best_model-yelp_polarity-64-100

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7418
  • Accuracy: 0.9219

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 0.8689 0.9062
No log 2.0 8 0.8610 0.9062
0.4308 3.0 12 0.8208 0.9062
0.4308 4.0 16 0.7905 0.9141
0.34 5.0 20 0.7690 0.9141
0.34 6.0 24 0.7703 0.9141
0.34 7.0 28 0.7918 0.9141
0.2192 8.0 32 0.7998 0.9062
0.2192 9.0 36 0.8100 0.9062
0.1324 10.0 40 0.8201 0.9062
0.1324 11.0 44 0.8098 0.9062
0.1324 12.0 48 0.7964 0.9062
0.0915 13.0 52 0.7568 0.9062
0.0915 14.0 56 0.7334 0.9062
0.0043 15.0 60 0.6802 0.9062
0.0043 16.0 64 0.6424 0.9141
0.0043 17.0 68 0.6256 0.9141
0.0 18.0 72 0.6206 0.9141
0.0 19.0 76 0.6239 0.9141
0.0 20.0 80 0.6272 0.9141
0.0 21.0 84 0.6317 0.9141
0.0 22.0 88 0.6363 0.9141
0.0 23.0 92 0.6404 0.9141
0.0 24.0 96 0.6448 0.9141
0.0 25.0 100 0.6481 0.9062
0.0 26.0 104 0.6504 0.9062
0.0 27.0 108 0.6511 0.9062
0.0 28.0 112 0.6515 0.9062
0.0 29.0 116 0.6518 0.9141
0.0 30.0 120 0.6518 0.9141
0.0 31.0 124 0.6517 0.9141
0.0 32.0 128 0.6517 0.9141
0.0 33.0 132 0.6518 0.9141
0.0 34.0 136 0.6524 0.9141
0.0 35.0 140 0.6528 0.9141
0.0 36.0 144 0.6535 0.9141
0.0 37.0 148 0.6540 0.9141
0.0 38.0 152 0.6545 0.9141
0.0 39.0 156 0.6553 0.9141
0.0 40.0 160 0.6558 0.9141
0.0 41.0 164 0.6566 0.9141
0.0 42.0 168 0.6574 0.9141
0.0 43.0 172 0.6584 0.9141
0.0 44.0 176 0.6596 0.9141
0.0 45.0 180 0.6605 0.9141
0.0 46.0 184 0.6616 0.9141
0.0 47.0 188 0.6626 0.9141
0.0 48.0 192 0.6633 0.9141
0.0 49.0 196 0.6642 0.9219
0.0 50.0 200 0.6649 0.9219
0.0 51.0 204 0.6660 0.9219
0.0 52.0 208 0.6670 0.9219
0.0 53.0 212 0.6679 0.9219
0.0 54.0 216 0.6688 0.9219
0.0 55.0 220 0.6694 0.9219
0.0 56.0 224 0.6701 0.9219
0.0 57.0 228 0.6710 0.9219
0.0 58.0 232 0.6718 0.9219
0.0 59.0 236 0.6723 0.9219
0.0 60.0 240 0.6733 0.9219
0.0 61.0 244 0.6741 0.9219
0.0 62.0 248 0.6749 0.9219
0.0 63.0 252 0.6759 0.9219
0.0 64.0 256 0.6766 0.9219
0.0 65.0 260 0.6773 0.9219
0.0 66.0 264 0.6783 0.9219
0.0 67.0 268 0.6791 0.9219
0.0 68.0 272 0.6803 0.9219
0.0 69.0 276 0.6813 0.9219
0.0 70.0 280 0.6824 0.9219
0.0 71.0 284 0.6831 0.9219
0.0 72.0 288 0.6839 0.9219
0.0 73.0 292 0.6850 0.9219
0.0 74.0 296 0.6860 0.9219
0.0 75.0 300 0.6866 0.9219
0.0 76.0 304 0.6874 0.9219
0.0 77.0 308 0.6883 0.9219
0.0 78.0 312 0.6892 0.9219
0.0 79.0 316 0.6901 0.9219
0.0 80.0 320 0.6910 0.9219
0.0 81.0 324 0.6919 0.9219
0.0 82.0 328 0.6929 0.9219
0.0 83.0 332 0.6937 0.9219
0.0 84.0 336 0.6948 0.9219
0.0 85.0 340 0.6957 0.9219
0.0 86.0 344 0.6968 0.9219
0.0 87.0 348 0.6978 0.9219
0.0 88.0 352 0.6988 0.9219
0.0 89.0 356 0.6999 0.9219
0.0 90.0 360 0.7008 0.9219
0.0 91.0 364 0.7015 0.9219
0.0 92.0 368 0.7021 0.9219
0.0 93.0 372 0.7030 0.9219
0.0 94.0 376 0.7037 0.9219
0.0 95.0 380 0.7046 0.9219
0.0 96.0 384 0.7054 0.9219
0.0 97.0 388 0.7063 0.9219
0.0 98.0 392 0.7069 0.9219
0.0 99.0 396 0.7080 0.9219
0.0 100.0 400 0.7089 0.9219
0.0 101.0 404 0.7098 0.9219
0.0 102.0 408 0.7107 0.9219
0.0 103.0 412 0.7118 0.9219
0.0 104.0 416 0.7131 0.9219
0.0 105.0 420 0.7140 0.9219
0.0 106.0 424 0.7149 0.9219
0.0 107.0 428 0.7161 0.9219
0.0 108.0 432 0.7174 0.9219
0.0 109.0 436 0.7183 0.9219
0.0 110.0 440 0.7193 0.9219
0.0 111.0 444 0.7203 0.9219
0.0 112.0 448 0.7210 0.9219
0.0 113.0 452 0.7217 0.9219
0.0 114.0 456 0.7226 0.9219
0.0 115.0 460 0.7231 0.9219
0.0 116.0 464 0.7237 0.9219
0.0 117.0 468 0.7245 0.9219
0.0 118.0 472 0.7253 0.9219
0.0 119.0 476 0.7259 0.9219
0.0 120.0 480 0.7269 0.9219
0.0 121.0 484 0.7279 0.9219
0.0 122.0 488 0.7289 0.9219
0.0 123.0 492 0.7297 0.9219
0.0 124.0 496 0.7308 0.9219
0.0 125.0 500 0.7315 0.9219
0.0 126.0 504 0.7324 0.9219
0.0 127.0 508 0.7330 0.9219
0.0 128.0 512 0.7336 0.9219
0.0 129.0 516 0.7344 0.9219
0.0 130.0 520 0.7352 0.9219
0.0 131.0 524 0.7361 0.9219
0.0 132.0 528 0.7366 0.9219
0.0 133.0 532 0.7373 0.9219
0.0 134.0 536 0.7380 0.9219
0.0 135.0 540 0.7385 0.9219
0.0 136.0 544 0.7390 0.9219
0.0 137.0 548 0.7394 0.9219
0.0 138.0 552 0.7399 0.9219
0.0 139.0 556 0.7402 0.9219
0.0 140.0 560 0.7405 0.9219
0.0 141.0 564 0.7408 0.9219
0.0 142.0 568 0.7410 0.9219
0.0 143.0 572 0.7412 0.9219
0.0 144.0 576 0.7414 0.9219
0.0 145.0 580 0.7415 0.9219
0.0 146.0 584 0.7417 0.9219
0.0 147.0 588 0.7417 0.9219
0.0 148.0 592 0.7418 0.9219
0.0 149.0 596 0.7418 0.9219
0.0 150.0 600 0.7418 0.9219

Framework versions

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