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best_model-sst-2-64-100

Text Classification
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PyTorch
bert
Generated from Trainer
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best_model-sst-2-64-100 / README.md
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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-sst-2-64-100
    results: []

best_model-sst-2-64-100

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: 0.9480
  • 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: 500
  • num_epochs: 150

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 4 0.8613 0.9141
No log 2.0 8 0.8613 0.9141
0.6496 3.0 12 0.8614 0.9141
0.6496 4.0 16 0.8614 0.9141
0.6483 5.0 20 0.8596 0.9141
0.6483 6.0 24 0.8575 0.9141
0.6483 7.0 28 0.8557 0.9141
0.6867 8.0 32 0.8528 0.9141
0.6867 9.0 36 0.8506 0.9141
0.3821 10.0 40 0.8542 0.9062
0.3821 11.0 44 0.8721 0.8984
0.3821 12.0 48 0.8877 0.8984
0.4452 13.0 52 0.8920 0.8984
0.4452 14.0 56 0.8952 0.8984
0.3224 15.0 60 0.8920 0.9062
0.3224 16.0 64 0.8833 0.9062
0.3224 17.0 68 0.8727 0.9062
0.2699 18.0 72 0.8284 0.8984
0.2699 19.0 76 0.7829 0.9062
0.1873 20.0 80 0.7713 0.9062
0.1873 21.0 84 0.7646 0.8984
0.1873 22.0 88 0.7517 0.8984
0.1282 23.0 92 0.7379 0.9062
0.1282 24.0 96 0.7295 0.9062
0.0438 25.0 100 0.7243 0.8984
0.0438 26.0 104 0.7038 0.9141
0.0438 27.0 108 0.6994 0.9219
0.0154 28.0 112 0.6997 0.9062
0.0154 29.0 116 0.7184 0.8984
0.0019 30.0 120 0.7601 0.9062
0.0019 31.0 124 0.7739 0.9062
0.0019 32.0 128 0.7854 0.9062
0.0003 33.0 132 0.7934 0.9062
0.0003 34.0 136 0.7945 0.9062
0.0002 35.0 140 0.7896 0.9062
0.0002 36.0 144 0.7711 0.9062
0.0002 37.0 148 0.7503 0.9062
0.0004 38.0 152 0.7436 0.9062
0.0004 39.0 156 0.7464 0.9062
0.0001 40.0 160 0.7492 0.9062
0.0001 41.0 164 0.7990 0.9062
0.0001 42.0 168 0.8244 0.9062
0.0059 43.0 172 0.8377 0.9062
0.0059 44.0 176 0.8496 0.9062
0.0001 45.0 180 0.8582 0.9062
0.0001 46.0 184 0.8646 0.9062
0.0001 47.0 188 0.8286 0.9062
0.0005 48.0 192 0.8002 0.9062
0.0005 49.0 196 0.7854 0.9062
0.0001 50.0 200 0.7691 0.9062
0.0001 51.0 204 0.7594 0.9062
0.0001 52.0 208 0.7618 0.9062
0.0003 53.0 212 0.8175 0.9062
0.0003 54.0 216 0.8539 0.9062
0.0001 55.0 220 0.8737 0.9062
0.0001 56.0 224 0.8661 0.9062
0.0001 57.0 228 0.8398 0.9062
0.0038 58.0 232 0.8162 0.9062
0.0038 59.0 236 0.7946 0.9062
0.0001 60.0 240 0.7866 0.9062
0.0001 61.0 244 0.7776 0.9141
0.0001 62.0 248 0.7781 0.9141
0.0001 63.0 252 0.7963 0.9062
0.0001 64.0 256 0.8099 0.9062
0.0 65.0 260 0.8196 0.9062
0.0 66.0 264 0.8284 0.9062
0.0 67.0 268 0.8880 0.9062
0.0045 68.0 272 0.9217 0.9062
0.0045 69.0 276 0.9374 0.8984
0.0082 70.0 280 0.9364 0.9062
0.0082 71.0 284 0.8651 0.9062
0.0082 72.0 288 0.7849 0.8984
0.0003 73.0 292 0.7981 0.8984
0.0003 74.0 296 0.7808 0.9141
0.021 75.0 300 0.8438 0.9062
0.021 76.0 304 0.8882 0.8984
0.021 77.0 308 0.9214 0.8984
0.0001 78.0 312 0.9396 0.8984
0.0001 79.0 316 0.9493 0.8984
0.0 80.0 320 0.9549 0.8984
0.0 81.0 324 0.9466 0.8984
0.0 82.0 328 0.9041 0.8984
0.0001 83.0 332 0.8993 0.8984
0.0001 84.0 336 0.9616 0.8984
0.0001 85.0 340 0.9844 0.8984
0.0001 86.0 344 0.9934 0.8906
0.0001 87.0 348 0.9999 0.8906
0.0001 88.0 352 0.9973 0.8906
0.0001 89.0 356 0.9943 0.8984
0.0 90.0 360 0.9929 0.8984
0.0 91.0 364 0.9921 0.8984
0.0 92.0 368 0.9915 0.8984
0.0 93.0 372 0.9916 0.8984
0.0 94.0 376 0.9924 0.8984
0.0 95.0 380 0.9930 0.8984
0.0 96.0 384 0.9936 0.8984
0.0 97.0 388 0.9940 0.8984
0.0 98.0 392 0.9946 0.8984
0.0 99.0 396 0.9950 0.8984
0.0006 100.0 400 0.9869 0.8984
0.0006 101.0 404 0.8625 0.8984
0.0006 102.0 408 0.7755 0.9219
0.0 103.0 412 0.7887 0.8984
0.0 104.0 416 0.7844 0.9062
0.0062 105.0 420 0.8504 0.8984
0.0062 106.0 424 0.9449 0.8984
0.0062 107.0 428 0.9568 0.8906
0.0 108.0 432 0.9504 0.8984
0.0 109.0 436 0.9700 0.8984
0.0 110.0 440 0.9875 0.8906
0.0 111.0 444 1.0002 0.8906
0.0 112.0 448 1.0095 0.8828
0.0 113.0 452 1.0156 0.8828
0.0 114.0 456 0.8995 0.8984
0.0144 115.0 460 0.8017 0.8984
0.0144 116.0 464 0.7774 0.9062
0.0144 117.0 468 0.7913 0.9062
0.0 118.0 472 0.8033 0.8984
0.0 119.0 476 0.8244 0.8906
0.0001 120.0 480 0.9148 0.8984
0.0001 121.0 484 1.0038 0.8828
0.0001 122.0 488 1.1128 0.875
0.0 123.0 492 1.1276 0.875
0.0 124.0 496 1.1209 0.8828
0.0 125.0 500 1.1161 0.8828
0.0 126.0 504 1.1119 0.8828
0.0 127.0 508 1.1037 0.8828
0.0 128.0 512 1.0644 0.8828
0.0 129.0 516 1.0175 0.875
0.0 130.0 520 0.9819 0.8828
0.0 131.0 524 0.9613 0.8906
0.0 132.0 528 0.9509 0.8906
0.0 133.0 532 0.9463 0.8906
0.0 134.0 536 0.9441 0.875
0.0 135.0 540 0.9432 0.875
0.0 136.0 544 0.9429 0.875
0.0 137.0 548 0.9429 0.8828
0.0 138.0 552 0.9430 0.8828
0.0 139.0 556 0.9432 0.8828
0.0 140.0 560 0.9434 0.8828
0.0 141.0 564 0.9436 0.8828
0.0 142.0 568 0.9438 0.8906
0.0 143.0 572 0.9439 0.8906
0.0 144.0 576 0.9448 0.8906
0.0 145.0 580 0.9461 0.8906
0.0 146.0 584 0.9470 0.8906
0.0 147.0 588 0.9476 0.8906
0.0 148.0 592 0.9478 0.8906
0.0 149.0 596 0.9480 0.8906
0.0 150.0 600 0.9480 0.8906

Framework versions

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