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LiLT-SER-IT

This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on the xfun dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5355
  • Precision: 0.7262
  • Recall: 0.7927
  • F1: 0.7580
  • Accuracy: 0.7687

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0696 7.46 500 1.0876 0.6322 0.6517 0.6418 0.7584
0.0576 14.93 1000 1.3989 0.6712 0.7601 0.7129 0.7601
0.0096 22.39 1500 1.8059 0.6774 0.7639 0.7181 0.7662
0.0092 29.85 2000 2.0416 0.7266 0.7334 0.7300 0.7652
0.0003 37.31 2500 2.0467 0.7166 0.7539 0.7348 0.7628
0.0013 44.78 3000 2.0159 0.7027 0.7821 0.7403 0.7638
0.0013 52.24 3500 2.2751 0.6961 0.7728 0.7325 0.7575
0.0002 59.7 4000 2.2084 0.7236 0.7563 0.7396 0.7723
0.0002 67.16 4500 2.1843 0.7048 0.7701 0.7360 0.7581
0.0001 74.63 5000 2.2483 0.7366 0.7745 0.7551 0.7770
0.0001 82.09 5500 2.2685 0.7171 0.7752 0.7451 0.7677
0.0005 89.55 6000 2.2877 0.7180 0.7821 0.7487 0.7692
0.0001 97.01 6500 2.2574 0.7308 0.7725 0.7511 0.7721
0.0 104.48 7000 2.4696 0.7255 0.7862 0.7546 0.7660
0.0 111.94 7500 2.3996 0.7140 0.7917 0.7509 0.7725
0.0 119.4 8000 2.4592 0.7261 0.7852 0.7545 0.7665
0.0 126.87 8500 2.4129 0.7336 0.7900 0.7607 0.7718
0.0 134.33 9000 2.5367 0.7316 0.7896 0.7595 0.7666
0.0 141.79 9500 2.5327 0.7278 0.7900 0.7576 0.7663
0.0 149.25 10000 2.5355 0.7262 0.7927 0.7580 0.7687

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.1
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Evaluation results