--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer model-index: - name: bert_baseline_prompt_adherence_task4_fold1 results: [] --- # bert_baseline_prompt_adherence_task4_fold1 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3376 - Qwk: 0.7136 - Mse: 0.3347 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | No log | 0.0299 | 2 | 1.2352 | 0.0 | 1.2327 | | No log | 0.0597 | 4 | 0.9657 | 0.0 | 0.9636 | | No log | 0.0896 | 6 | 0.8614 | 0.2376 | 0.8596 | | No log | 0.1194 | 8 | 0.8177 | 0.3472 | 0.8159 | | No log | 0.1493 | 10 | 0.7773 | 0.3551 | 0.7756 | | No log | 0.1791 | 12 | 0.7212 | 0.3577 | 0.7196 | | No log | 0.2090 | 14 | 0.7108 | 0.3493 | 0.7094 | | No log | 0.2388 | 16 | 0.6580 | 0.3495 | 0.6568 | | No log | 0.2687 | 18 | 0.6153 | 0.3520 | 0.6140 | | No log | 0.2985 | 20 | 0.5757 | 0.3622 | 0.5740 | | No log | 0.3284 | 22 | 0.5514 | 0.3998 | 0.5492 | | No log | 0.3582 | 24 | 0.5541 | 0.3725 | 0.5516 | | No log | 0.3881 | 26 | 0.5877 | 0.3478 | 0.5850 | | No log | 0.4179 | 28 | 0.5115 | 0.3637 | 0.5088 | | No log | 0.4478 | 30 | 0.4795 | 0.3784 | 0.4767 | | No log | 0.4776 | 32 | 0.4555 | 0.4686 | 0.4527 | | No log | 0.5075 | 34 | 0.4509 | 0.6119 | 0.4480 | | No log | 0.5373 | 36 | 0.4494 | 0.5913 | 0.4465 | | No log | 0.5672 | 38 | 0.4459 | 0.5273 | 0.4431 | | No log | 0.5970 | 40 | 0.4446 | 0.4820 | 0.4421 | | No log | 0.6269 | 42 | 0.4337 | 0.5212 | 0.4310 | | No log | 0.6567 | 44 | 0.4555 | 0.4669 | 0.4523 | | No log | 0.6866 | 46 | 0.5375 | 0.3796 | 0.5338 | | No log | 0.7164 | 48 | 0.5162 | 0.4192 | 0.5125 | | No log | 0.7463 | 50 | 0.4909 | 0.4316 | 0.4873 | | No log | 0.7761 | 52 | 0.4538 | 0.4563 | 0.4505 | | No log | 0.8060 | 54 | 0.3986 | 0.6088 | 0.3957 | | No log | 0.8358 | 56 | 0.3970 | 0.6362 | 0.3945 | | No log | 0.8657 | 58 | 0.4051 | 0.6160 | 0.4029 | | No log | 0.8955 | 60 | 0.4402 | 0.6915 | 0.4385 | | No log | 0.9254 | 62 | 0.4589 | 0.7131 | 0.4574 | | No log | 0.9552 | 64 | 0.4155 | 0.6957 | 0.4136 | | No log | 0.9851 | 66 | 0.3925 | 0.6686 | 0.3902 | | No log | 1.0149 | 68 | 0.3803 | 0.6230 | 0.3779 | | No log | 1.0448 | 70 | 0.3849 | 0.5866 | 0.3823 | | No log | 1.0746 | 72 | 0.3933 | 0.5621 | 0.3907 | | No log | 1.1045 | 74 | 0.3767 | 0.6134 | 0.3742 | | No log | 1.1343 | 76 | 0.3974 | 0.6952 | 0.3954 | | No log | 1.1642 | 78 | 0.4197 | 0.7059 | 0.4180 | | No log | 1.1940 | 80 | 0.4126 | 0.7019 | 0.4106 | | No log | 1.2239 | 82 | 0.3825 | 0.6743 | 0.3801 | | No log | 1.2537 | 84 | 0.3870 | 0.5898 | 0.3841 | | No log | 1.2836 | 86 | 0.4077 | 0.5308 | 0.4046 | | No log | 1.3134 | 88 | 0.3785 | 0.6068 | 0.3754 | | No log | 1.3433 | 90 | 0.3659 | 0.6831 | 0.3634 | | No log | 1.3731 | 92 | 0.4110 | 0.7143 | 0.4094 | | No log | 1.4030 | 94 | 0.4019 | 0.7165 | 0.4003 | | No log | 1.4328 | 96 | 0.3639 | 0.6585 | 0.3616 | | No log | 1.4627 | 98 | 0.3611 | 0.5696 | 0.3580 | | No log | 1.4925 | 100 | 0.4004 | 0.4979 | 0.3969 | | No log | 1.5224 | 102 | 0.4052 | 0.4842 | 0.4016 | | No log | 1.5522 | 104 | 0.3603 | 0.5786 | 0.3571 | | No log | 1.5821 | 106 | 0.3606 | 0.7021 | 0.3581 | | No log | 1.6119 | 108 | 0.3934 | 0.7166 | 0.3912 | | No log | 1.6418 | 110 | 0.3772 | 0.7016 | 0.3746 | | No log | 1.6716 | 112 | 0.3629 | 0.6278 | 0.3595 | | No log | 1.7015 | 114 | 0.4135 | 0.4969 | 0.4097 | | No log | 1.7313 | 116 | 0.4136 | 0.5040 | 0.4096 | | No log | 1.7612 | 118 | 0.3709 | 0.6049 | 0.3670 | | No log | 1.7910 | 120 | 0.3694 | 0.6999 | 0.3659 | | No log | 1.8209 | 122 | 0.3852 | 0.7076 | 0.3820 | | No log | 1.8507 | 124 | 0.4129 | 0.7210 | 0.4100 | | No log | 1.8806 | 126 | 0.4104 | 0.7182 | 0.4071 | | No log | 1.9104 | 128 | 0.3831 | 0.7150 | 0.3795 | | No log | 1.9403 | 130 | 0.3645 | 0.7005 | 0.3605 | | No log | 1.9701 | 132 | 0.3586 | 0.6678 | 0.3545 | | No log | 2.0 | 134 | 0.3508 | 0.6547 | 0.3467 | | No log | 2.0299 | 136 | 0.3474 | 0.6867 | 0.3438 | | No log | 2.0597 | 138 | 0.3686 | 0.7233 | 0.3658 | | No log | 2.0896 | 140 | 0.3793 | 0.7348 | 0.3771 | | No log | 2.1194 | 142 | 0.3694 | 0.7303 | 0.3672 | | No log | 2.1493 | 144 | 0.3552 | 0.7217 | 0.3529 | | No log | 2.1791 | 146 | 0.3436 | 0.6527 | 0.3411 | | No log | 2.2090 | 148 | 0.3436 | 0.6231 | 0.3408 | | No log | 2.2388 | 150 | 0.3448 | 0.6403 | 0.3422 | | No log | 2.2687 | 152 | 0.3592 | 0.6930 | 0.3570 | | No log | 2.2985 | 154 | 0.3549 | 0.6682 | 0.3526 | | No log | 2.3284 | 156 | 0.3531 | 0.6565 | 0.3509 | | No log | 2.3582 | 158 | 0.3577 | 0.5698 | 0.3554 | | No log | 2.3881 | 160 | 0.3661 | 0.5155 | 0.3635 | | No log | 2.4179 | 162 | 0.3571 | 0.5463 | 0.3543 | | No log | 2.4478 | 164 | 0.3388 | 0.6163 | 0.3358 | | No log | 2.4776 | 166 | 0.3428 | 0.6843 | 0.3400 | | No log | 2.5075 | 168 | 0.3889 | 0.7374 | 0.3864 | | No log | 2.5373 | 170 | 0.4104 | 0.7415 | 0.4078 | | No log | 2.5672 | 172 | 0.3920 | 0.7343 | 0.3890 | | No log | 2.5970 | 174 | 0.3589 | 0.7071 | 0.3553 | | No log | 2.6269 | 176 | 0.3418 | 0.6973 | 0.3379 | | No log | 2.6567 | 178 | 0.3379 | 0.6729 | 0.3338 | | No log | 2.6866 | 180 | 0.3366 | 0.6531 | 0.3325 | | No log | 2.7164 | 182 | 0.3335 | 0.6938 | 0.3297 | | No log | 2.7463 | 184 | 0.3515 | 0.7081 | 0.3485 | | No log | 2.7761 | 186 | 0.3844 | 0.7387 | 0.3820 | | No log | 2.8060 | 188 | 0.3874 | 0.7441 | 0.3852 | | No log | 2.8358 | 190 | 0.3495 | 0.7224 | 0.3468 | | No log | 2.8657 | 192 | 0.3242 | 0.6783 | 0.3208 | | No log | 2.8955 | 194 | 0.3322 | 0.6336 | 0.3286 | | No log | 2.9254 | 196 | 0.3354 | 0.6577 | 0.3316 | | No log | 2.9552 | 198 | 0.3368 | 0.6913 | 0.3332 | | No log | 2.9851 | 200 | 0.3676 | 0.7275 | 0.3647 | | No log | 3.0149 | 202 | 0.4124 | 0.7294 | 0.4101 | | No log | 3.0448 | 204 | 0.4165 | 0.7388 | 0.4142 | | No log | 3.0746 | 206 | 0.3928 | 0.7287 | 0.3903 | | No log | 3.1045 | 208 | 0.3558 | 0.7149 | 0.3527 | | No log | 3.1343 | 210 | 0.3375 | 0.6970 | 0.3341 | | No log | 3.1642 | 212 | 0.3306 | 0.6716 | 0.3272 | | No log | 3.1940 | 214 | 0.3266 | 0.6814 | 0.3234 | | No log | 3.2239 | 216 | 0.3316 | 0.7124 | 0.3287 | | No log | 3.2537 | 218 | 0.3329 | 0.7182 | 0.3301 | | No log | 3.2836 | 220 | 0.3393 | 0.7243 | 0.3367 | | No log | 3.3134 | 222 | 0.3680 | 0.7327 | 0.3658 | | No log | 3.3433 | 224 | 0.3882 | 0.7483 | 0.3862 | | No log | 3.3731 | 226 | 0.3790 | 0.7355 | 0.3770 | | No log | 3.4030 | 228 | 0.3458 | 0.7312 | 0.3432 | | No log | 3.4328 | 230 | 0.3360 | 0.7308 | 0.3333 | | No log | 3.4627 | 232 | 0.3497 | 0.7328 | 0.3473 | | No log | 3.4925 | 234 | 0.3563 | 0.7328 | 0.3539 | | No log | 3.5224 | 236 | 0.3456 | 0.7253 | 0.3428 | | No log | 3.5522 | 238 | 0.3342 | 0.7016 | 0.3307 | | No log | 3.5821 | 240 | 0.3457 | 0.6459 | 0.3418 | | No log | 3.6119 | 242 | 0.3569 | 0.6425 | 0.3529 | | No log | 3.6418 | 244 | 0.3518 | 0.6463 | 0.3478 | | No log | 3.6716 | 246 | 0.3437 | 0.6589 | 0.3400 | | No log | 3.7015 | 248 | 0.3393 | 0.6991 | 0.3360 | | No log | 3.7313 | 250 | 0.3454 | 0.7089 | 0.3423 | | No log | 3.7612 | 252 | 0.3429 | 0.7128 | 0.3398 | | No log | 3.7910 | 254 | 0.3331 | 0.7014 | 0.3299 | | No log | 3.8209 | 256 | 0.3270 | 0.6810 | 0.3237 | | No log | 3.8507 | 258 | 0.3264 | 0.6792 | 0.3231 | | No log | 3.8806 | 260 | 0.3264 | 0.6870 | 0.3233 | | No log | 3.9104 | 262 | 0.3303 | 0.6999 | 0.3274 | | No log | 3.9403 | 264 | 0.3387 | 0.7057 | 0.3359 | | No log | 3.9701 | 266 | 0.3403 | 0.7095 | 0.3376 | | No log | 4.0 | 268 | 0.3329 | 0.7027 | 0.3301 | | No log | 4.0299 | 270 | 0.3325 | 0.6986 | 0.3296 | | No log | 4.0597 | 272 | 0.3292 | 0.6847 | 0.3262 | | No log | 4.0896 | 274 | 0.3278 | 0.6556 | 0.3247 | | No log | 4.1194 | 276 | 0.3274 | 0.6594 | 0.3243 | | No log | 4.1493 | 278 | 0.3292 | 0.7012 | 0.3262 | | No log | 4.1791 | 280 | 0.3352 | 0.7056 | 0.3323 | | No log | 4.2090 | 282 | 0.3480 | 0.7221 | 0.3453 | | No log | 4.2388 | 284 | 0.3556 | 0.7342 | 0.3530 | | No log | 4.2687 | 286 | 0.3560 | 0.7362 | 0.3534 | | No log | 4.2985 | 288 | 0.3612 | 0.7380 | 0.3586 | | No log | 4.3284 | 290 | 0.3578 | 0.7358 | 0.3552 | | No log | 4.3582 | 292 | 0.3490 | 0.7266 | 0.3463 | | No log | 4.3881 | 294 | 0.3390 | 0.7071 | 0.3360 | | No log | 4.4179 | 296 | 0.3362 | 0.6972 | 0.3331 | | No log | 4.4478 | 298 | 0.3333 | 0.7030 | 0.3301 | | No log | 4.4776 | 300 | 0.3326 | 0.6942 | 0.3293 | | No log | 4.5075 | 302 | 0.3322 | 0.6888 | 0.3288 | | No log | 4.5373 | 304 | 0.3335 | 0.6910 | 0.3301 | | No log | 4.5672 | 306 | 0.3350 | 0.6892 | 0.3316 | | No log | 4.5970 | 308 | 0.3375 | 0.7055 | 0.3343 | | No log | 4.6269 | 310 | 0.3425 | 0.7088 | 0.3395 | | No log | 4.6567 | 312 | 0.3475 | 0.7169 | 0.3447 | | No log | 4.6866 | 314 | 0.3502 | 0.7266 | 0.3474 | | No log | 4.7164 | 316 | 0.3509 | 0.7285 | 0.3482 | | No log | 4.7463 | 318 | 0.3496 | 0.7266 | 0.3469 | | No log | 4.7761 | 320 | 0.3480 | 0.7266 | 0.3452 | | No log | 4.8060 | 322 | 0.3461 | 0.7179 | 0.3433 | | No log | 4.8358 | 324 | 0.3440 | 0.7159 | 0.3412 | | No log | 4.8657 | 326 | 0.3411 | 0.7152 | 0.3383 | | No log | 4.8955 | 328 | 0.3397 | 0.7165 | 0.3368 | | No log | 4.9254 | 330 | 0.3389 | 0.7177 | 0.3360 | | No log | 4.9552 | 332 | 0.3382 | 0.7134 | 0.3353 | | No log | 4.9851 | 334 | 0.3376 | 0.7136 | 0.3347 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1