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bert-base-uncased-sst-2-64-13

Text Classification
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PyTorch
bert
Generated from Trainer
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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased-sst-2-64-13
    results: []

bert-base-uncased-sst-2-64-13

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: 1.1906
  • 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: 150

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 4 0.6896 0.6094
No log 2.0 8 0.6884 0.6094
0.7032 3.0 12 0.6865 0.6016
0.7032 4.0 16 0.6836 0.6172
0.6985 5.0 20 0.6801 0.6484
0.6985 6.0 24 0.6758 0.6094
0.6985 7.0 28 0.6702 0.6641
0.6762 8.0 32 0.6630 0.7109
0.6762 9.0 36 0.6541 0.6875
0.6336 10.0 40 0.6457 0.6094
0.6336 11.0 44 0.6332 0.6406
0.6336 12.0 48 0.6204 0.6484
0.574 13.0 52 0.6191 0.625
0.574 14.0 56 0.6103 0.625
0.4443 15.0 60 0.5704 0.6719
0.4443 16.0 64 0.5639 0.6562
0.4443 17.0 68 0.5667 0.6875
0.3245 18.0 72 0.5509 0.7031
0.3245 19.0 76 0.5315 0.7109
0.2226 20.0 80 0.5254 0.7266
0.2226 21.0 84 0.5252 0.7578
0.2226 22.0 88 0.5177 0.7422
0.1465 23.0 92 0.5220 0.7422
0.1465 24.0 96 0.5320 0.7422
0.0857 25.0 100 0.5444 0.75
0.0857 26.0 104 0.5613 0.75
0.0857 27.0 108 0.5824 0.7578
0.0485 28.0 112 0.6097 0.7656
0.0485 29.0 116 0.6377 0.7578
0.0251 30.0 120 0.6674 0.7656
0.0251 31.0 124 0.6924 0.7578
0.0251 32.0 128 0.7150 0.7656
0.0146 33.0 132 0.7351 0.7656
0.0146 34.0 136 0.7557 0.7656
0.01 35.0 140 0.7747 0.7656
0.01 36.0 144 0.7929 0.7656
0.01 37.0 148 0.8073 0.7578
0.0075 38.0 152 0.8195 0.7734
0.0075 39.0 156 0.8316 0.7656
0.0061 40.0 160 0.8418 0.7656
0.0061 41.0 164 0.8550 0.7656
0.0061 42.0 168 0.8673 0.7656
0.005 43.0 172 0.8791 0.7734
0.005 44.0 176 0.8911 0.7812
0.0044 45.0 180 0.9022 0.7734
0.0044 46.0 184 0.9113 0.7734
0.0044 47.0 188 0.9195 0.7734
0.0039 48.0 192 0.9268 0.7734
0.0039 49.0 196 0.9340 0.7656
0.0034 50.0 200 0.9405 0.7656
0.0034 51.0 204 0.9480 0.7656
0.0034 52.0 208 0.9575 0.7656
0.0031 53.0 212 0.9649 0.7656
0.0031 54.0 216 0.9711 0.7656
0.0028 55.0 220 0.9775 0.7656
0.0028 56.0 224 0.9822 0.7656
0.0028 57.0 228 0.9865 0.7578
0.0025 58.0 232 0.9903 0.7656
0.0025 59.0 236 0.9945 0.7656
0.0024 60.0 240 0.9989 0.7656
0.0024 61.0 244 1.0031 0.7656
0.0024 62.0 248 1.0074 0.7656
0.0022 63.0 252 1.0114 0.7734
0.0022 64.0 256 1.0152 0.7734
0.0021 65.0 260 1.0186 0.7812
0.0021 66.0 264 1.0223 0.7734
0.0021 67.0 268 1.0254 0.7812
0.0019 68.0 272 1.0290 0.7812
0.0019 69.0 276 1.0333 0.7812
0.0019 70.0 280 1.0378 0.7812
0.0019 71.0 284 1.0419 0.7812
0.0019 72.0 288 1.0464 0.7812
0.0017 73.0 292 1.0507 0.7812
0.0017 74.0 296 1.0549 0.7812
0.0016 75.0 300 1.0586 0.7812
0.0016 76.0 304 1.0618 0.7812
0.0016 77.0 308 1.0650 0.7812
0.0015 78.0 312 1.0684 0.7812
0.0015 79.0 316 1.0719 0.7812
0.0015 80.0 320 1.0752 0.7812
0.0015 81.0 324 1.0784 0.7812
0.0015 82.0 328 1.0815 0.7891
0.0014 83.0 332 1.0845 0.7891
0.0014 84.0 336 1.0877 0.7891
0.0014 85.0 340 1.0909 0.7891
0.0014 86.0 344 1.0940 0.7891
0.0014 87.0 348 1.0971 0.7891
0.0013 88.0 352 1.1001 0.7891
0.0013 89.0 356 1.1030 0.7891
0.0012 90.0 360 1.1057 0.7891
0.0012 91.0 364 1.1088 0.7891
0.0012 92.0 368 1.1120 0.7891
0.0012 93.0 372 1.1151 0.7891
0.0012 94.0 376 1.1183 0.7891
0.0011 95.0 380 1.1211 0.7891
0.0011 96.0 384 1.1238 0.7891
0.0011 97.0 388 1.1267 0.7891
0.0011 98.0 392 1.1297 0.7891
0.0011 99.0 396 1.1324 0.7891
0.0011 100.0 400 1.1349 0.7891
0.0011 101.0 404 1.1373 0.7891
0.0011 102.0 408 1.1395 0.7891
0.001 103.0 412 1.1415 0.7891
0.001 104.0 416 1.1433 0.7891
0.001 105.0 420 1.1451 0.7891
0.001 106.0 424 1.1471 0.7812
0.001 107.0 428 1.1491 0.7812
0.001 108.0 432 1.1512 0.7812
0.001 109.0 436 1.1531 0.7812
0.001 110.0 440 1.1549 0.7812
0.001 111.0 444 1.1566 0.7812
0.001 112.0 448 1.1583 0.7812
0.001 113.0 452 1.1598 0.7812
0.001 114.0 456 1.1613 0.7812
0.0009 115.0 460 1.1628 0.7812
0.0009 116.0 464 1.1642 0.7812
0.0009 117.0 468 1.1657 0.7812
0.0009 118.0 472 1.1672 0.7812
0.0009 119.0 476 1.1686 0.7812
0.0008 120.0 480 1.1700 0.7812
0.0008 121.0 484 1.1713 0.7812
0.0008 122.0 488 1.1727 0.7812
0.0009 123.0 492 1.1742 0.7812
0.0009 124.0 496 1.1757 0.7812
0.0009 125.0 500 1.1770 0.7812
0.0009 126.0 504 1.1783 0.7812
0.0009 127.0 508 1.1795 0.7812
0.0008 128.0 512 1.1805 0.7812
0.0008 129.0 516 1.1815 0.7812
0.0009 130.0 520 1.1823 0.7812
0.0009 131.0 524 1.1832 0.7812
0.0009 132.0 528 1.1840 0.7812
0.0008 133.0 532 1.1847 0.7812
0.0008 134.0 536 1.1854 0.7812
0.0008 135.0 540 1.1861 0.7812
0.0008 136.0 544 1.1867 0.7812
0.0008 137.0 548 1.1872 0.7812
0.0008 138.0 552 1.1876 0.7812
0.0008 139.0 556 1.1881 0.7812
0.0008 140.0 560 1.1885 0.7812
0.0008 141.0 564 1.1888 0.7812
0.0008 142.0 568 1.1891 0.7812
0.0008 143.0 572 1.1895 0.7812
0.0008 144.0 576 1.1897 0.7812
0.0008 145.0 580 1.1900 0.7812
0.0008 146.0 584 1.1902 0.7812
0.0008 147.0 588 1.1904 0.7812
0.0008 148.0 592 1.1905 0.7812
0.0008 149.0 596 1.1906 0.7812
0.0008 150.0 600 1.1906 0.7812

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.4.0
  • Tokenizers 0.13.3