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bert-large-uncased-sst-2-64-13
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: 0.7922
- Accuracy: 0.9062
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 | 4 | 0.7522 | 0.4922 |
| No log | 2.0 | 8 | 0.7425 | 0.4922 |
| 0.76 | 3.0 | 12 | 0.7340 | 0.4922 |
| 0.76 | 4.0 | 16 | 0.7249 | 0.4922 |
| 0.7156 | 5.0 | 20 | 0.7169 | 0.4922 |
| 0.7156 | 6.0 | 24 | 0.7071 | 0.4922 |
| 0.7156 | 7.0 | 28 | 0.6967 | 0.4922 |
| 0.696 | 8.0 | 32 | 0.6778 | 0.4922 |
| 0.696 | 9.0 | 36 | 0.6520 | 0.5391 |
| 0.6324 | 10.0 | 40 | 0.6192 | 0.6562 |
| 0.6324 | 11.0 | 44 | 0.5962 | 0.7109 |
| 0.6324 | 12.0 | 48 | 0.5862 | 0.6953 |
| 0.5297 | 13.0 | 52 | 0.5024 | 0.8359 |
| 0.5297 | 14.0 | 56 | 0.4287 | 0.8438 |
| 0.3191 | 15.0 | 60 | 0.3940 | 0.8281 |
| 0.3191 | 16.0 | 64 | 0.3352 | 0.8828 |
| 0.3191 | 17.0 | 68 | 0.3640 | 0.8359 |
| 0.1373 | 18.0 | 72 | 0.2822 | 0.9062 |
| 0.1373 | 19.0 | 76 | 0.2677 | 0.9062 |
| 0.0624 | 20.0 | 80 | 0.2650 | 0.9219 |
| 0.0624 | 21.0 | 84 | 0.2758 | 0.9141 |
| 0.0624 | 22.0 | 88 | 0.2662 | 0.9141 |
| 0.0257 | 23.0 | 92 | 0.3016 | 0.9141 |
| 0.0257 | 24.0 | 96 | 0.3611 | 0.8906 |
| 0.0118 | 25.0 | 100 | 0.3683 | 0.8984 |
| 0.0118 | 26.0 | 104 | 0.3733 | 0.8984 |
| 0.0118 | 27.0 | 108 | 0.3953 | 0.8984 |
| 0.0065 | 28.0 | 112 | 0.4194 | 0.8984 |
| 0.0065 | 29.0 | 116 | 0.4195 | 0.8984 |
| 0.0042 | 30.0 | 120 | 0.4249 | 0.8984 |
| 0.0042 | 31.0 | 124 | 0.4360 | 0.9062 |
| 0.0042 | 32.0 | 128 | 0.4412 | 0.9062 |
| 0.0033 | 33.0 | 132 | 0.4467 | 0.9062 |
| 0.0033 | 34.0 | 136 | 0.4550 | 0.9062 |
| 0.0026 | 35.0 | 140 | 0.4652 | 0.9062 |
| 0.0026 | 36.0 | 144 | 0.4725 | 0.9062 |
| 0.0026 | 37.0 | 148 | 0.4796 | 0.9062 |
| 0.0021 | 38.0 | 152 | 0.4906 | 0.9062 |
| 0.0021 | 39.0 | 156 | 0.5007 | 0.9062 |
| 0.0019 | 40.0 | 160 | 0.5109 | 0.9062 |
| 0.0019 | 41.0 | 164 | 0.5194 | 0.9062 |
| 0.0019 | 42.0 | 168 | 0.5274 | 0.9062 |
| 0.0014 | 43.0 | 172 | 0.5348 | 0.9062 |
| 0.0014 | 44.0 | 176 | 0.5408 | 0.9062 |
| 0.0012 | 45.0 | 180 | 0.5484 | 0.9062 |
| 0.0012 | 46.0 | 184 | 0.5577 | 0.9062 |
| 0.0012 | 47.0 | 188 | 0.5688 | 0.9062 |
| 0.0009 | 48.0 | 192 | 0.5802 | 0.8984 |
| 0.0009 | 49.0 | 196 | 0.5905 | 0.8984 |
| 0.0007 | 50.0 | 200 | 0.6000 | 0.8984 |
| 0.0007 | 51.0 | 204 | 0.6085 | 0.8984 |
| 0.0007 | 52.0 | 208 | 0.6164 | 0.8984 |
| 0.0006 | 53.0 | 212 | 0.6250 | 0.8984 |
| 0.0006 | 54.0 | 216 | 0.6326 | 0.8984 |
| 0.0005 | 55.0 | 220 | 0.6389 | 0.8984 |
| 0.0005 | 56.0 | 224 | 0.6453 | 0.8984 |
| 0.0005 | 57.0 | 228 | 0.6451 | 0.8984 |
| 0.0005 | 58.0 | 232 | 0.6473 | 0.9062 |
| 0.0005 | 59.0 | 236 | 0.6512 | 0.9062 |
| 0.0003 | 60.0 | 240 | 0.6561 | 0.9062 |
| 0.0003 | 61.0 | 244 | 0.6620 | 0.9062 |
| 0.0003 | 62.0 | 248 | 0.6680 | 0.9062 |
| 0.0003 | 63.0 | 252 | 0.6736 | 0.9062 |
| 0.0003 | 64.0 | 256 | 0.6788 | 0.9062 |
| 0.0003 | 65.0 | 260 | 0.6836 | 0.9062 |
| 0.0003 | 66.0 | 264 | 0.6880 | 0.9062 |
| 0.0003 | 67.0 | 268 | 0.6923 | 0.9062 |
| 0.0002 | 68.0 | 272 | 0.6954 | 0.9062 |
| 0.0002 | 69.0 | 276 | 0.6983 | 0.9062 |
| 0.0002 | 70.0 | 280 | 0.7008 | 0.9062 |
| 0.0002 | 71.0 | 284 | 0.7032 | 0.9062 |
| 0.0002 | 72.0 | 288 | 0.7059 | 0.9062 |
| 0.0002 | 73.0 | 292 | 0.7085 | 0.9062 |
| 0.0002 | 74.0 | 296 | 0.7112 | 0.9062 |
| 0.0002 | 75.0 | 300 | 0.7144 | 0.9062 |
| 0.0002 | 76.0 | 304 | 0.7173 | 0.9062 |
| 0.0002 | 77.0 | 308 | 0.7199 | 0.9062 |
| 0.0002 | 78.0 | 312 | 0.7223 | 0.9062 |
| 0.0002 | 79.0 | 316 | 0.7247 | 0.9062 |
| 0.0002 | 80.0 | 320 | 0.7272 | 0.9062 |
| 0.0002 | 81.0 | 324 | 0.7295 | 0.9062 |
| 0.0002 | 82.0 | 328 | 0.7318 | 0.9062 |
| 0.0001 | 83.0 | 332 | 0.7341 | 0.9062 |
| 0.0001 | 84.0 | 336 | 0.7362 | 0.9062 |
| 0.0001 | 85.0 | 340 | 0.7383 | 0.9062 |
| 0.0001 | 86.0 | 344 | 0.7402 | 0.9062 |
| 0.0001 | 87.0 | 348 | 0.7417 | 0.9062 |
| 0.0001 | 88.0 | 352 | 0.7430 | 0.9062 |
| 0.0001 | 89.0 | 356 | 0.7445 | 0.9062 |
| 0.0001 | 90.0 | 360 | 0.7458 | 0.9062 |
| 0.0001 | 91.0 | 364 | 0.7470 | 0.9062 |
| 0.0001 | 92.0 | 368 | 0.7463 | 0.9062 |
| 0.0001 | 93.0 | 372 | 0.7463 | 0.9062 |
| 0.0001 | 94.0 | 376 | 0.7466 | 0.9062 |
| 0.0001 | 95.0 | 380 | 0.7472 | 0.9062 |
| 0.0001 | 96.0 | 384 | 0.7469 | 0.9062 |
| 0.0001 | 97.0 | 388 | 0.7472 | 0.9062 |
| 0.0001 | 98.0 | 392 | 0.7480 | 0.9062 |
| 0.0001 | 99.0 | 396 | 0.7488 | 0.9062 |
| 0.0001 | 100.0 | 400 | 0.7501 | 0.9062 |
| 0.0001 | 101.0 | 404 | 0.7514 | 0.9062 |
| 0.0001 | 102.0 | 408 | 0.7527 | 0.9062 |
| 0.0001 | 103.0 | 412 | 0.7539 | 0.9062 |
| 0.0001 | 104.0 | 416 | 0.7551 | 0.9062 |
| 0.0001 | 105.0 | 420 | 0.7563 | 0.9062 |
| 0.0001 | 106.0 | 424 | 0.7575 | 0.9062 |
| 0.0001 | 107.0 | 428 | 0.7584 | 0.9062 |
| 0.0001 | 108.0 | 432 | 0.7593 | 0.9062 |
| 0.0001 | 109.0 | 436 | 0.7603 | 0.9062 |
| 0.0001 | 110.0 | 440 | 0.7612 | 0.9062 |
| 0.0001 | 111.0 | 444 | 0.7622 | 0.9062 |
| 0.0001 | 112.0 | 448 | 0.7631 | 0.9062 |
| 0.0001 | 113.0 | 452 | 0.7640 | 0.9062 |
| 0.0001 | 114.0 | 456 | 0.7650 | 0.9062 |
| 0.0001 | 115.0 | 460 | 0.7659 | 0.9062 |
| 0.0001 | 116.0 | 464 | 0.7669 | 0.9062 |
| 0.0001 | 117.0 | 468 | 0.7677 | 0.9062 |
| 0.0001 | 118.0 | 472 | 0.7686 | 0.9062 |
| 0.0001 | 119.0 | 476 | 0.7693 | 0.9062 |
| 0.0001 | 120.0 | 480 | 0.7701 | 0.9062 |
| 0.0001 | 121.0 | 484 | 0.7708 | 0.9062 |
| 0.0001 | 122.0 | 488 | 0.7756 | 0.9062 |
| 0.0015 | 123.0 | 492 | 0.7777 | 0.9062 |
| 0.0015 | 124.0 | 496 | 0.7776 | 0.9062 |
| 0.0001 | 125.0 | 500 | 0.7776 | 0.9062 |
| 0.0001 | 126.0 | 504 | 0.7780 | 0.9062 |
| 0.0001 | 127.0 | 508 | 0.7786 | 0.9062 |
| 0.0001 | 128.0 | 512 | 0.7794 | 0.9062 |
| 0.0001 | 129.0 | 516 | 0.7803 | 0.9062 |
| 0.0002 | 130.0 | 520 | 0.7822 | 0.9062 |
| 0.0002 | 131.0 | 524 | 0.7843 | 0.9062 |
| 0.0002 | 132.0 | 528 | 0.7859 | 0.9062 |
| 0.0001 | 133.0 | 532 | 0.7871 | 0.9062 |
| 0.0001 | 134.0 | 536 | 0.7880 | 0.9062 |
| 0.0001 | 135.0 | 540 | 0.7887 | 0.9062 |
| 0.0001 | 136.0 | 544 | 0.7894 | 0.9062 |
| 0.0001 | 137.0 | 548 | 0.7899 | 0.9062 |
| 0.0001 | 138.0 | 552 | 0.7903 | 0.9062 |
| 0.0001 | 139.0 | 556 | 0.7907 | 0.9062 |
| 0.0001 | 140.0 | 560 | 0.7910 | 0.9062 |
| 0.0001 | 141.0 | 564 | 0.7912 | 0.9062 |
| 0.0001 | 142.0 | 568 | 0.7914 | 0.9062 |
| 0.0001 | 143.0 | 572 | 0.7916 | 0.9062 |
| 0.0001 | 144.0 | 576 | 0.7918 | 0.9062 |
| 0.0001 | 145.0 | 580 | 0.7919 | 0.9062 |
| 0.0001 | 146.0 | 584 | 0.7920 | 0.9062 |
| 0.0001 | 147.0 | 588 | 0.7921 | 0.9062 |
| 0.0001 | 148.0 | 592 | 0.7922 | 0.9062 |
| 0.0001 | 149.0 | 596 | 0.7922 | 0.9062 |
| 0.0001 | 150.0 | 600 | 0.7922 | 0.9062 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3
