absa

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: 1.0417
  • Accuracy: 0.8779
  • F1: 0.7493

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: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.4543 1.0 298 0.3298 0.8707 0.6817
0.2805 2.0 596 0.3210 0.8798 0.7341
0.208 3.0 894 0.3442 0.8841 0.7757
0.167 4.0 1192 0.3991 0.8841 0.7528
0.131 5.0 1490 0.4410 0.8798 0.7546
0.1039 6.0 1788 0.5180 0.8784 0.7626
0.0863 7.0 2086 0.5390 0.8793 0.7588
0.073 8.0 2384 0.5781 0.8702 0.7600
0.0651 9.0 2682 0.5738 0.875 0.7503
0.0535 10.0 2980 0.5999 0.8755 0.7501
0.0389 11.0 3278 0.6803 0.8760 0.7553
0.0385 12.0 3576 0.7202 0.8836 0.7301
0.0388 13.0 3874 0.7378 0.8774 0.7323
0.0324 14.0 4172 0.8089 0.8740 0.7334
0.0278 15.0 4470 0.7891 0.8769 0.7540
0.0224 16.0 4768 0.8061 0.8740 0.7448
0.022 17.0 5066 0.8205 0.8716 0.7492
0.0208 18.0 5364 0.7715 0.8716 0.7271
0.0184 19.0 5662 0.8142 0.8803 0.7440
0.0173 20.0 5960 0.8908 0.8764 0.7480
0.0149 21.0 6258 0.8814 0.8731 0.7427
0.0145 22.0 6556 0.8972 0.8784 0.7416
0.0161 23.0 6854 0.8861 0.8736 0.7395
0.0164 24.0 7152 0.9344 0.8736 0.7498
0.0168 25.0 7450 0.9008 0.8740 0.7466
0.0147 26.0 7748 0.9498 0.8769 0.7456
0.0124 27.0 8046 0.9168 0.8712 0.7254
0.0128 28.0 8344 0.9192 0.8774 0.7362
0.0138 29.0 8642 0.9745 0.8788 0.7612
0.0125 30.0 8940 0.9276 0.8784 0.7455
0.0118 31.0 9238 1.0205 0.8707 0.7552
0.0123 32.0 9536 0.9628 0.8764 0.7486
0.0139 33.0 9834 1.0042 0.8745 0.7541
0.0126 34.0 10132 0.9834 0.8760 0.7461
0.013 35.0 10430 0.9986 0.8769 0.7450
0.0134 36.0 10728 0.9907 0.8788 0.7490
0.0135 37.0 11026 1.0038 0.8736 0.7458
0.0121 38.0 11324 1.0175 0.8740 0.7476
0.0122 39.0 11622 1.0053 0.8755 0.7499
0.0112 40.0 11920 1.0120 0.8784 0.7467
0.0115 41.0 12218 1.0084 0.8764 0.7448
0.0129 42.0 12516 1.0021 0.8798 0.7491
0.0107 43.0 12814 1.0105 0.8784 0.7476
0.0108 44.0 13112 1.0131 0.8774 0.7454
0.0114 45.0 13410 1.0363 0.875 0.7504
0.0115 46.0 13708 1.0333 0.8798 0.7553
0.0106 47.0 14006 1.0297 0.8788 0.7500
0.0102 48.0 14304 1.0378 0.8779 0.7494
0.01 49.0 14602 1.0414 0.8769 0.7469
0.0107 50.0 14900 1.0417 0.8779 0.7493

Framework versions

  • Transformers 4.50.2
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1
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