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Data_extraction

Token Classification
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lilt
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Data_extraction

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4277
  • Fsc Code: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22}
  • Ame: {'precision': 0.391304347826087, 'recall': 0.42857142857142855, 'f1': 0.4090909090909091, 'number': 42}
  • Ccount No: {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6}
  • Ign: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}
  • Mount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}
  • Ther: {'precision': 0.5287356321839081, 'recall': 0.5348837209302325, 'f1': 0.5317919075144507, 'number': 86}
  • Overall Precision: 0.6045
  • Overall Recall: 0.6149
  • Overall F1: 0.6097
  • Overall Accuracy: 0.9431

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: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Fsc Code Ame Ccount No Ign Mount Ther Overall Precision Overall Recall Overall F1 Overall Accuracy
0.1559 20.0 200 0.2349 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.3448275862068966, 'recall': 0.47619047619047616, 'f1': 0.39999999999999997, 'number': 42} {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.4329896907216495, 'recall': 0.4883720930232558, 'f1': 0.45901639344262296, 'number': 86} 0.5155 0.5747 0.5435 0.9376
0.0138 40.0 400 0.2607 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.3148148148148148, 'recall': 0.40476190476190477, 'f1': 0.3541666666666667, 'number': 42} {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} {'precision': 1.0, 'recall': 0.8, 'f1': 0.888888888888889, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.5, 'recall': 0.5465116279069767, 'f1': 0.5222222222222221, 'number': 86} 0.5550 0.6092 0.5808 0.9372
0.0031 60.0 600 0.3808 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.2786885245901639, 'recall': 0.40476190476190477, 'f1': 0.33009708737864074, 'number': 42} {'precision': 1.0, 'recall': 0.6666666666666666, 'f1': 0.8, 'number': 6} {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.4077669902912621, 'recall': 0.4883720930232558, 'f1': 0.44444444444444436, 'number': 86} 0.4928 0.5920 0.5379 0.9372
0.0031 80.0 800 0.3239 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.2807017543859649, 'recall': 0.38095238095238093, 'f1': 0.32323232323232326, 'number': 42} {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 6} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.45, 'recall': 0.5232558139534884, 'f1': 0.48387096774193555, 'number': 86} 0.5248 0.6092 0.5638 0.9532
0.0007 100.0 1000 0.3718 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.375, 'recall': 0.42857142857142855, 'f1': 0.39999999999999997, 'number': 42} {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 6} {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.4891304347826087, 'recall': 0.5232558139534884, 'f1': 0.5056179775280899, 'number': 86} 0.5722 0.6149 0.5928 0.9467
0.0002 120.0 1200 0.4208 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.34, 'recall': 0.40476190476190477, 'f1': 0.36956521739130443, 'number': 42} {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 6} {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.4731182795698925, 'recall': 0.5116279069767442, 'f1': 0.4916201117318436, 'number': 86} 0.5474 0.5977 0.5714 0.9408
0.0003 140.0 1400 0.4155 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.3333333333333333, 'recall': 0.40476190476190477, 'f1': 0.3655913978494623, 'number': 42} {'precision': 0.8, 'recall': 0.6666666666666666, 'f1': 0.7272727272727272, 'number': 6} {'precision': 0.8333333333333334, 'recall': 1.0, 'f1': 0.9090909090909091, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.46808510638297873, 'recall': 0.5116279069767442, 'f1': 0.4888888888888889, 'number': 86} 0.5497 0.6034 0.5753 0.9397
0.0004 160.0 1600 0.4277 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.391304347826087, 'recall': 0.42857142857142855, 'f1': 0.4090909090909091, 'number': 42} {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.5287356321839081, 'recall': 0.5348837209302325, 'f1': 0.5317919075144507, 'number': 86} 0.6045 0.6149 0.6097 0.9431
0.0001 180.0 1800 0.3870 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.27586206896551724, 'recall': 0.38095238095238093, 'f1': 0.32, 'number': 42} {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.45, 'recall': 0.5232558139534884, 'f1': 0.48387096774193555, 'number': 86} 0.5149 0.5977 0.5532 0.9476
0.0001 200.0 2000 0.3956 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.3617021276595745, 'recall': 0.40476190476190477, 'f1': 0.3820224719101123, 'number': 42} {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.5056179775280899, 'recall': 0.5232558139534884, 'f1': 0.5142857142857142, 'number': 86} 0.5833 0.6034 0.5932 0.9526
0.0001 220.0 2200 0.4029 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.3469387755102041, 'recall': 0.40476190476190477, 'f1': 0.3736263736263736, 'number': 42} {'precision': 0.6, 'recall': 0.5, 'f1': 0.5454545454545454, 'number': 6} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.5, 'recall': 0.5348837209302325, 'f1': 0.5168539325842696, 'number': 86} 0.5699 0.6092 0.5889 0.9508
0.0 240.0 2400 0.4031 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22} {'precision': 0.34, 'recall': 0.40476190476190477, 'f1': 0.36956521739130443, 'number': 42} {'precision': 0.75, 'recall': 0.5, 'f1': 0.6, 'number': 6} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} {'precision': 0.4891304347826087, 'recall': 0.5232558139534884, 'f1': 0.5056179775280899, 'number': 86} 0.5645 0.6034 0.5833 0.9499

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Token Classification
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