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best_model-sst-2-64-42

Text Classification
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PyTorch
bert
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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-2-64-42
    results: []

best_model-sst-2-64-42

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: 1.4849
  • Accuracy: 0.8281

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 1.3914 0.8125
No log 2.0 8 1.3910 0.8203
0.3843 3.0 12 1.3922 0.8203
0.3843 4.0 16 1.3920 0.8203
0.5793 5.0 20 1.3923 0.8203
0.5793 6.0 24 1.3989 0.8203
0.5793 7.0 28 1.4029 0.8281
0.3663 8.0 32 1.4103 0.8281
0.3663 9.0 36 1.3999 0.8281
0.2779 10.0 40 1.4010 0.8281
0.2779 11.0 44 1.3978 0.8281
0.2779 12.0 48 1.3963 0.8203
0.3589 13.0 52 1.4087 0.8203
0.3589 14.0 56 1.4067 0.8281
0.3185 15.0 60 1.4148 0.8281
0.3185 16.0 64 1.4171 0.8359
0.3185 17.0 68 1.4140 0.8359
0.1743 18.0 72 1.3982 0.8359
0.1743 19.0 76 1.3650 0.8359
0.1416 20.0 80 1.3456 0.8359
0.1416 21.0 84 1.3210 0.8359
0.1416 22.0 88 1.3070 0.8359
0.0354 23.0 92 1.3015 0.8359
0.0354 24.0 96 1.3319 0.8438
0.0035 25.0 100 1.3656 0.8281
0.0035 26.0 104 1.3587 0.8281
0.0035 27.0 108 1.3243 0.8359
0.0006 28.0 112 1.2945 0.8438
0.0006 29.0 116 1.2898 0.8438
0.0028 30.0 120 1.3066 0.8438
0.0028 31.0 124 1.3055 0.8438
0.0028 32.0 128 1.3202 0.8438
0.0049 33.0 132 1.3351 0.8438
0.0049 34.0 136 1.3190 0.8438
0.0102 35.0 140 1.3141 0.8438
0.0102 36.0 144 1.3142 0.8438
0.0102 37.0 148 1.3647 0.8281
0.0034 38.0 152 1.4250 0.8203
0.0034 39.0 156 1.4708 0.8203
0.0001 40.0 160 1.4570 0.8203
0.0001 41.0 164 1.4446 0.8203
0.0001 42.0 168 1.4345 0.8281
0.0001 43.0 172 1.4272 0.8281
0.0001 44.0 176 1.4185 0.8281
0.0001 45.0 180 1.4048 0.8281
0.0001 46.0 184 1.3962 0.8281
0.0001 47.0 188 1.4924 0.8203
0.0002 48.0 192 1.5361 0.8125
0.0002 49.0 196 1.5831 0.8125
0.0292 50.0 200 1.4789 0.8281
0.0292 51.0 204 1.2642 0.8359
0.0292 52.0 208 1.2154 0.8516
0.0001 53.0 212 1.1895 0.8516
0.0001 54.0 216 1.1775 0.8438
0.0001 55.0 220 1.1730 0.8438
0.0001 56.0 224 1.1746 0.8438
0.0001 57.0 228 1.1782 0.8516
0.0001 58.0 232 1.1838 0.8516
0.0001 59.0 236 1.2456 0.8281
0.025 60.0 240 1.3887 0.8281
0.025 61.0 244 1.4950 0.8125
0.025 62.0 248 1.5753 0.8047
0.0001 63.0 252 1.6287 0.8047
0.0001 64.0 256 1.6608 0.8047
0.0001 65.0 260 1.6803 0.8047
0.0001 66.0 264 1.6919 0.7969
0.0001 67.0 268 1.5961 0.8047
0.0001 68.0 272 1.4858 0.8125
0.0001 69.0 276 1.4104 0.8281
0.0001 70.0 280 1.3623 0.8281
0.0001 71.0 284 1.3333 0.8359
0.0001 72.0 288 1.3172 0.8359
0.0 73.0 292 1.3107 0.8359
0.0 74.0 296 1.5801 0.8047
0.0014 75.0 300 1.7857 0.8047
0.0014 76.0 304 1.8724 0.7969
0.0014 77.0 308 1.9146 0.7969
0.0001 78.0 312 1.9250 0.7969
0.0001 79.0 316 1.9265 0.7969
0.0001 80.0 320 1.9268 0.7969
0.0001 81.0 324 1.9243 0.7969
0.0001 82.0 328 1.9215 0.7969
0.0 83.0 332 1.9188 0.7969
0.0 84.0 336 1.9159 0.7969
0.0 85.0 340 1.9137 0.7969
0.0 86.0 344 1.9119 0.7969
0.0 87.0 348 1.9103 0.7969
0.0009 88.0 352 1.6541 0.8047
0.0009 89.0 356 1.2749 0.8438
0.0 90.0 360 1.2046 0.8438
0.0 91.0 364 1.1909 0.8438
0.0 92.0 368 1.1860 0.8594
0.0 93.0 372 1.1901 0.8594
0.0 94.0 376 1.1966 0.8516
0.0001 95.0 380 1.2014 0.8516
0.0001 96.0 384 1.2061 0.8438
0.0001 97.0 388 1.2109 0.8438
0.0 98.0 392 1.2170 0.8516
0.0 99.0 396 1.2210 0.8516
0.0 100.0 400 1.2237 0.8516
0.0 101.0 404 1.2258 0.8516
0.0 102.0 408 1.2276 0.8438
0.0 103.0 412 1.2290 0.8438
0.0 104.0 416 1.2301 0.8438
0.0 105.0 420 1.2313 0.8438
0.0 106.0 424 1.2324 0.8438
0.0 107.0 428 1.2334 0.8438
0.0 108.0 432 1.2345 0.8438
0.0 109.0 436 1.2356 0.8438
0.0 110.0 440 1.2366 0.8438
0.0 111.0 444 1.2375 0.8516
0.0 112.0 448 1.2384 0.8516
0.0 113.0 452 1.2400 0.8516
0.0 114.0 456 1.2415 0.8516
0.0 115.0 460 1.2428 0.8516
0.0 116.0 464 1.2439 0.8516
0.0 117.0 468 1.2450 0.8516
0.0 118.0 472 1.2459 0.8516
0.0 119.0 476 1.2467 0.8516
0.0 120.0 480 1.2476 0.8516
0.0 121.0 484 1.2485 0.8516
0.0 122.0 488 1.2495 0.8516
0.0 123.0 492 1.2495 0.8516
0.0 124.0 496 1.2491 0.8516
0.0 125.0 500 1.2491 0.8516
0.0 126.0 504 1.2494 0.8516
0.0 127.0 508 1.2498 0.8516
0.0 128.0 512 1.2503 0.8516
0.0 129.0 516 1.2509 0.8516
0.0 130.0 520 1.2514 0.8516
0.0 131.0 524 1.2519 0.8516
0.0 132.0 528 1.2527 0.8516
0.0 133.0 532 1.2535 0.8516
0.0 134.0 536 1.2542 0.8516
0.0 135.0 540 1.2549 0.8516
0.0 136.0 544 1.2554 0.8516
0.0 137.0 548 1.3879 0.8359
0.0001 138.0 552 1.6893 0.7969
0.0001 139.0 556 1.8348 0.7969
0.0 140.0 560 1.8942 0.7969
0.0 141.0 564 1.8778 0.7969
0.0 142.0 568 1.7187 0.8047
0.0001 143.0 572 1.6119 0.8203
0.0001 144.0 576 1.5523 0.8281
0.0 145.0 580 1.5189 0.8281
0.0 146.0 584 1.5008 0.8281
0.0 147.0 588 1.4916 0.8281
0.0 148.0 592 1.4872 0.8281
0.0 149.0 596 1.4854 0.8281
0.0 150.0 600 1.4849 0.8281

Framework versions

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