generated_from_trainer

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t5_qg_2

This model is a fine-tuned version of Gayathri142214002/t5_qg_1 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
0.8817 0.03 10 0.8651
0.8338 0.06 20 0.8145
0.9069 0.09 30 0.7292
0.8168 0.12 40 0.6823
0.6639 0.15 50 0.6587
0.6897 0.18 60 0.6266
0.6563 0.21 70 0.6206
0.7409 0.24 80 0.5953
0.7765 0.27 90 0.5762
0.6625 0.3 100 0.5683
0.5672 0.33 110 0.5687
0.5073 0.36 120 0.5722
0.5656 0.39 130 0.5583
0.5908 0.42 140 0.5357
0.6982 0.45 150 0.5139
0.5616 0.48 160 0.4979
0.6185 0.51 170 0.4837
0.5247 0.54 180 0.4845
0.5561 0.57 190 0.4848
0.5342 0.6 200 0.4802
0.4285 0.63 210 0.4893
0.4691 0.66 220 0.4944
0.483 0.69 230 0.4913
0.607 0.72 240 0.4877
0.5613 0.75 250 0.4692
0.5271 0.78 260 0.4566
0.4606 0.81 270 0.4692
0.52 0.84 280 0.4777
0.4936 0.87 290 0.4673
0.5076 0.9 300 0.4637
0.4497 0.93 310 0.4702
0.3881 0.96 320 0.4821
0.4673 0.99 330 0.4641
0.4199 1.02 340 0.4639
0.4399 1.05 350 0.4751
0.4949 1.08 360 0.4808
0.4312 1.11 370 0.4795
0.4369 1.14 380 0.4809
0.4733 1.17 390 0.4661
0.4259 1.2 400 0.4490
0.4345 1.23 410 0.4440
0.4075 1.26 420 0.4519
0.4423 1.29 430 0.4503
0.3973 1.32 440 0.4466
0.3671 1.35 450 0.4481
0.4627 1.38 460 0.4431
0.435 1.41 470 0.4454
0.4073 1.44 480 0.4470
0.4309 1.47 490 0.4398
0.4392 1.5 500 0.4333
0.4372 1.53 510 0.4270
0.4842 1.56 520 0.4329
0.4509 1.59 530 0.4343
0.4836 1.62 540 0.4324
0.3892 1.65 550 0.4187
0.3473 1.68 560 0.4163
0.4382 1.71 570 0.4161
0.365 1.74 580 0.4102
0.4001 1.77 590 0.4122
0.3835 1.8 600 0.4162
0.432 1.83 610 0.4147
0.4152 1.86 620 0.4254
0.4328 1.89 630 0.4319
0.4156 1.92 640 0.4210
0.3748 1.95 650 0.4182
0.449 1.98 660 0.4155
0.4083 2.01 670 0.4145
0.3137 2.04 680 0.4187
0.3838 2.07 690 0.4220
0.4279 2.1 700 0.4105
0.3715 2.13 710 0.4113
0.3207 2.16 720 0.4169
0.3755 2.19 730 0.4207
0.3726 2.22 740 0.4280
0.4083 2.25 750 0.4389
0.3895 2.28 760 0.4378
0.3834 2.31 770 0.4327
0.3968 2.34 780 0.4292
0.3871 2.37 790 0.4223
0.4057 2.4 800 0.4232
0.3633 2.43 810 0.4161
0.3998 2.46 820 0.4107
0.3789 2.49 830 0.4080
0.4204 2.52 840 0.4126
0.401 2.55 850 0.4094
0.4049 2.58 860 0.3987
0.4129 2.61 870 0.3941
0.3414 2.64 880 0.3951
0.4505 2.67 890 0.3942
0.407 2.7 900 0.3985
0.3854 2.73 910 0.4011
0.4164 2.76 920 0.4025
0.4032 2.79 930 0.4005
0.3638 2.82 940 0.4002
0.3799 2.85 950 0.4008
0.4283 2.89 960 0.3955
0.3696 2.92 970 0.3947
0.3766 2.95 980 0.3959
0.3956 2.98 990 0.3950
0.3816 3.01 1000 0.3950
0.3108 3.04 1010 0.3934
0.3578 3.07 1020 0.3970
0.3519 3.1 1030 0.4019
0.3121 3.13 1040 0.4065
0.3845 3.16 1050 0.4081
0.342 3.19 1060 0.4075
0.3573 3.22 1070 0.4093
0.3673 3.25 1080 0.4089
0.3596 3.28 1090 0.4084
0.3474 3.31 1100 0.4089
0.3966 3.34 1110 0.4072
0.3813 3.37 1120 0.4046
0.3557 3.4 1130 0.4067
0.4239 3.43 1140 0.4052
0.3938 3.46 1150 0.3973
0.3686 3.49 1160 0.3980
0.3763 3.52 1170 0.3954
0.3754 3.55 1180 0.3936
0.3656 3.58 1190 0.3937
0.3383 3.61 1200 0.3942
0.3984 3.64 1210 0.3974
0.3814 3.67 1220 0.3995
0.3309 3.7 1230 0.3980
0.347 3.73 1240 0.3971
0.3727 3.76 1250 0.3958
0.381 3.79 1260 0.3950
0.3824 3.82 1270 0.3946
0.3971 3.85 1280 0.3941
0.3634 3.88 1290 0.3950
0.3501 3.91 1300 0.3939
0.327 3.94 1310 0.3927
0.3313 3.97 1320 0.3978
0.378 4.0 1330 0.4029
0.3376 4.03 1340 0.4008
0.3636 4.06 1350 0.3977
0.3064 4.09 1360 0.3976
0.4012 4.12 1370 0.3981
0.2932 4.15 1380 0.3991
0.3805 4.18 1390 0.3996
0.3438 4.21 1400 0.3996
0.3413 4.24 1410 0.4001
0.365 4.27 1420 0.3981
0.3769 4.3 1430 0.3960
0.3552 4.33 1440 0.3940
0.3328 4.36 1450 0.3937
0.3719 4.39 1460 0.3929
0.3607 4.42 1470 0.3933
0.3117 4.45 1480 0.3942
0.3522 4.48 1490 0.3964
0.375 4.51 1500 0.3979
0.3337 4.54 1510 0.3985
0.3549 4.57 1520 0.3959
0.4021 4.6 1530 0.3928
0.3217 4.63 1540 0.3895
0.3533 4.66 1550 0.3881
0.3564 4.69 1560 0.3882
0.3281 4.72 1570 0.3891
0.3474 4.75 1580 0.3897
0.3447 4.78 1590 0.3910
0.318 4.81 1600 0.3928
0.3414 4.84 1610 0.3942
0.3311 4.87 1620 0.3940
0.2932 4.9 1630 0.3921
0.3456 4.93 1640 0.3886
0.3462 4.96 1650 0.3851
0.3599 4.99 1660 0.3847
0.2819 5.02 1670 0.3864
0.3026 5.05 1680 0.3899
0.3272 5.08 1690 0.3909
0.3466 5.11 1700 0.3911
0.3279 5.14 1710 0.3912
0.3317 5.17 1720 0.3908
0.3523 5.2 1730 0.3906
0.3044 5.23 1740 0.3911
0.3361 5.26 1750 0.3902
0.3303 5.29 1760 0.3891
0.351 5.32 1770 0.3887
0.3093 5.35 1780 0.3904
0.3773 5.38 1790 0.3900
0.3302 5.41 1800 0.3901
0.303 5.44 1810 0.3911
0.3131 5.47 1820 0.3914
0.3483 5.5 1830 0.3907
0.3046 5.53 1840 0.3902
0.3794 5.56 1850 0.3898
0.3228 5.59 1860 0.3892
0.3268 5.62 1870 0.3888
0.3602 5.65 1880 0.3869
0.3655 5.68 1890 0.3854
0.3168 5.71 1900 0.3844
0.3359 5.74 1910 0.3840
0.314 5.77 1920 0.3841
0.3428 5.8 1930 0.3843
0.3504 5.83 1940 0.3835
0.3342 5.86 1950 0.3831
0.3474 5.89 1960 0.3828
0.3453 5.92 1970 0.3821
0.2968 5.95 1980 0.3829
0.3402 5.98 1990 0.3826
0.3401 6.01 2000 0.3819
0.3282 6.04 2010 0.3817
0.3265 6.07 2020 0.3821
0.3265 6.1 2030 0.3836
0.3189 6.13 2040 0.3841
0.2829 6.16 2050 0.3847
0.341 6.19 2060 0.3855
0.307 6.22 2070 0.3858
0.3037 6.25 2080 0.3853
0.2908 6.28 2090 0.3853
0.3204 6.31 2100 0.3856
0.3103 6.34 2110 0.3855
0.3359 6.37 2120 0.3851
0.3156 6.4 2130 0.3842
0.3207 6.43 2140 0.3837
0.2814 6.46 2150 0.3833
0.2908 6.49 2160 0.3833
0.3119 6.52 2170 0.3836
0.3272 6.55 2180 0.3839
0.3309 6.58 2190 0.3835
0.3141 6.61 2200 0.3832
0.3082 6.64 2210 0.3831
0.3661 6.67 2220 0.3829
0.3256 6.7 2230 0.3828
0.2938 6.73 2240 0.3828
0.3268 6.76 2250 0.3827
0.34 6.79 2260 0.3827
0.3444 6.82 2270 0.3825
0.3251 6.85 2280 0.3823
0.277 6.88 2290 0.3824
0.2945 6.91 2300 0.3824
0.3313 6.94 2310 0.3824
0.3536 6.97 2320 0.3823

Framework versions