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legal-bert-base-uncased

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metadata
license: cc-by-sa-4.0
tags:
  - generated_from_trainer
base_model: nlpaueb/legal-bert-base-uncased
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: legal-bert-base-uncased
    results: []

legal-bert-base-uncased

This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1536
  • Accuracy: 0.8203
  • Precision: 0.8212
  • Recall: 0.8203
  • Precision Macro: 0.7660
  • Recall Macro: 0.7548
  • Macro Fpr: 0.0156
  • Weighted Fpr: 0.0150
  • Weighted Specificity: 0.9766
  • Macro Specificity: 0.9867
  • Weighted Sensitivity: 0.8242
  • Macro Sensitivity: 0.7548
  • F1 Micro: 0.8242
  • F1 Macro: 0.7566
  • F1 Weighted: 0.8221

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
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall Precision Macro Recall Macro Macro Fpr Weighted Fpr Weighted Specificity Macro Specificity Weighted Sensitivity Macro Sensitivity F1 Micro F1 Macro F1 Weighted
1.1096 1.0 643 0.6748 0.7978 0.7855 0.7978 0.6239 0.6340 0.0188 0.0178 0.9702 0.9845 0.7978 0.6340 0.7978 0.6134 0.7840
0.6187 2.0 1286 0.6449 0.8110 0.8196 0.8110 0.7806 0.7327 0.0169 0.0164 0.9755 0.9858 0.8110 0.7327 0.8110 0.7268 0.8090
0.4747 3.0 1929 0.8151 0.8149 0.8192 0.8149 0.7659 0.7390 0.0166 0.0160 0.9761 0.9861 0.8149 0.7390 0.8149 0.7370 0.8125
0.2645 4.0 2572 0.9345 0.8218 0.8198 0.8218 0.7446 0.7413 0.0158 0.0152 0.9774 0.9866 0.8218 0.7413 0.8218 0.7385 0.8189
0.1901 5.0 3215 1.0929 0.8195 0.8242 0.8195 0.8264 0.7432 0.0161 0.0155 0.9750 0.9863 0.8195 0.7432 0.8195 0.7595 0.8166
0.1131 6.0 3858 1.1536 0.8203 0.8212 0.8203 0.7968 0.7786 0.0159 0.0154 0.9766 0.9865 0.8203 0.7786 0.8203 0.7840 0.8197
0.063 7.0 4501 1.3218 0.8118 0.8184 0.8118 0.7518 0.7526 0.0166 0.0163 0.9773 0.9859 0.8118 0.7526 0.8118 0.7495 0.8136
0.0264 8.0 5144 1.3863 0.8257 0.8262 0.8257 0.7784 0.7768 0.0155 0.0149 0.9768 0.9868 0.8257 0.7768 0.8257 0.7730 0.8247
0.03 9.0 5787 1.5542 0.8079 0.8167 0.8079 0.7639 0.7653 0.0172 0.0167 0.9744 0.9855 0.8079 0.7653 0.8079 0.7595 0.8096
0.0149 10.0 6430 1.5835 0.8141 0.8155 0.8141 0.7545 0.7361 0.0168 0.0160 0.9730 0.9858 0.8141 0.7361 0.8141 0.7412 0.8127
0.005 11.0 7073 1.5325 0.8242 0.8250 0.8242 0.7805 0.7812 0.0156 0.0150 0.9758 0.9867 0.8242 0.7812 0.8242 0.7681 0.8226
0.003 12.0 7716 1.5714 0.8288 0.8299 0.8288 0.7701 0.7679 0.0152 0.0145 0.9765 0.9870 0.8288 0.7679 0.8288 0.7626 0.8276
0.0033 13.0 8359 1.5511 0.8249 0.8219 0.8249 0.7676 0.7598 0.0156 0.0149 0.9760 0.9867 0.8249 0.7598 0.8249 0.7608 0.8225
0.0018 14.0 9002 1.5510 0.8249 0.8225 0.8249 0.7686 0.7554 0.0155 0.0149 0.9767 0.9868 0.8249 0.7554 0.8249 0.7572 0.8224
0.0008 15.0 9645 1.5469 0.8242 0.8220 0.8242 0.7660 0.7548 0.0156 0.0150 0.9766 0.9867 0.8242 0.7548 0.8242 0.7566 0.8221

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.2