--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer model-index: - name: bert_baseline_prompt_adherence_task6_fold0 results: [] --- # bert_baseline_prompt_adherence_task6_fold0 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: 0.3214 - Qwk: 0.7303 - Mse: 0.3214 ## 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 | 0.0294 | 2 | 1.9024 | 0.0 | 1.9024 | | No log | 0.0588 | 4 | 1.6433 | -0.0376 | 1.6433 | | No log | 0.0882 | 6 | 1.4580 | -0.0106 | 1.4580 | | No log | 0.1176 | 8 | 1.1646 | 0.0061 | 1.1646 | | No log | 0.1471 | 10 | 0.9673 | 0.0061 | 0.9673 | | No log | 0.1765 | 12 | 0.8529 | 0.0061 | 0.8529 | | No log | 0.2059 | 14 | 0.8166 | 0.1658 | 0.8166 | | No log | 0.2353 | 16 | 0.8014 | 0.0430 | 0.8014 | | No log | 0.2647 | 18 | 0.7507 | 0.2430 | 0.7507 | | No log | 0.2941 | 20 | 0.6900 | 0.3980 | 0.6900 | | No log | 0.3235 | 22 | 0.6517 | 0.4566 | 0.6517 | | No log | 0.3529 | 24 | 0.6213 | 0.4542 | 0.6213 | | No log | 0.3824 | 26 | 0.5886 | 0.3707 | 0.5886 | | No log | 0.4118 | 28 | 0.5738 | 0.3586 | 0.5738 | | No log | 0.4412 | 30 | 0.5467 | 0.4152 | 0.5467 | | No log | 0.4706 | 32 | 0.5428 | 0.3809 | 0.5428 | | No log | 0.5 | 34 | 0.4846 | 0.4818 | 0.4846 | | No log | 0.5294 | 36 | 0.4315 | 0.4939 | 0.4315 | | No log | 0.5588 | 38 | 0.4162 | 0.5244 | 0.4162 | | No log | 0.5882 | 40 | 0.3789 | 0.5985 | 0.3789 | | No log | 0.6176 | 42 | 0.3613 | 0.5956 | 0.3613 | | No log | 0.6471 | 44 | 0.3505 | 0.5933 | 0.3505 | | No log | 0.6765 | 46 | 0.3581 | 0.6419 | 0.3581 | | No log | 0.7059 | 48 | 0.3340 | 0.6165 | 0.3340 | | No log | 0.7353 | 50 | 0.3323 | 0.6190 | 0.3323 | | No log | 0.7647 | 52 | 0.3927 | 0.7193 | 0.3927 | | No log | 0.7941 | 54 | 0.4713 | 0.7380 | 0.4713 | | No log | 0.8235 | 56 | 0.3768 | 0.6750 | 0.3768 | | No log | 0.8529 | 58 | 0.3531 | 0.5832 | 0.3531 | | No log | 0.8824 | 60 | 0.4043 | 0.5390 | 0.4043 | | No log | 0.9118 | 62 | 0.3447 | 0.5850 | 0.3447 | | No log | 0.9412 | 64 | 0.3618 | 0.6575 | 0.3618 | | No log | 0.9706 | 66 | 0.3572 | 0.6539 | 0.3572 | | No log | 1.0 | 68 | 0.3949 | 0.6765 | 0.3949 | | No log | 1.0294 | 70 | 0.3686 | 0.6660 | 0.3686 | | No log | 1.0588 | 72 | 0.3326 | 0.6180 | 0.3326 | | No log | 1.0882 | 74 | 0.3520 | 0.5823 | 0.3520 | | No log | 1.1176 | 76 | 0.3303 | 0.6063 | 0.3303 | | No log | 1.1471 | 78 | 0.3475 | 0.6601 | 0.3475 | | No log | 1.1765 | 80 | 0.3729 | 0.6752 | 0.3729 | | No log | 1.2059 | 82 | 0.3223 | 0.6533 | 0.3223 | | No log | 1.2353 | 84 | 0.3665 | 0.5588 | 0.3665 | | No log | 1.2647 | 86 | 0.3824 | 0.5485 | 0.3824 | | No log | 1.2941 | 88 | 0.3093 | 0.6230 | 0.3093 | | No log | 1.3235 | 90 | 0.3160 | 0.6514 | 0.3160 | | No log | 1.3529 | 92 | 0.3212 | 0.6588 | 0.3212 | | No log | 1.3824 | 94 | 0.3075 | 0.6459 | 0.3075 | | No log | 1.4118 | 96 | 0.3146 | 0.6141 | 0.3146 | | No log | 1.4412 | 98 | 0.3140 | 0.6051 | 0.3140 | | No log | 1.4706 | 100 | 0.2968 | 0.6409 | 0.2968 | | No log | 1.5 | 102 | 0.3146 | 0.6665 | 0.3146 | | No log | 1.5294 | 104 | 0.3225 | 0.6744 | 0.3225 | | No log | 1.5588 | 106 | 0.2963 | 0.6660 | 0.2963 | | No log | 1.5882 | 108 | 0.3015 | 0.6202 | 0.3015 | | No log | 1.6176 | 110 | 0.3128 | 0.6101 | 0.3128 | | No log | 1.6471 | 112 | 0.2930 | 0.6835 | 0.2930 | | No log | 1.6765 | 114 | 0.3211 | 0.7509 | 0.3211 | | No log | 1.7059 | 116 | 0.3024 | 0.7304 | 0.3024 | | No log | 1.7353 | 118 | 0.2830 | 0.6659 | 0.2830 | | No log | 1.7647 | 120 | 0.2853 | 0.6455 | 0.2853 | | No log | 1.7941 | 122 | 0.2959 | 0.7087 | 0.2959 | | No log | 1.8235 | 124 | 0.3210 | 0.7212 | 0.3210 | | No log | 1.8529 | 126 | 0.3687 | 0.7455 | 0.3687 | | No log | 1.8824 | 128 | 0.3281 | 0.7077 | 0.3281 | | No log | 1.9118 | 130 | 0.2932 | 0.6235 | 0.2932 | | No log | 1.9412 | 132 | 0.3188 | 0.5858 | 0.3188 | | No log | 1.9706 | 134 | 0.3395 | 0.5668 | 0.3395 | | No log | 2.0 | 136 | 0.3031 | 0.5998 | 0.3031 | | No log | 2.0294 | 138 | 0.2965 | 0.6165 | 0.2965 | | No log | 2.0588 | 140 | 0.2870 | 0.6407 | 0.2870 | | No log | 2.0882 | 142 | 0.2971 | 0.6951 | 0.2971 | | No log | 2.1176 | 144 | 0.3088 | 0.7183 | 0.3088 | | No log | 2.1471 | 146 | 0.2953 | 0.6786 | 0.2953 | | No log | 2.1765 | 148 | 0.3026 | 0.6304 | 0.3026 | | No log | 2.2059 | 150 | 0.2990 | 0.6499 | 0.2990 | | No log | 2.2353 | 152 | 0.3100 | 0.6986 | 0.3100 | | No log | 2.2647 | 154 | 0.3029 | 0.6558 | 0.3029 | | No log | 2.2941 | 156 | 0.3094 | 0.6451 | 0.3094 | | No log | 2.3235 | 158 | 0.3189 | 0.6789 | 0.3189 | | No log | 2.3529 | 160 | 0.3296 | 0.7205 | 0.3296 | | No log | 2.3824 | 162 | 0.3857 | 0.7668 | 0.3857 | | No log | 2.4118 | 164 | 0.3847 | 0.7738 | 0.3847 | | No log | 2.4412 | 166 | 0.3288 | 0.7196 | 0.3288 | | No log | 2.4706 | 168 | 0.3127 | 0.6857 | 0.3127 | | No log | 2.5 | 170 | 0.3142 | 0.6321 | 0.3142 | | No log | 2.5294 | 172 | 0.2971 | 0.6805 | 0.2971 | | No log | 2.5588 | 174 | 0.2919 | 0.6809 | 0.2919 | | No log | 2.5882 | 176 | 0.2883 | 0.6832 | 0.2883 | | No log | 2.6176 | 178 | 0.2908 | 0.7045 | 0.2908 | | No log | 2.6471 | 180 | 0.3183 | 0.7436 | 0.3183 | | No log | 2.6765 | 182 | 0.3840 | 0.7915 | 0.3840 | | No log | 2.7059 | 184 | 0.4421 | 0.8013 | 0.4421 | | No log | 2.7353 | 186 | 0.4693 | 0.8022 | 0.4693 | | No log | 2.7647 | 188 | 0.3933 | 0.7893 | 0.3933 | | No log | 2.7941 | 190 | 0.3167 | 0.7355 | 0.3167 | | No log | 2.8235 | 192 | 0.3053 | 0.6908 | 0.3053 | | No log | 2.8529 | 194 | 0.3028 | 0.6759 | 0.3028 | | No log | 2.8824 | 196 | 0.2999 | 0.6618 | 0.2999 | | No log | 2.9118 | 198 | 0.2966 | 0.6730 | 0.2966 | | No log | 2.9412 | 200 | 0.3041 | 0.6986 | 0.3041 | | No log | 2.9706 | 202 | 0.3492 | 0.7601 | 0.3492 | | No log | 3.0 | 204 | 0.3807 | 0.7895 | 0.3807 | | No log | 3.0294 | 206 | 0.3448 | 0.7616 | 0.3448 | | No log | 3.0588 | 208 | 0.2938 | 0.7110 | 0.2938 | | No log | 3.0882 | 210 | 0.2832 | 0.6748 | 0.2832 | | No log | 3.1176 | 212 | 0.2984 | 0.6126 | 0.2984 | | No log | 3.1471 | 214 | 0.3016 | 0.6126 | 0.3016 | | No log | 3.1765 | 216 | 0.2831 | 0.6494 | 0.2831 | | No log | 3.2059 | 218 | 0.2895 | 0.7158 | 0.2895 | | No log | 3.2353 | 220 | 0.3130 | 0.7480 | 0.3130 | | No log | 3.2647 | 222 | 0.3255 | 0.7594 | 0.3255 | | No log | 3.2941 | 224 | 0.3160 | 0.7489 | 0.3160 | | No log | 3.3235 | 226 | 0.3049 | 0.7209 | 0.3049 | | No log | 3.3529 | 228 | 0.2995 | 0.7190 | 0.2995 | | No log | 3.3824 | 230 | 0.3001 | 0.7290 | 0.3001 | | No log | 3.4118 | 232 | 0.3108 | 0.7388 | 0.3108 | | No log | 3.4412 | 234 | 0.3102 | 0.7363 | 0.3102 | | No log | 3.4706 | 236 | 0.3025 | 0.7162 | 0.3025 | | No log | 3.5 | 238 | 0.2998 | 0.7035 | 0.2998 | | No log | 3.5294 | 240 | 0.3007 | 0.7119 | 0.3007 | | No log | 3.5588 | 242 | 0.3111 | 0.7343 | 0.3111 | | No log | 3.5882 | 244 | 0.3146 | 0.7327 | 0.3146 | | No log | 3.6176 | 246 | 0.2978 | 0.6769 | 0.2978 | | No log | 3.6471 | 248 | 0.3000 | 0.6276 | 0.3000 | | No log | 3.6765 | 250 | 0.3051 | 0.6143 | 0.3051 | | No log | 3.7059 | 252 | 0.3019 | 0.6351 | 0.3019 | | No log | 3.7353 | 254 | 0.3106 | 0.7052 | 0.3106 | | No log | 3.7647 | 256 | 0.3545 | 0.7642 | 0.3545 | | No log | 3.7941 | 258 | 0.3953 | 0.7867 | 0.3953 | | No log | 3.8235 | 260 | 0.3837 | 0.7747 | 0.3837 | | No log | 3.8529 | 262 | 0.3480 | 0.7461 | 0.3480 | | No log | 3.8824 | 264 | 0.3239 | 0.7146 | 0.3239 | | No log | 3.9118 | 266 | 0.3220 | 0.6736 | 0.3220 | | No log | 3.9412 | 268 | 0.3211 | 0.6705 | 0.3211 | | No log | 3.9706 | 270 | 0.3251 | 0.7223 | 0.3251 | | No log | 4.0 | 272 | 0.3272 | 0.7300 | 0.3272 | | No log | 4.0294 | 274 | 0.3318 | 0.7415 | 0.3318 | | No log | 4.0588 | 276 | 0.3398 | 0.7610 | 0.3398 | | No log | 4.0882 | 278 | 0.3381 | 0.7626 | 0.3381 | | No log | 4.1176 | 280 | 0.3233 | 0.7381 | 0.3233 | | No log | 4.1471 | 282 | 0.3073 | 0.7105 | 0.3073 | | No log | 4.1765 | 284 | 0.3043 | 0.6954 | 0.3043 | | No log | 4.2059 | 286 | 0.3047 | 0.6717 | 0.3047 | | No log | 4.2353 | 288 | 0.3047 | 0.6814 | 0.3047 | | No log | 4.2647 | 290 | 0.3073 | 0.7041 | 0.3073 | | No log | 4.2941 | 292 | 0.3091 | 0.7089 | 0.3091 | | No log | 4.3235 | 294 | 0.3070 | 0.7084 | 0.3070 | | No log | 4.3529 | 296 | 0.3104 | 0.7163 | 0.3104 | | No log | 4.3824 | 298 | 0.3123 | 0.7197 | 0.3123 | | No log | 4.4118 | 300 | 0.3140 | 0.7269 | 0.3140 | | No log | 4.4412 | 302 | 0.3164 | 0.7296 | 0.3164 | | No log | 4.4706 | 304 | 0.3152 | 0.7273 | 0.3152 | | No log | 4.5 | 306 | 0.3091 | 0.7130 | 0.3091 | | No log | 4.5294 | 308 | 0.3050 | 0.7072 | 0.3050 | | No log | 4.5588 | 310 | 0.3048 | 0.7058 | 0.3048 | | No log | 4.5882 | 312 | 0.3085 | 0.7116 | 0.3085 | | No log | 4.6176 | 314 | 0.3132 | 0.7236 | 0.3132 | | No log | 4.6471 | 316 | 0.3216 | 0.7357 | 0.3216 | | No log | 4.6765 | 318 | 0.3286 | 0.7448 | 0.3286 | | No log | 4.7059 | 320 | 0.3381 | 0.7533 | 0.3381 | | No log | 4.7353 | 322 | 0.3398 | 0.7547 | 0.3398 | | No log | 4.7647 | 324 | 0.3416 | 0.7532 | 0.3416 | | No log | 4.7941 | 326 | 0.3410 | 0.7532 | 0.3410 | | No log | 4.8235 | 328 | 0.3387 | 0.7493 | 0.3387 | | No log | 4.8529 | 330 | 0.3357 | 0.7492 | 0.3357 | | No log | 4.8824 | 332 | 0.3319 | 0.7451 | 0.3319 | | No log | 4.9118 | 334 | 0.3279 | 0.7365 | 0.3279 | | No log | 4.9412 | 336 | 0.3242 | 0.7330 | 0.3242 | | No log | 4.9706 | 338 | 0.3222 | 0.7336 | 0.3222 | | No log | 5.0 | 340 | 0.3214 | 0.7303 | 0.3214 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1