bert_baseline_prompt_adherence_task3_fold1
This model is a fine-tuned version of google-bert/bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3261
- Qwk: 0.7419
- Mse: 0.3261
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 | 0.0308 | 2 | 0.9807 | 0.1780 | 0.9807 |
No log | 0.0615 | 4 | 0.8190 | 0.0 | 0.8190 |
No log | 0.0923 | 6 | 0.7346 | 0.0 | 0.7346 |
No log | 0.1231 | 8 | 0.7097 | 0.0 | 0.7097 |
No log | 0.1538 | 10 | 0.7013 | 0.0 | 0.7013 |
No log | 0.1846 | 12 | 0.6926 | 0.0 | 0.6926 |
No log | 0.2154 | 14 | 0.6150 | 0.2050 | 0.6150 |
No log | 0.2462 | 16 | 0.5746 | 0.2776 | 0.5746 |
No log | 0.2769 | 18 | 0.5175 | 0.4580 | 0.5175 |
No log | 0.3077 | 20 | 0.5071 | 0.5008 | 0.5071 |
No log | 0.3385 | 22 | 0.4892 | 0.5513 | 0.4892 |
No log | 0.3692 | 24 | 0.4671 | 0.5600 | 0.4671 |
No log | 0.4 | 26 | 0.4385 | 0.5743 | 0.4385 |
No log | 0.4308 | 28 | 0.4164 | 0.5687 | 0.4164 |
No log | 0.4615 | 30 | 0.4113 | 0.6369 | 0.4113 |
No log | 0.4923 | 32 | 0.4986 | 0.6548 | 0.4986 |
No log | 0.5231 | 34 | 0.5170 | 0.6308 | 0.5170 |
No log | 0.5538 | 36 | 0.4885 | 0.5581 | 0.4885 |
No log | 0.5846 | 38 | 0.4618 | 0.5734 | 0.4618 |
No log | 0.6154 | 40 | 0.4343 | 0.3746 | 0.4343 |
No log | 0.6462 | 42 | 0.4788 | 0.3393 | 0.4788 |
No log | 0.6769 | 44 | 0.4827 | 0.3710 | 0.4827 |
No log | 0.7077 | 46 | 0.3803 | 0.5139 | 0.3803 |
No log | 0.7385 | 48 | 0.3696 | 0.6756 | 0.3696 |
No log | 0.7692 | 50 | 0.3869 | 0.6732 | 0.3869 |
No log | 0.8 | 52 | 0.3724 | 0.6508 | 0.3724 |
No log | 0.8308 | 54 | 0.3556 | 0.6402 | 0.3556 |
No log | 0.8615 | 56 | 0.3590 | 0.5721 | 0.3590 |
No log | 0.8923 | 58 | 0.3473 | 0.5751 | 0.3473 |
No log | 0.9231 | 60 | 0.3341 | 0.6211 | 0.3341 |
No log | 0.9538 | 62 | 0.3796 | 0.6945 | 0.3796 |
No log | 0.9846 | 64 | 0.4263 | 0.7143 | 0.4263 |
No log | 1.0154 | 66 | 0.4252 | 0.7207 | 0.4252 |
No log | 1.0462 | 68 | 0.4305 | 0.7265 | 0.4305 |
No log | 1.0769 | 70 | 0.3484 | 0.7072 | 0.3484 |
No log | 1.1077 | 72 | 0.3248 | 0.6605 | 0.3248 |
No log | 1.1385 | 74 | 0.3408 | 0.6993 | 0.3408 |
No log | 1.1692 | 76 | 0.3358 | 0.6859 | 0.3358 |
No log | 1.2 | 78 | 0.3628 | 0.7167 | 0.3628 |
No log | 1.2308 | 80 | 0.3367 | 0.7063 | 0.3367 |
No log | 1.2615 | 82 | 0.3377 | 0.7016 | 0.3377 |
No log | 1.2923 | 84 | 0.3682 | 0.7021 | 0.3682 |
No log | 1.3231 | 86 | 0.3592 | 0.6835 | 0.3592 |
No log | 1.3538 | 88 | 0.3456 | 0.6733 | 0.3456 |
No log | 1.3846 | 90 | 0.3377 | 0.6864 | 0.3377 |
No log | 1.4154 | 92 | 0.3212 | 0.6771 | 0.3212 |
No log | 1.4462 | 94 | 0.3114 | 0.6704 | 0.3114 |
No log | 1.4769 | 96 | 0.3247 | 0.6922 | 0.3247 |
No log | 1.5077 | 98 | 0.3329 | 0.7037 | 0.3329 |
No log | 1.5385 | 100 | 0.3875 | 0.7406 | 0.3875 |
No log | 1.5692 | 102 | 0.3673 | 0.7270 | 0.3673 |
No log | 1.6 | 104 | 0.3376 | 0.7079 | 0.3376 |
No log | 1.6308 | 106 | 0.3260 | 0.6641 | 0.3260 |
No log | 1.6615 | 108 | 0.3205 | 0.6688 | 0.3205 |
No log | 1.6923 | 110 | 0.3265 | 0.7115 | 0.3265 |
No log | 1.7231 | 112 | 0.4055 | 0.7452 | 0.4055 |
No log | 1.7538 | 114 | 0.4056 | 0.7501 | 0.4056 |
No log | 1.7846 | 116 | 0.3271 | 0.7345 | 0.3271 |
No log | 1.8154 | 118 | 0.2897 | 0.6549 | 0.2897 |
No log | 1.8462 | 120 | 0.2970 | 0.5891 | 0.2970 |
No log | 1.8769 | 122 | 0.3028 | 0.6598 | 0.3028 |
No log | 1.9077 | 124 | 0.3424 | 0.7042 | 0.3424 |
No log | 1.9385 | 126 | 0.3600 | 0.7182 | 0.3600 |
No log | 1.9692 | 128 | 0.3537 | 0.7192 | 0.3537 |
No log | 2.0 | 130 | 0.3151 | 0.7111 | 0.3151 |
No log | 2.0308 | 132 | 0.2889 | 0.6887 | 0.2889 |
No log | 2.0615 | 134 | 0.2812 | 0.6636 | 0.2812 |
No log | 2.0923 | 136 | 0.2804 | 0.6723 | 0.2804 |
No log | 2.1231 | 138 | 0.2997 | 0.7178 | 0.2997 |
No log | 2.1538 | 140 | 0.3040 | 0.7180 | 0.3040 |
No log | 2.1846 | 142 | 0.2868 | 0.6978 | 0.2868 |
No log | 2.2154 | 144 | 0.3064 | 0.7133 | 0.3064 |
No log | 2.2462 | 146 | 0.3242 | 0.7393 | 0.3242 |
No log | 2.2769 | 148 | 0.3634 | 0.7441 | 0.3634 |
No log | 2.3077 | 150 | 0.3306 | 0.7538 | 0.3306 |
No log | 2.3385 | 152 | 0.2919 | 0.6979 | 0.2919 |
No log | 2.3692 | 154 | 0.2832 | 0.6896 | 0.2832 |
No log | 2.4 | 156 | 0.2814 | 0.6919 | 0.2814 |
No log | 2.4308 | 158 | 0.3092 | 0.7323 | 0.3092 |
No log | 2.4615 | 160 | 0.3124 | 0.7312 | 0.3124 |
No log | 2.4923 | 162 | 0.2931 | 0.6999 | 0.2931 |
No log | 2.5231 | 164 | 0.2997 | 0.7057 | 0.2997 |
No log | 2.5538 | 166 | 0.2948 | 0.6755 | 0.2948 |
No log | 2.5846 | 168 | 0.2973 | 0.6602 | 0.2973 |
No log | 2.6154 | 170 | 0.3109 | 0.6931 | 0.3109 |
No log | 2.6462 | 172 | 0.3195 | 0.7091 | 0.3195 |
No log | 2.6769 | 174 | 0.3614 | 0.7301 | 0.3614 |
No log | 2.7077 | 176 | 0.3865 | 0.7458 | 0.3865 |
No log | 2.7385 | 178 | 0.3807 | 0.7437 | 0.3807 |
No log | 2.7692 | 180 | 0.3296 | 0.7136 | 0.3296 |
No log | 2.8 | 182 | 0.3040 | 0.6771 | 0.3040 |
No log | 2.8308 | 184 | 0.3004 | 0.6553 | 0.3004 |
No log | 2.8615 | 186 | 0.3005 | 0.6671 | 0.3005 |
No log | 2.8923 | 188 | 0.3144 | 0.7023 | 0.3144 |
No log | 2.9231 | 190 | 0.3337 | 0.7186 | 0.3337 |
No log | 2.9538 | 192 | 0.3574 | 0.7361 | 0.3574 |
No log | 2.9846 | 194 | 0.3635 | 0.7393 | 0.3635 |
No log | 3.0154 | 196 | 0.3718 | 0.7456 | 0.3718 |
No log | 3.0462 | 198 | 0.3520 | 0.7369 | 0.3520 |
No log | 3.0769 | 200 | 0.3182 | 0.7152 | 0.3182 |
No log | 3.1077 | 202 | 0.2977 | 0.6968 | 0.2977 |
No log | 3.1385 | 204 | 0.3083 | 0.7107 | 0.3083 |
No log | 3.1692 | 206 | 0.3088 | 0.7154 | 0.3088 |
No log | 3.2 | 208 | 0.3043 | 0.7046 | 0.3043 |
No log | 3.2308 | 210 | 0.3116 | 0.7165 | 0.3116 |
No log | 3.2615 | 212 | 0.3249 | 0.7280 | 0.3249 |
No log | 3.2923 | 214 | 0.3299 | 0.7237 | 0.3299 |
No log | 3.3231 | 216 | 0.3162 | 0.7211 | 0.3162 |
No log | 3.3538 | 218 | 0.3099 | 0.7051 | 0.3099 |
No log | 3.3846 | 220 | 0.3210 | 0.7122 | 0.3210 |
No log | 3.4154 | 222 | 0.3211 | 0.7014 | 0.3211 |
No log | 3.4462 | 224 | 0.3307 | 0.7150 | 0.3307 |
No log | 3.4769 | 226 | 0.3494 | 0.7385 | 0.3494 |
No log | 3.5077 | 228 | 0.3586 | 0.7349 | 0.3586 |
No log | 3.5385 | 230 | 0.3637 | 0.7372 | 0.3637 |
No log | 3.5692 | 232 | 0.3602 | 0.7341 | 0.3602 |
No log | 3.6 | 234 | 0.3325 | 0.7104 | 0.3325 |
No log | 3.6308 | 236 | 0.3106 | 0.6508 | 0.3106 |
No log | 3.6615 | 238 | 0.3102 | 0.6331 | 0.3102 |
No log | 3.6923 | 240 | 0.3122 | 0.6795 | 0.3122 |
No log | 3.7231 | 242 | 0.3664 | 0.7351 | 0.3664 |
No log | 3.7538 | 244 | 0.4637 | 0.7692 | 0.4637 |
No log | 3.7846 | 246 | 0.4890 | 0.7653 | 0.4890 |
No log | 3.8154 | 248 | 0.4399 | 0.7707 | 0.4399 |
No log | 3.8462 | 250 | 0.3669 | 0.7570 | 0.3669 |
No log | 3.8769 | 252 | 0.3203 | 0.7198 | 0.3203 |
No log | 3.9077 | 254 | 0.3120 | 0.7160 | 0.3120 |
No log | 3.9385 | 256 | 0.3250 | 0.7464 | 0.3250 |
No log | 3.9692 | 258 | 0.3259 | 0.7473 | 0.3259 |
No log | 4.0 | 260 | 0.3151 | 0.7323 | 0.3151 |
No log | 4.0308 | 262 | 0.3094 | 0.7229 | 0.3094 |
No log | 4.0615 | 264 | 0.3087 | 0.7171 | 0.3087 |
No log | 4.0923 | 266 | 0.3251 | 0.7373 | 0.3251 |
No log | 4.1231 | 268 | 0.3589 | 0.7566 | 0.3589 |
No log | 4.1538 | 270 | 0.3822 | 0.7659 | 0.3822 |
No log | 4.1846 | 272 | 0.3732 | 0.7540 | 0.3732 |
No log | 4.2154 | 274 | 0.3398 | 0.7494 | 0.3398 |
No log | 4.2462 | 276 | 0.3140 | 0.7166 | 0.3140 |
No log | 4.2769 | 278 | 0.3087 | 0.6987 | 0.3087 |
No log | 4.3077 | 280 | 0.3153 | 0.7117 | 0.3153 |
No log | 4.3385 | 282 | 0.3321 | 0.7500 | 0.3321 |
No log | 4.3692 | 284 | 0.3502 | 0.7553 | 0.3502 |
No log | 4.4 | 286 | 0.3540 | 0.7564 | 0.3540 |
No log | 4.4308 | 288 | 0.3416 | 0.7550 | 0.3416 |
No log | 4.4615 | 290 | 0.3188 | 0.7331 | 0.3188 |
No log | 4.4923 | 292 | 0.3005 | 0.6814 | 0.3005 |
No log | 4.5231 | 294 | 0.2955 | 0.6704 | 0.2955 |
No log | 4.5538 | 296 | 0.2953 | 0.6723 | 0.2953 |
No log | 4.5846 | 298 | 0.2986 | 0.6795 | 0.2986 |
No log | 4.6154 | 300 | 0.3092 | 0.7144 | 0.3092 |
No log | 4.6462 | 302 | 0.3261 | 0.7374 | 0.3261 |
No log | 4.6769 | 304 | 0.3347 | 0.7471 | 0.3347 |
No log | 4.7077 | 306 | 0.3362 | 0.7471 | 0.3362 |
No log | 4.7385 | 308 | 0.3357 | 0.7471 | 0.3357 |
No log | 4.7692 | 310 | 0.3384 | 0.7490 | 0.3384 |
No log | 4.8 | 312 | 0.3362 | 0.7490 | 0.3362 |
No log | 4.8308 | 314 | 0.3300 | 0.7449 | 0.3300 |
No log | 4.8615 | 316 | 0.3257 | 0.7419 | 0.3257 |
No log | 4.8923 | 318 | 0.3250 | 0.7419 | 0.3250 |
No log | 4.9231 | 320 | 0.3240 | 0.7419 | 0.3240 |
No log | 4.9538 | 322 | 0.3250 | 0.7419 | 0.3250 |
No log | 4.9846 | 324 | 0.3261 | 0.7419 | 0.3261 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for salbatarni/bert_baseline_prompt_adherence_task3_fold1
Base model
google-bert/bert-base-cased