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LiLT-SER-FR-SIN

This model is a fine-tuned version of kavg/LiLT-SER-FR on the xfun dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2426
  • Precision: 0.7618
  • Recall: 0.7956
  • F1: 0.7783
  • Accuracy: 0.8648

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
0.0057 21.74 500 0.8019 0.6884 0.7020 0.6951 0.8582
0.008 43.48 1000 1.0139 0.6963 0.7623 0.7278 0.8648
0.0006 65.22 1500 0.9878 0.7090 0.7562 0.7318 0.8592
0.0038 86.96 2000 1.2269 0.7104 0.7401 0.7250 0.8373
0.001 108.7 2500 0.9751 0.7276 0.7697 0.7481 0.8707
0.0004 130.43 3000 1.0918 0.7479 0.7672 0.7574 0.8538
0.0003 152.17 3500 1.0782 0.7102 0.7635 0.7359 0.8604
0.0 173.91 4000 1.0515 0.7402 0.7894 0.7640 0.8704
0.0001 195.65 4500 1.2154 0.7373 0.7709 0.7538 0.8419
0.0 217.39 5000 1.1026 0.7411 0.7722 0.7563 0.8642
0.0001 239.13 5500 1.0594 0.7262 0.7512 0.7385 0.8576
0.0 260.87 6000 1.1103 0.7377 0.7759 0.7563 0.8609
0.0 282.61 6500 1.1591 0.7267 0.7599 0.7429 0.8610
0.0 304.35 7000 1.2382 0.7574 0.7537 0.7556 0.8562
0.0 326.09 7500 1.2027 0.7485 0.7882 0.7678 0.8578
0.0001 347.83 8000 1.1492 0.7433 0.7808 0.7616 0.8659
0.0002 369.57 8500 1.1924 0.7570 0.7980 0.7770 0.8655
0.0 391.3 9000 1.2426 0.7618 0.7956 0.7783 0.8648
0.0 413.04 9500 1.3078 0.7620 0.7808 0.7713 0.8597
0.0 434.78 10000 1.3219 0.7639 0.7771 0.7705 0.8579

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