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bert-base-uncased-sst-2-16-42-smoothed
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6339
- Accuracy: 0.7812
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: 75
- label_smoothing_factor: 0.45
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 0.7048 | 0.5 |
| No log | 2.0 | 8 | 0.7021 | 0.5 |
| 0.7078 | 3.0 | 12 | 0.6982 | 0.5 |
| 0.7078 | 4.0 | 16 | 0.6936 | 0.4922 |
| 0.6935 | 5.0 | 20 | 0.6898 | 0.5625 |
| 0.6935 | 6.0 | 24 | 0.6878 | 0.625 |
| 0.6935 | 7.0 | 28 | 0.6856 | 0.6484 |
| 0.682 | 8.0 | 32 | 0.6818 | 0.6797 |
| 0.682 | 9.0 | 36 | 0.6759 | 0.7109 |
| 0.6567 | 10.0 | 40 | 0.6692 | 0.6562 |
| 0.6567 | 11.0 | 44 | 0.6662 | 0.6562 |
| 0.6567 | 12.0 | 48 | 0.6669 | 0.6484 |
| 0.6177 | 13.0 | 52 | 0.6612 | 0.6719 |
| 0.6177 | 14.0 | 56 | 0.6589 | 0.6641 |
| 0.5787 | 15.0 | 60 | 0.6565 | 0.6797 |
| 0.5787 | 16.0 | 64 | 0.6519 | 0.7109 |
| 0.5787 | 17.0 | 68 | 0.6506 | 0.7266 |
| 0.5487 | 18.0 | 72 | 0.6496 | 0.75 |
| 0.5487 | 19.0 | 76 | 0.6489 | 0.7344 |
| 0.5398 | 20.0 | 80 | 0.6492 | 0.75 |
| 0.5398 | 21.0 | 84 | 0.6467 | 0.7578 |
| 0.5398 | 22.0 | 88 | 0.6439 | 0.75 |
| 0.5384 | 23.0 | 92 | 0.6419 | 0.75 |
| 0.5384 | 24.0 | 96 | 0.6442 | 0.75 |
| 0.5389 | 25.0 | 100 | 0.6442 | 0.7422 |
| 0.5389 | 26.0 | 104 | 0.6391 | 0.75 |
| 0.5389 | 27.0 | 108 | 0.6428 | 0.7734 |
| 0.5373 | 28.0 | 112 | 0.6449 | 0.7656 |
| 0.5373 | 29.0 | 116 | 0.6412 | 0.7812 |
| 0.5379 | 30.0 | 120 | 0.6359 | 0.7578 |
| 0.5379 | 31.0 | 124 | 0.6348 | 0.7734 |
| 0.5379 | 32.0 | 128 | 0.6343 | 0.7891 |
| 0.5366 | 33.0 | 132 | 0.6340 | 0.7969 |
| 0.5366 | 34.0 | 136 | 0.6340 | 0.7891 |
| 0.5366 | 35.0 | 140 | 0.6339 | 0.7734 |
| 0.5366 | 36.0 | 144 | 0.6337 | 0.7734 |
| 0.5366 | 37.0 | 148 | 0.6335 | 0.7734 |
| 0.5363 | 38.0 | 152 | 0.6334 | 0.7969 |
| 0.5363 | 39.0 | 156 | 0.6347 | 0.7734 |
| 0.5367 | 40.0 | 160 | 0.6355 | 0.7656 |
| 0.5367 | 41.0 | 164 | 0.6363 | 0.7578 |
| 0.5367 | 42.0 | 168 | 0.6374 | 0.7656 |
| 0.5359 | 43.0 | 172 | 0.6375 | 0.7656 |
| 0.5359 | 44.0 | 176 | 0.6357 | 0.7578 |
| 0.5358 | 45.0 | 180 | 0.6351 | 0.7656 |
| 0.5358 | 46.0 | 184 | 0.6339 | 0.7734 |
| 0.5358 | 47.0 | 188 | 0.6334 | 0.7812 |
| 0.5362 | 48.0 | 192 | 0.6330 | 0.7891 |
| 0.5362 | 49.0 | 196 | 0.6327 | 0.7891 |
| 0.5364 | 50.0 | 200 | 0.6329 | 0.7891 |
| 0.5364 | 51.0 | 204 | 0.6341 | 0.7734 |
| 0.5364 | 52.0 | 208 | 0.6337 | 0.7734 |
| 0.5365 | 53.0 | 212 | 0.6326 | 0.7891 |
| 0.5365 | 54.0 | 216 | 0.6325 | 0.7891 |
| 0.5361 | 55.0 | 220 | 0.6326 | 0.7969 |
| 0.5361 | 56.0 | 224 | 0.6328 | 0.7891 |
| 0.5361 | 57.0 | 228 | 0.6328 | 0.7891 |
| 0.5361 | 58.0 | 232 | 0.6330 | 0.7891 |
| 0.5361 | 59.0 | 236 | 0.6335 | 0.7812 |
| 0.5363 | 60.0 | 240 | 0.6340 | 0.7812 |
| 0.5363 | 61.0 | 244 | 0.6343 | 0.7812 |
| 0.5363 | 62.0 | 248 | 0.6346 | 0.7734 |
| 0.536 | 63.0 | 252 | 0.6348 | 0.7734 |
| 0.536 | 64.0 | 256 | 0.6349 | 0.7734 |
| 0.5362 | 65.0 | 260 | 0.6353 | 0.7656 |
| 0.5362 | 66.0 | 264 | 0.6358 | 0.7656 |
| 0.5362 | 67.0 | 268 | 0.6361 | 0.7578 |
| 0.536 | 68.0 | 272 | 0.6355 | 0.7656 |
| 0.536 | 69.0 | 276 | 0.6349 | 0.7734 |
| 0.5358 | 70.0 | 280 | 0.6344 | 0.7734 |
| 0.5358 | 71.0 | 284 | 0.6342 | 0.7812 |
| 0.5358 | 72.0 | 288 | 0.6340 | 0.7812 |
| 0.5357 | 73.0 | 292 | 0.6340 | 0.7812 |
| 0.5357 | 74.0 | 296 | 0.6339 | 0.7812 |
| 0.5358 | 75.0 | 300 | 0.6339 | 0.7812 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
