525 0.63 550 0.2719 0.92
0.2522 0.69 600 0.3905 0.8933
0.2078 0.75 650 0.2674 0.9133
0.264 0.81 700 0.2774 0.9133
0.211 0.87 750 0.2652 0.9333
0.286 0.92 800 0.1777 0.94
0.2341 0.98 850 0.2570 0.9133
0.1797 1.04 900 0.3162 0.92
0.1831 1.1 950 0.3205 0.92
0.2006 1.15 1000 0.3173 0.9133
0.1555 1.21 1050 0.3388 0.9267
0.1712 1.27 1100 0.3968 0.92
0.1488 1.33 1150 0.4167 0.9133
0.1893 1.38 1200 0.3269 0.9267
0.1543 1.44 1250 0.3797 0.9133
0.1825 1.5 1300 0.2203 0.94
0.1841 1.56 1350 0.2744 0.9133
0.1523 1.61 1400 0.3561 0.9067
0.1914 1.67 1450 0.2859 0.9067
0.1742 1.73 1500 0.2461 0.9267
0.145 1.79 1550 0.4266 0.9133
0.208 1.85 1600 0.3470 0.9067
0.147 1.9 1650 0.4521 0.9133
0.1867 1.96 1700 0.3648 0.9067
0.182 2.02 1750 0.2659 0.9333
0.1079 2.08 1800 0.3393 0.92
0.1338 2.13 1850 0.3483 0.9267
0.1181 2.19 1900 0.4384 0.92
0.1418 2.25 1950 0.3468 0.9267
0.0953 2.31 2000 0.4008 0.9267
0.1313 2.36 2050 0.3301 0.9333
0.0499 2.42 2100 0.4018 0.92
0.1197 2.48 2150 0.3394 0.9267
0.1237 2.54 2200 0.3399 0.92
0.0766 2.6 2250 0.3947 0.9267
0.1142 2.65 2300 0.4055 0.9133
0.1362 2.71 2350 0.2599 0.9333
0.1332 2.77 2400 0.3293 0.9133
0.1241 2.83 2450 0.3717 0.92
0.0696 2.88 2500 0.4440 0.92
0.1012 2.94 2550 0.4026 0.92
0.1028 3.0 2600 0.4202 0.9133
0.0551 3.06 2650 0.4649 0.9133
0.0796 3.11 2700 0.4053 0.92
0.0786 3.17 2750 0.4862 0.9067
0.0843 3.23 2800 0.4007 0.9267
0.0502 3.29 2850 0.4510 0.92
0.0726 3.34 2900 0.4171 0.9267
0.0933 3.4 2950 0.3485 0.9333
0.0624 3.46 3000 0.4442 0.9133
0.0475 3.52 3050 0.4449 0.92
0.0498 3.58 3100 0.4147 0.9267
0.1101 3.63 3150 0.3484 0.9333
0.0785 3.69 3200 0.3630 0.9267
0.075 3.75 3250 0.4267 0.92
0.0709 3.81 3300 0.3638 0.9267
0.0754 3.86 3350 0.3890 0.9333
0.1038 3.92 3400 0.3910 0.9267
0.0274 3.98 3450 0.4246 0.9267
0.0723 4.04 3500 0.3847 0.9267
0.015 4.09 3550 0.4134 0.9333
0.0329 4.15 3600 0.4136 0.9333
0.0619 4.21 3650 0.4048 0.9333
0.0505 4.27 3700 0.4228 0.9267
0.0523 4.33 3750 0.4139 0.9267
0.0365 4.38 3800 0.4067 0.9267
0.0434 4.44 3850 0.4132 0.9333
0.0262 4.5 3900 0.4245 0.9333
0.0534 4.56 3950 0.4217 0.9333
0.0186 4.61 4000 0.4282 0.9333
0.0548 4.67 4050 0.4255 0.9333
0.0146 4.73 4100 0.4368 0.9333
0.0442 4.79 4150 0.4470 0.9333
0.0431 4.84 4200 0.4469 0.9333
0.0297 4.9 4250 0.4470 0.9333
0.0601 4.96 4300 0.4463 0.9333

Framework versions