<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
bert-large-uncased-sst-2-32-13
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6858
- Accuracy: 0.8906
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 150
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 2 | 0.7579 | 0.4844 |
| No log | 2.0 | 4 | 0.7556 | 0.4844 |
| No log | 3.0 | 6 | 0.7502 | 0.4844 |
| No log | 4.0 | 8 | 0.7421 | 0.4844 |
| 0.7491 | 5.0 | 10 | 0.7348 | 0.5 |
| 0.7491 | 6.0 | 12 | 0.7286 | 0.5 |
| 0.7491 | 7.0 | 14 | 0.7228 | 0.5 |
| 0.7491 | 8.0 | 16 | 0.7177 | 0.5 |
| 0.7491 | 9.0 | 18 | 0.7136 | 0.5 |
| 0.7136 | 10.0 | 20 | 0.7095 | 0.5 |
| 0.7136 | 11.0 | 22 | 0.7049 | 0.5 |
| 0.7136 | 12.0 | 24 | 0.6993 | 0.5 |
| 0.7136 | 13.0 | 26 | 0.6926 | 0.5 |
| 0.7136 | 14.0 | 28 | 0.6860 | 0.5 |
| 0.6781 | 15.0 | 30 | 0.6777 | 0.5 |
| 0.6781 | 16.0 | 32 | 0.6691 | 0.5 |
| 0.6781 | 17.0 | 34 | 0.6605 | 0.5312 |
| 0.6781 | 18.0 | 36 | 0.6524 | 0.5156 |
| 0.6781 | 19.0 | 38 | 0.6428 | 0.5469 |
| 0.584 | 20.0 | 40 | 0.6239 | 0.6562 |
| 0.584 | 21.0 | 42 | 0.6124 | 0.6719 |
| 0.584 | 22.0 | 44 | 0.6053 | 0.6562 |
| 0.584 | 23.0 | 46 | 0.5981 | 0.6875 |
| 0.584 | 24.0 | 48 | 0.5707 | 0.7031 |
| 0.439 | 25.0 | 50 | 0.5284 | 0.7656 |
| 0.439 | 26.0 | 52 | 0.5125 | 0.7812 |
| 0.439 | 27.0 | 54 | 0.5117 | 0.75 |
| 0.439 | 28.0 | 56 | 0.4922 | 0.7656 |
| 0.439 | 29.0 | 58 | 0.4698 | 0.7812 |
| 0.2661 | 30.0 | 60 | 0.4417 | 0.7656 |
| 0.2661 | 31.0 | 62 | 0.4234 | 0.7812 |
| 0.2661 | 32.0 | 64 | 0.4309 | 0.7656 |
| 0.2661 | 33.0 | 66 | 0.4503 | 0.7812 |
| 0.2661 | 34.0 | 68 | 0.4344 | 0.8125 |
| 0.1 | 35.0 | 70 | 0.3772 | 0.8281 |
| 0.1 | 36.0 | 72 | 0.3475 | 0.875 |
| 0.1 | 37.0 | 74 | 0.3404 | 0.875 |
| 0.1 | 38.0 | 76 | 0.3334 | 0.8906 |
| 0.1 | 39.0 | 78 | 0.3313 | 0.9062 |
| 0.033 | 40.0 | 80 | 0.3315 | 0.9062 |
| 0.033 | 41.0 | 82 | 0.3340 | 0.9062 |
| 0.033 | 42.0 | 84 | 0.3364 | 0.9062 |
| 0.033 | 43.0 | 86 | 0.3412 | 0.9062 |
| 0.033 | 44.0 | 88 | 0.3509 | 0.8906 |
| 0.0142 | 45.0 | 90 | 0.3588 | 0.875 |
| 0.0142 | 46.0 | 92 | 0.3675 | 0.875 |
| 0.0142 | 47.0 | 94 | 0.3788 | 0.875 |
| 0.0142 | 48.0 | 96 | 0.3957 | 0.875 |
| 0.0142 | 49.0 | 98 | 0.4137 | 0.875 |
| 0.0081 | 50.0 | 100 | 0.4338 | 0.875 |
| 0.0081 | 51.0 | 102 | 0.4507 | 0.875 |
| 0.0081 | 52.0 | 104 | 0.4645 | 0.8906 |
| 0.0081 | 53.0 | 106 | 0.4767 | 0.8906 |
| 0.0081 | 54.0 | 108 | 0.4875 | 0.8906 |
| 0.0048 | 55.0 | 110 | 0.4977 | 0.8906 |
| 0.0048 | 56.0 | 112 | 0.5052 | 0.8906 |
| 0.0048 | 57.0 | 114 | 0.5082 | 0.8906 |
| 0.0048 | 58.0 | 116 | 0.5095 | 0.8906 |
| 0.0048 | 59.0 | 118 | 0.4912 | 0.875 |
| 0.0032 | 60.0 | 120 | 0.4782 | 0.875 |
| 0.0032 | 61.0 | 122 | 0.4720 | 0.875 |
| 0.0032 | 62.0 | 124 | 0.4713 | 0.875 |
| 0.0032 | 63.0 | 126 | 0.4757 | 0.875 |
| 0.0032 | 64.0 | 128 | 0.4820 | 0.875 |
| 0.0021 | 65.0 | 130 | 0.4919 | 0.875 |
| 0.0021 | 66.0 | 132 | 0.5045 | 0.875 |
| 0.0021 | 67.0 | 134 | 0.5175 | 0.875 |
| 0.0021 | 68.0 | 136 | 0.5308 | 0.875 |
| 0.0021 | 69.0 | 138 | 0.5430 | 0.875 |
| 0.0014 | 70.0 | 140 | 0.5544 | 0.875 |
| 0.0014 | 71.0 | 142 | 0.5643 | 0.8906 |
| 0.0014 | 72.0 | 144 | 0.5735 | 0.8906 |
| 0.0014 | 73.0 | 146 | 0.5810 | 0.8906 |
| 0.0014 | 74.0 | 148 | 0.5871 | 0.8906 |
| 0.0011 | 75.0 | 150 | 0.6019 | 0.8906 |
| 0.0011 | 76.0 | 152 | 0.6149 | 0.8906 |
| 0.0011 | 77.0 | 154 | 0.6262 | 0.8906 |
| 0.0011 | 78.0 | 156 | 0.6356 | 0.8906 |
| 0.0011 | 79.0 | 158 | 0.6435 | 0.8906 |
| 0.0007 | 80.0 | 160 | 0.6504 | 0.8906 |
| 0.0007 | 81.0 | 162 | 0.6568 | 0.8906 |
| 0.0007 | 82.0 | 164 | 0.6606 | 0.8906 |
| 0.0007 | 83.0 | 166 | 0.6625 | 0.8906 |
| 0.0007 | 84.0 | 168 | 0.6645 | 0.8906 |
| 0.0006 | 85.0 | 170 | 0.6663 | 0.8906 |
| 0.0006 | 86.0 | 172 | 0.6676 | 0.8906 |
| 0.0006 | 87.0 | 174 | 0.6691 | 0.8906 |
| 0.0006 | 88.0 | 176 | 0.6705 | 0.8906 |
| 0.0006 | 89.0 | 178 | 0.6717 | 0.8906 |
| 0.0006 | 90.0 | 180 | 0.6726 | 0.8906 |
| 0.0006 | 91.0 | 182 | 0.6735 | 0.8906 |
| 0.0006 | 92.0 | 184 | 0.6745 | 0.8906 |
| 0.0006 | 93.0 | 186 | 0.6756 | 0.8906 |
| 0.0006 | 94.0 | 188 | 0.6768 | 0.8906 |
| 0.0005 | 95.0 | 190 | 0.6781 | 0.8906 |
| 0.0005 | 96.0 | 192 | 0.6788 | 0.8906 |
| 0.0005 | 97.0 | 194 | 0.6791 | 0.8906 |
| 0.0005 | 98.0 | 196 | 0.6794 | 0.8906 |
| 0.0005 | 99.0 | 198 | 0.6798 | 0.8906 |
| 0.0004 | 100.0 | 200 | 0.6801 | 0.8906 |
| 0.0004 | 101.0 | 202 | 0.6805 | 0.8906 |
| 0.0004 | 102.0 | 204 | 0.6810 | 0.8906 |
| 0.0004 | 103.0 | 206 | 0.6817 | 0.8906 |
| 0.0004 | 104.0 | 208 | 0.6826 | 0.8906 |
| 0.0004 | 105.0 | 210 | 0.6833 | 0.8906 |
| 0.0004 | 106.0 | 212 | 0.6841 | 0.8906 |
| 0.0004 | 107.0 | 214 | 0.6850 | 0.8906 |
| 0.0004 | 108.0 | 216 | 0.6857 | 0.8906 |
| 0.0004 | 109.0 | 218 | 0.6866 | 0.8906 |
| 0.0004 | 110.0 | 220 | 0.6874 | 0.8906 |
| 0.0004 | 111.0 | 222 | 0.6881 | 0.8906 |
| 0.0004 | 112.0 | 224 | 0.6886 | 0.8906 |
| 0.0004 | 113.0 | 226 | 0.6889 | 0.8906 |
| 0.0004 | 114.0 | 228 | 0.6890 | 0.8906 |
| 0.0003 | 115.0 | 230 | 0.6889 | 0.8906 |
| 0.0003 | 116.0 | 232 | 0.6888 | 0.8906 |
| 0.0003 | 117.0 | 234 | 0.6886 | 0.8906 |
| 0.0003 | 118.0 | 236 | 0.6885 | 0.8906 |
| 0.0003 | 119.0 | 238 | 0.6874 | 0.8906 |
| 0.0003 | 120.0 | 240 | 0.6866 | 0.8906 |
| 0.0003 | 121.0 | 242 | 0.6860 | 0.8906 |
| 0.0003 | 122.0 | 244 | 0.6857 | 0.8906 |
| 0.0003 | 123.0 | 246 | 0.6855 | 0.8906 |
| 0.0003 | 124.0 | 248 | 0.6852 | 0.8906 |
| 0.0003 | 125.0 | 250 | 0.6850 | 0.8906 |
| 0.0003 | 126.0 | 252 | 0.6847 | 0.8906 |
| 0.0003 | 127.0 | 254 | 0.6846 | 0.8906 |
| 0.0003 | 128.0 | 256 | 0.6846 | 0.8906 |
| 0.0003 | 129.0 | 258 | 0.6846 | 0.8906 |
| 0.0003 | 130.0 | 260 | 0.6846 | 0.8906 |
| 0.0003 | 131.0 | 262 | 0.6847 | 0.8906 |
| 0.0003 | 132.0 | 264 | 0.6847 | 0.8906 |
| 0.0003 | 133.0 | 266 | 0.6848 | 0.8906 |
| 0.0003 | 134.0 | 268 | 0.6846 | 0.8906 |
| 0.0003 | 135.0 | 270 | 0.6846 | 0.8906 |
| 0.0003 | 136.0 | 272 | 0.6846 | 0.8906 |
| 0.0003 | 137.0 | 274 | 0.6846 | 0.8906 |
| 0.0003 | 138.0 | 276 | 0.6847 | 0.8906 |
| 0.0003 | 139.0 | 278 | 0.6848 | 0.8906 |
| 0.0003 | 140.0 | 280 | 0.6849 | 0.8906 |
| 0.0003 | 141.0 | 282 | 0.6851 | 0.8906 |
| 0.0003 | 142.0 | 284 | 0.6852 | 0.8906 |
| 0.0003 | 143.0 | 286 | 0.6854 | 0.8906 |
| 0.0003 | 144.0 | 288 | 0.6855 | 0.8906 |
| 0.0003 | 145.0 | 290 | 0.6855 | 0.8906 |
| 0.0003 | 146.0 | 292 | 0.6856 | 0.8906 |
| 0.0003 | 147.0 | 294 | 0.6857 | 0.8906 |
| 0.0003 | 148.0 | 296 | 0.6857 | 0.8906 |
| 0.0003 | 149.0 | 298 | 0.6858 | 0.8906 |
| 0.0003 | 150.0 | 300 | 0.6858 | 0.8906 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
