highlightedreport-classifier-test

This model is a fine-tuned version of latterworks/highlightedreport-classifier-test on the None dataset. It achieves the following results on the evaluation set:

  • Accuracy: 0.7840
  • Loss: 0.6084
  • F1: 0.7701
  • Precision: 0.7694
  • Recall: 0.7708

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: 128
  • eval_batch_size: 128
  • 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
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Accuracy Validation Loss F1 Precision Recall
0.6461 0.2375 100 0.7873 0.5155 0.7772 0.7647 0.7900
0.4988 0.4751 200 0.8014 0.4455 0.7839 0.8012 0.7673
0.4784 0.7126 300 0.7944 0.4508 0.7914 0.7554 0.8310
0.4827 0.9501 400 0.7793 0.4632 0.7836 0.7259 0.8512
0.4628 1.1876 500 0.7944 0.4459 0.7819 0.7787 0.7851
0.4622 1.4252 600 0.7907 0.4591 0.7890 0.7488 0.8338
0.4508 1.6627 700 0.7927 0.4550 0.7875 0.7590 0.8181
0.4572 1.9002 800 0.7903 0.4516 0.7639 0.8104 0.7224
0.4359 2.1378 900 0.7917 0.4772 0.7874 0.7558 0.8217
0.3967 2.3753 1000 0.7984 0.4567 0.7797 0.8003 0.7601
0.415 2.6128 1100 0.7852 0.4792 0.7847 0.7408 0.8342
0.4097 2.8504 1200 0.7965 0.4661 0.7847 0.7795 0.7900
0.3962 3.0879 1300 0.7972 0.4655 0.7738 0.8120 0.7391
0.3738 3.3254 1400 0.7887 0.4740 0.7806 0.7612 0.8011
0.3618 3.5629 1500 0.7935 0.4706 0.7720 0.8015 0.7445
0.3604 3.8005 1600 0.7942 0.4779 0.78 0.7828 0.7772
0.3533 4.0380 1700 0.7892 0.4899 0.7752 0.7760 0.7744
0.3194 4.2755 1800 0.7902 0.5034 0.7785 0.7717 0.7854
0.3285 4.5131 1900 0.7893 0.4958 0.7767 0.7730 0.7804
0.3256 4.7506 2000 0.7908 0.4952 0.7720 0.7905 0.7544
0.321 4.9881 2100 0.7873 0.5050 0.7760 0.7675 0.7847
0.2915 5.2257 2200 0.7872 0.5167 0.7722 0.7761 0.7683
0.2819 5.4632 2300 0.7828 0.5344 0.7745 0.7554 0.7947
0.2839 5.7007 2400 0.7812 0.5529 0.7761 0.7465 0.8082
0.2836 5.9382 2500 0.7771 0.5433 0.7741 0.7384 0.8135
0.2686 6.1758 2600 0.7832 0.5545 0.7692 0.7687 0.7698
0.2559 6.4133 2700 0.7820 0.5578 0.7728 0.7564 0.7900
0.2525 6.6508 2800 0.7837 0.5647 0.7703 0.7678 0.7730
0.251 6.8884 2900 0.7867 0.5588 0.7713 0.7764 0.7662
0.2463 7.1259 3000 0.7877 0.5754 0.7738 0.7739 0.7737
0.2284 7.3634 3100 0.7842 0.5907 0.7758 0.7571 0.7954
0.2295 7.6010 3200 0.7835 0.5832 0.7654 0.7789 0.7523
0.234 7.8385 3300 0.7807 0.5821 0.7670 0.7650 0.7690
0.2296 8.0760 3400 0.7850 0.5823 0.7667 0.7813 0.7527
0.2161 8.3135 3500 0.7837 0.5908 0.7694 0.7699 0.7690
0.2253 8.5511 3600 0.7857 0.5907 0.7648 0.7887 0.7423
0.21 8.7886 3700 0.7835 0.6021 0.7719 0.7636 0.7804
0.2123 9.0261 3800 0.7840 0.6025 0.7691 0.7720 0.7662
0.1977 9.2637 3900 0.7827 0.6081 0.7655 0.7755 0.7559
0.2061 9.5012 4000 0.7838 0.6090 0.7715 0.7656 0.7776
0.2032 9.7387 4100 0.7850 0.6081 0.7718 0.7690 0.7747
0.2077 9.9762 4200 0.7838 0.6084 0.7699 0.7694 0.7705

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.4.1
  • Tokenizers 0.21.1
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