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t5-end2end-questions-generation_3
This model is a fine-tuned version of Gayathri142214002/t5-end2end-questions-generation_2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3733
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:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.6874 | 0.05 | 10 | 0.5152 |
0.5855 | 0.11 | 20 | 0.4873 |
0.5778 | 0.16 | 30 | 0.4874 |
0.5322 | 0.21 | 40 | 0.4939 |
0.5852 | 0.27 | 50 | 0.4955 |
0.5046 | 0.32 | 60 | 0.5046 |
0.3833 | 0.37 | 70 | 0.5042 |
0.459 | 0.42 | 80 | 0.5073 |
0.4129 | 0.48 | 90 | 0.5082 |
0.5371 | 0.53 | 100 | 0.5053 |
0.4841 | 0.58 | 110 | 0.5015 |
0.4994 | 0.64 | 120 | 0.4695 |
0.4129 | 0.69 | 130 | 0.4582 |
0.4524 | 0.74 | 140 | 0.4540 |
0.4908 | 0.8 | 150 | 0.4424 |
0.5084 | 0.85 | 160 | 0.4244 |
0.4423 | 0.9 | 170 | 0.4048 |
0.4426 | 0.96 | 180 | 0.3875 |
0.3701 | 1.01 | 190 | 0.4059 |
0.4315 | 1.06 | 200 | 0.3965 |
0.3846 | 1.12 | 210 | 0.4089 |
0.3511 | 1.17 | 220 | 0.4195 |
0.3644 | 1.22 | 230 | 0.4303 |
0.3706 | 1.27 | 240 | 0.4264 |
0.3325 | 1.33 | 250 | 0.4291 |
0.4266 | 1.38 | 260 | 0.4280 |
0.3983 | 1.43 | 270 | 0.4209 |
0.4121 | 1.49 | 280 | 0.4144 |
0.386 | 1.54 | 290 | 0.4140 |
0.4533 | 1.59 | 300 | 0.4128 |
0.3978 | 1.65 | 310 | 0.4182 |
0.38 | 1.7 | 320 | 0.4248 |
0.3818 | 1.75 | 330 | 0.4194 |
0.3957 | 1.81 | 340 | 0.3931 |
0.3409 | 1.86 | 350 | 0.3859 |
0.3619 | 1.91 | 360 | 0.3973 |
0.388 | 1.97 | 370 | 0.4041 |
0.377 | 2.02 | 380 | 0.4017 |
0.3645 | 2.07 | 390 | 0.3985 |
0.3268 | 2.12 | 400 | 0.3987 |
0.3389 | 2.18 | 410 | 0.3990 |
0.3405 | 2.23 | 420 | 0.3985 |
0.3677 | 2.28 | 430 | 0.3972 |
0.333 | 2.34 | 440 | 0.4131 |
0.3641 | 2.39 | 450 | 0.4188 |
0.3568 | 2.44 | 460 | 0.4132 |
0.3443 | 2.5 | 470 | 0.4266 |
0.337 | 2.55 | 480 | 0.4321 |
0.3562 | 2.6 | 490 | 0.4271 |
0.3411 | 2.66 | 500 | 0.4131 |
0.3386 | 2.71 | 510 | 0.4078 |
0.3307 | 2.76 | 520 | 0.3912 |
0.3293 | 2.82 | 530 | 0.3931 |
0.3647 | 2.87 | 540 | 0.3893 |
0.3555 | 2.92 | 550 | 0.3781 |
0.3588 | 2.97 | 560 | 0.3730 |
0.3088 | 3.03 | 570 | 0.3639 |
0.2886 | 3.08 | 580 | 0.3604 |
0.2834 | 3.13 | 590 | 0.3723 |
0.3154 | 3.19 | 600 | 0.3817 |
0.319 | 3.24 | 610 | 0.3847 |
0.298 | 3.29 | 620 | 0.3844 |
0.3443 | 3.35 | 630 | 0.3821 |
0.3418 | 3.4 | 640 | 0.3800 |
0.3606 | 3.45 | 650 | 0.3774 |
0.3668 | 3.51 | 660 | 0.3667 |
0.3331 | 3.56 | 670 | 0.3593 |
0.3045 | 3.61 | 680 | 0.3561 |
0.3307 | 3.67 | 690 | 0.3524 |
0.3409 | 3.72 | 700 | 0.3528 |
0.3783 | 3.77 | 710 | 0.3558 |
0.3288 | 3.82 | 720 | 0.3622 |
0.3248 | 3.88 | 730 | 0.3728 |
0.3115 | 3.93 | 740 | 0.3714 |
0.3374 | 3.98 | 750 | 0.3712 |
0.2982 | 4.04 | 760 | 0.3734 |
0.2636 | 4.09 | 770 | 0.3749 |
0.3035 | 4.14 | 780 | 0.3787 |
0.3041 | 4.2 | 790 | 0.3782 |
0.3112 | 4.25 | 800 | 0.3791 |
0.332 | 4.3 | 810 | 0.3789 |
0.2698 | 4.36 | 820 | 0.3822 |
0.277 | 4.41 | 830 | 0.3808 |
0.3357 | 4.46 | 840 | 0.3800 |
0.3224 | 4.52 | 850 | 0.3770 |
0.3358 | 4.57 | 860 | 0.3731 |
0.2973 | 4.62 | 870 | 0.3743 |
0.2815 | 4.67 | 880 | 0.3776 |
0.3233 | 4.73 | 890 | 0.3768 |
0.3034 | 4.78 | 900 | 0.3728 |
0.2978 | 4.83 | 910 | 0.3718 |
0.2692 | 4.89 | 920 | 0.3700 |
0.3294 | 4.94 | 930 | 0.3710 |
0.3022 | 4.99 | 940 | 0.3733 |
0.3036 | 5.05 | 950 | 0.3706 |
0.3134 | 5.1 | 960 | 0.3673 |
0.2808 | 5.15 | 970 | 0.3643 |
0.2728 | 5.21 | 980 | 0.3661 |
0.3201 | 5.26 | 990 | 0.3697 |
0.2873 | 5.31 | 1000 | 0.3710 |
0.2709 | 5.37 | 1010 | 0.3716 |
0.2758 | 5.42 | 1020 | 0.3728 |
0.2695 | 5.47 | 1030 | 0.3759 |
0.2734 | 5.52 | 1040 | 0.3758 |
0.2993 | 5.58 | 1050 | 0.3747 |
0.2697 | 5.63 | 1060 | 0.3756 |
0.3017 | 5.68 | 1070 | 0.3772 |
0.299 | 5.74 | 1080 | 0.3782 |
0.2834 | 5.79 | 1090 | 0.3795 |
0.3069 | 5.84 | 1100 | 0.3782 |
0.3309 | 5.9 | 1110 | 0.3764 |
0.3041 | 5.95 | 1120 | 0.3750 |
0.3047 | 6.0 | 1130 | 0.3743 |
0.2717 | 6.06 | 1140 | 0.3738 |
0.2108 | 6.11 | 1150 | 0.3749 |
0.2637 | 6.16 | 1160 | 0.3764 |
0.2972 | 6.22 | 1170 | 0.3748 |
0.2818 | 6.27 | 1180 | 0.3735 |
0.2934 | 6.32 | 1190 | 0.3729 |
0.3049 | 6.37 | 1200 | 0.3726 |
0.2389 | 6.43 | 1210 | 0.3727 |
0.2831 | 6.48 | 1220 | 0.3742 |
0.3017 | 6.53 | 1230 | 0.3751 |
0.2869 | 6.59 | 1240 | 0.3755 |
0.2719 | 6.64 | 1250 | 0.3749 |
0.2754 | 6.69 | 1260 | 0.3744 |
0.3138 | 6.75 | 1270 | 0.3738 |
0.2772 | 6.8 | 1280 | 0.3734 |
0.2952 | 6.85 | 1290 | 0.3733 |
0.2746 | 6.91 | 1300 | 0.3733 |
0.2918 | 6.96 | 1310 | 0.3733 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3