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legal-bert-base-uncased

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metadata
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
  - generated_from_trainer
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: 1.2564
  • Accuracy: 0.8273
  • Precision: 0.8292
  • Recall: 0.8273
  • Precision Macro: 0.7794
  • Recall Macro: 0.7759
  • Macro Fpr: 0.0153
  • Weighted Fpr: 0.0147
  • Weighted Specificity: 0.9772
  • Macro Specificity: 0.9870
  • Weighted Sensitivity: 0.8273
  • Macro Sensitivity: 0.7759
  • F1 Micro: 0.8273
  • F1 Macro: 0.7741
  • F1 Weighted: 0.8269

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: 10
  • 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
1.2105 1.0 643 0.7916 0.7761 0.7729 0.7761 0.6230 0.5920 0.0214 0.0202 0.9664 0.9828 0.7761 0.5920 0.7761 0.5703 0.7551
0.6521 2.0 1286 0.6834 0.8025 0.8067 0.8025 0.7779 0.7152 0.0180 0.0173 0.9721 0.9850 0.8025 0.7152 0.8025 0.7181 0.7983
0.513 3.0 1929 0.8107 0.8141 0.8142 0.8141 0.7859 0.7227 0.0168 0.0160 0.9740 0.9859 0.8141 0.7227 0.8141 0.7261 0.8083
0.2635 4.0 2572 0.8442 0.8249 0.8285 0.8249 0.8298 0.7733 0.0156 0.0149 0.9759 0.9867 0.8249 0.7733 0.8249 0.7812 0.8242
0.1821 5.0 3215 0.9549 0.8226 0.8287 0.8226 0.8135 0.7623 0.0157 0.0152 0.9766 0.9866 0.8226 0.7623 0.8226 0.7758 0.8233
0.1123 6.0 3858 1.0790 0.8273 0.8316 0.8273 0.7865 0.7758 0.0152 0.0147 0.9779 0.9870 0.8273 0.7758 0.8273 0.7671 0.8268
0.0465 7.0 4501 1.1538 0.8280 0.8324 0.8280 0.7857 0.8054 0.0152 0.0146 0.9780 0.9871 0.8280 0.8054 0.8280 0.7890 0.8285
0.0256 8.0 5144 1.2413 0.8180 0.8263 0.8180 0.7780 0.8012 0.0162 0.0156 0.9771 0.9863 0.8180 0.8012 0.8180 0.7792 0.8196
0.0166 9.0 5787 1.2510 0.8218 0.8222 0.8218 0.7782 0.7600 0.0159 0.0152 0.9755 0.9865 0.8218 0.7600 0.8218 0.7660 0.8210
0.0107 10.0 6430 1.2564 0.8273 0.8292 0.8273 0.7794 0.7759 0.0153 0.0147 0.9772 0.9870 0.8273 0.7759 0.8273 0.7741 0.8269

Framework versions

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