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DEREXP
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.1590
- Mse: 3.1590
- Mae: 1.3397
- R2: 0.4465
- Accuracy: 0.2528
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|---|---|---|---|---|---|---|---|
| 14.7557 | 0.01 | 500 | 4.3307 | 4.3307 | 1.6240 | 0.2411 | 0.1976 |
| 4.5754 | 0.02 | 1000 | 4.1273 | 4.1273 | 1.5719 | 0.2768 | 0.2084 |
| 4.2925 | 0.02 | 1500 | 4.3074 | 4.3074 | 1.6155 | 0.2452 | 0.2012 |
| 3.9816 | 0.03 | 2000 | 3.7767 | 3.7767 | 1.5008 | 0.3382 | 0.2134 |
| 3.9171 | 0.04 | 2500 | 3.7033 | 3.7033 | 1.4732 | 0.3511 | 0.2304 |
| 3.946 | 0.05 | 3000 | 3.6217 | 3.6217 | 1.4552 | 0.3654 | 0.2352 |
| 4.1 | 0.06 | 3500 | 3.6101 | 3.6101 | 1.4612 | 0.3674 | 0.2216 |
| 3.8535 | 0.06 | 4000 | 3.6160 | 3.6160 | 1.4576 | 0.3664 | 0.2294 |
| 3.9037 | 0.07 | 4500 | 3.5864 | 3.5864 | 1.4476 | 0.3716 | 0.2374 |
| 3.9358 | 0.08 | 5000 | 3.5087 | 3.5087 | 1.4237 | 0.3852 | 0.2414 |
| 3.8062 | 0.09 | 5500 | 3.6085 | 3.6085 | 1.4595 | 0.3677 | 0.2256 |
| 3.8802 | 0.1 | 6000 | 3.6371 | 3.6371 | 1.4615 | 0.3627 | 0.223 |
| 3.7239 | 0.1 | 6500 | 3.5191 | 3.5191 | 1.4278 | 0.3834 | 0.2324 |
| 3.7618 | 0.11 | 7000 | 3.8408 | 3.8408 | 1.4973 | 0.3270 | 0.2316 |
| 3.7217 | 0.12 | 7500 | 3.8241 | 3.8241 | 1.5046 | 0.3299 | 0.2236 |
| 3.8204 | 0.13 | 8000 | 3.5290 | 3.5290 | 1.4256 | 0.3816 | 0.2388 |
| 3.7211 | 0.14 | 8500 | 3.6903 | 3.6903 | 1.4674 | 0.3534 | 0.227 |
| 3.7243 | 0.14 | 9000 | 3.4718 | 3.4718 | 1.4201 | 0.3917 | 0.231 |
| 3.7713 | 0.15 | 9500 | 3.8970 | 3.8970 | 1.5304 | 0.3171 | 0.2092 |
| 3.6289 | 0.16 | 10000 | 3.5273 | 3.5273 | 1.4255 | 0.3819 | 0.2388 |
| 3.7516 | 0.17 | 10500 | 3.9020 | 3.9020 | 1.5230 | 0.3163 | 0.2138 |
| 3.7491 | 0.18 | 11000 | 3.4809 | 3.4809 | 1.4209 | 0.3901 | 0.2378 |
| 3.7809 | 0.18 | 11500 | 3.8779 | 3.8779 | 1.5087 | 0.3205 | 0.229 |
| 3.7163 | 0.19 | 12000 | 3.5177 | 3.5177 | 1.4330 | 0.3836 | 0.2298 |
| 3.732 | 0.2 | 12500 | 3.9986 | 3.9986 | 1.5401 | 0.2993 | 0.218 |
| 3.7381 | 0.21 | 13000 | 3.4782 | 3.4782 | 1.4277 | 0.3905 | 0.2302 |
| 3.7652 | 0.22 | 13500 | 3.6239 | 3.6239 | 1.4587 | 0.3650 | 0.2244 |
| 3.6003 | 0.22 | 14000 | 3.4873 | 3.4873 | 1.4288 | 0.3889 | 0.2316 |
| 3.6865 | 0.23 | 14500 | 3.5895 | 3.5895 | 1.4511 | 0.3710 | 0.23 |
| 3.7398 | 0.24 | 15000 | 3.8835 | 3.8835 | 1.5183 | 0.3195 | 0.2172 |
| 3.5939 | 0.25 | 15500 | 3.6334 | 3.6334 | 1.4643 | 0.3633 | 0.2256 |
| 3.691 | 0.26 | 16000 | 3.4251 | 3.4251 | 1.3994 | 0.3998 | 0.2488 |
| 3.7279 | 0.26 | 16500 | 3.3956 | 3.3956 | 1.4034 | 0.4050 | 0.2336 |
| 3.797 | 0.27 | 17000 | 3.4029 | 3.4029 | 1.3968 | 0.4037 | 0.2486 |
| 3.684 | 0.28 | 17500 | 3.5831 | 3.5831 | 1.4451 | 0.3721 | 0.2304 |
| 3.5894 | 0.29 | 18000 | 3.6120 | 3.6120 | 1.4492 | 0.3671 | 0.2338 |
| 3.5938 | 0.3 | 18500 | 3.4975 | 3.4975 | 1.4240 | 0.3871 | 0.231 |
| 3.4948 | 0.3 | 19000 | 3.4791 | 3.4791 | 1.4167 | 0.3904 | 0.24 |
| 3.6527 | 0.31 | 19500 | 3.3409 | 3.3409 | 1.3817 | 0.4146 | 0.2474 |
| 3.5545 | 0.32 | 20000 | 3.3412 | 3.3412 | 1.3860 | 0.4145 | 0.2466 |
| 3.6102 | 0.33 | 20500 | 3.4148 | 3.4148 | 1.3961 | 0.4016 | 0.2488 |
| 3.542 | 0.34 | 21000 | 3.5980 | 3.5980 | 1.4508 | 0.3695 | 0.2244 |
| 3.5081 | 0.34 | 21500 | 3.6310 | 3.6310 | 1.4488 | 0.3637 | 0.2372 |
| 3.7745 | 0.35 | 22000 | 3.5246 | 3.5246 | 1.4294 | 0.3824 | 0.2378 |
| 3.5048 | 0.36 | 22500 | 3.4395 | 3.4395 | 1.4126 | 0.3973 | 0.241 |
| 3.6374 | 0.37 | 23000 | 3.3863 | 3.3863 | 1.3928 | 0.4066 | 0.247 |
| 3.5231 | 0.38 | 23500 | 3.5991 | 3.5991 | 1.4468 | 0.3693 | 0.2348 |
| 3.5893 | 0.38 | 24000 | 3.2910 | 3.2910 | 1.3692 | 0.4233 | 0.2504 |
| 3.5051 | 0.39 | 24500 | 3.3765 | 3.3765 | 1.3953 | 0.4083 | 0.2394 |
| 3.6082 | 0.4 | 25000 | 3.3060 | 3.3060 | 1.3830 | 0.4207 | 0.2412 |
| 3.4009 | 0.41 | 25500 | 3.4448 | 3.4448 | 1.4095 | 0.3964 | 0.2404 |
| 3.4239 | 0.42 | 26000 | 3.4127 | 3.4127 | 1.4027 | 0.4020 | 0.2412 |
| 3.6036 | 0.42 | 26500 | 3.5339 | 3.5339 | 1.4405 | 0.3808 | 0.2266 |
| 3.4107 | 0.43 | 27000 | 3.3319 | 3.3319 | 1.3776 | 0.4162 | 0.2542 |
| 3.3903 | 0.44 | 27500 | 3.4434 | 3.4434 | 1.4072 | 0.3966 | 0.2486 |
| 3.5583 | 0.45 | 28000 | 3.3119 | 3.3119 | 1.3728 | 0.4197 | 0.2516 |
| 3.4701 | 0.46 | 28500 | 3.3733 | 3.3733 | 1.3910 | 0.4089 | 0.2494 |
| 3.4113 | 0.46 | 29000 | 3.4144 | 3.4144 | 1.4027 | 0.4017 | 0.2414 |
| 3.5731 | 0.47 | 29500 | 3.3822 | 3.3822 | 1.3911 | 0.4073 | 0.2428 |
| 3.5738 | 0.48 | 30000 | 3.4408 | 3.4408 | 1.4120 | 0.3971 | 0.2386 |
| 3.481 | 0.49 | 30500 | 3.3255 | 3.3255 | 1.3794 | 0.4173 | 0.2514 |
| 3.4716 | 0.5 | 31000 | 3.2817 | 3.2817 | 1.3703 | 0.4250 | 0.2492 |
| 3.5487 | 0.5 | 31500 | 3.3388 | 3.3388 | 1.3851 | 0.4149 | 0.2472 |
| 3.2559 | 0.51 | 32000 | 3.3552 | 3.3552 | 1.3847 | 0.4121 | 0.249 |
| 3.5715 | 0.52 | 32500 | 3.2896 | 3.2896 | 1.3692 | 0.4236 | 0.251 |
| 3.4085 | 0.53 | 33000 | 3.2690 | 3.2690 | 1.3685 | 0.4272 | 0.2522 |
| 3.5582 | 0.54 | 33500 | 3.3228 | 3.3228 | 1.3800 | 0.4178 | 0.2462 |
| 3.4105 | 0.54 | 34000 | 3.4462 | 3.4462 | 1.4089 | 0.3961 | 0.2474 |
| 3.5401 | 0.55 | 34500 | 3.3181 | 3.3181 | 1.3751 | 0.4186 | 0.2558 |
| 3.4213 | 0.56 | 35000 | 3.2455 | 3.2455 | 1.3592 | 0.4313 | 0.2548 |
| 3.4644 | 0.57 | 35500 | 3.3900 | 3.3900 | 1.4004 | 0.4060 | 0.2388 |
| 3.4277 | 0.58 | 36000 | 3.2150 | 3.2150 | 1.3506 | 0.4366 | 0.2558 |
| 3.3376 | 0.58 | 36500 | 3.3522 | 3.3522 | 1.3944 | 0.4126 | 0.24 |
| 3.4311 | 0.59 | 37000 | 3.4152 | 3.4152 | 1.3980 | 0.4016 | 0.2498 |
| 3.336 | 0.6 | 37500 | 3.2996 | 3.2996 | 1.3674 | 0.4218 | 0.2594 |
| 3.3557 | 0.61 | 38000 | 3.2040 | 3.2040 | 1.3499 | 0.4386 | 0.2486 |
| 3.3586 | 0.62 | 38500 | 3.2784 | 3.2784 | 1.3632 | 0.4255 | 0.2534 |
| 3.3187 | 0.62 | 39000 | 3.3466 | 3.3466 | 1.3832 | 0.4136 | 0.2468 |
| 3.3899 | 0.63 | 39500 | 3.3209 | 3.3209 | 1.3795 | 0.4181 | 0.25 |
| 3.4483 | 0.64 | 40000 | 3.4685 | 3.4685 | 1.4165 | 0.3922 | 0.2436 |
| 3.3463 | 0.65 | 40500 | 3.3874 | 3.3874 | 1.3961 | 0.4064 | 0.2448 |
| 3.373 | 0.66 | 41000 | 3.2243 | 3.2243 | 1.3518 | 0.4350 | 0.2562 |
| 3.4526 | 0.66 | 41500 | 3.2819 | 3.2819 | 1.3693 | 0.4249 | 0.253 |
| 3.3581 | 0.67 | 42000 | 3.3412 | 3.3412 | 1.3843 | 0.4145 | 0.2456 |
| 3.4551 | 0.68 | 42500 | 3.2484 | 3.2484 | 1.3594 | 0.4308 | 0.2574 |
| 3.4022 | 0.69 | 43000 | 3.2010 | 3.2010 | 1.3468 | 0.4391 | 0.2568 |
| 3.3281 | 0.7 | 43500 | 3.3184 | 3.3184 | 1.3764 | 0.4185 | 0.2476 |
| 3.4044 | 0.7 | 44000 | 3.2361 | 3.2361 | 1.3528 | 0.4329 | 0.2506 |
| 3.3427 | 0.71 | 44500 | 3.2269 | 3.2269 | 1.3557 | 0.4346 | 0.2492 |
| 3.4106 | 0.72 | 45000 | 3.2758 | 3.2758 | 1.3733 | 0.4260 | 0.2434 |
| 3.4406 | 0.73 | 45500 | 3.2235 | 3.2235 | 1.3548 | 0.4352 | 0.2526 |
| 3.491 | 0.74 | 46000 | 3.2842 | 3.2842 | 1.3688 | 0.4245 | 0.2496 |
| 3.4671 | 0.74 | 46500 | 3.1811 | 3.1811 | 1.3464 | 0.4426 | 0.249 |
| 3.5774 | 0.75 | 47000 | 3.2649 | 3.2649 | 1.3608 | 0.4279 | 0.251 |
| 3.4953 | 0.76 | 47500 | 3.2681 | 3.2681 | 1.3616 | 0.4273 | 0.2538 |
| 3.4212 | 0.77 | 48000 | 3.4407 | 3.4407 | 1.4088 | 0.3971 | 0.2424 |
| 3.3285 | 0.78 | 48500 | 3.3279 | 3.3279 | 1.3771 | 0.4169 | 0.2454 |
| 3.361 | 0.78 | 49000 | 3.3717 | 3.3717 | 1.3910 | 0.4092 | 0.243 |
| 3.5419 | 0.79 | 49500 | 3.2851 | 3.2851 | 1.3748 | 0.4244 | 0.2448 |
| 3.3979 | 0.8 | 50000 | 3.3991 | 3.3991 | 1.4039 | 0.4044 | 0.2378 |
| 3.3354 | 0.81 | 50500 | 3.2636 | 3.2636 | 1.3650 | 0.4281 | 0.2456 |
| 3.4488 | 0.82 | 51000 | 3.2604 | 3.2604 | 1.3695 | 0.4287 | 0.243 |
| 3.2583 | 0.82 | 51500 | 3.2759 | 3.2759 | 1.3759 | 0.4260 | 0.2442 |
| 3.3419 | 0.83 | 52000 | 3.2789 | 3.2789 | 1.3728 | 0.4254 | 0.2494 |
| 3.4243 | 0.84 | 52500 | 3.2993 | 3.2993 | 1.3772 | 0.4219 | 0.2486 |
| 3.3154 | 0.85 | 53000 | 3.2350 | 3.2350 | 1.3585 | 0.4331 | 0.2528 |
| 3.3462 | 0.86 | 53500 | 3.2361 | 3.2361 | 1.3594 | 0.4329 | 0.2516 |
| 3.4554 | 0.86 | 54000 | 3.2307 | 3.2307 | 1.3548 | 0.4339 | 0.2528 |
| 3.5053 | 0.87 | 54500 | 3.1970 | 3.1970 | 1.3494 | 0.4398 | 0.2526 |
| 3.2745 | 0.88 | 55000 | 3.2506 | 3.2506 | 1.3614 | 0.4304 | 0.2546 |
| 3.3788 | 0.89 | 55500 | 3.2090 | 3.2090 | 1.3540 | 0.4377 | 0.2516 |
| 3.3216 | 0.9 | 56000 | 3.3347 | 3.3347 | 1.3857 | 0.4157 | 0.2462 |
| 3.2991 | 0.9 | 56500 | 3.1590 | 3.1590 | 1.3397 | 0.4465 | 0.2528 |
| 3.175 | 0.91 | 57000 | 3.2950 | 3.2950 | 1.3734 | 0.4226 | 0.2534 |
| 3.4697 | 0.92 | 57500 | 3.2021 | 3.2021 | 1.3483 | 0.4389 | 0.255 |
| 3.2413 | 0.93 | 58000 | 3.2157 | 3.2157 | 1.3523 | 0.4365 | 0.2518 |
| 3.3949 | 0.94 | 58500 | 3.2709 | 3.2709 | 1.3678 | 0.4268 | 0.2494 |
| 3.3502 | 0.94 | 59000 | 3.2263 | 3.2263 | 1.3558 | 0.4347 | 0.253 |
| 3.3492 | 0.95 | 59500 | 3.2667 | 3.2667 | 1.3659 | 0.4276 | 0.2538 |
| 3.3568 | 0.96 | 60000 | 3.1717 | 3.1717 | 1.3410 | 0.4442 | 0.2542 |
| 3.3886 | 0.97 | 60500 | 3.1800 | 3.1800 | 1.3444 | 0.4428 | 0.2534 |
| 3.2994 | 0.98 | 61000 | 3.2166 | 3.2166 | 1.3539 | 0.4364 | 0.2498 |
| 3.3381 | 0.98 | 61500 | 3.1964 | 3.1964 | 1.3484 | 0.4399 | 0.2534 |
| 3.351 | 0.99 | 62000 | 3.1664 | 3.1664 | 1.3393 | 0.4452 | 0.2538 |
| 3.4063 | 1.0 | 62500 | 3.1764 | 3.1764 | 1.3421 | 0.4434 | 0.2542 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
