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xshubhamx
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InLegalBERT

Text Classification
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bert
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Metrics

  • loss: 1.0342
  • accuracy: 0.8359
  • precision: 0.8409
  • recall: 0.8359
  • precision_macro: 0.8136
  • recall_macro: 0.8000
  • macro_fpr: 0.0142
  • weighted_fpr: 0.0138
  • weighted_specificity: 0.9792
  • macro_specificity: 0.9877
  • weighted_sensitivity: 0.8359
  • macro_sensitivity: 0.8000
  • f1_micro: 0.8359
  • f1_macro: 0.8010
  • f1_weighted: 0.8352
  • runtime: 19.9583
  • samples_per_second: 64.4340
  • steps_per_second: 8.0670

Metrics

  • loss: 1.0345
  • accuracy: 0.8358
  • precision: 0.8408
  • recall: 0.8358
  • precision_macro: 0.8207
  • recall_macro: 0.7957
  • macro_fpr: 0.0143
  • weighted_fpr: 0.0138
  • weighted_specificity: 0.9790
  • macro_specificity: 0.9877
  • weighted_sensitivity: 0.8358
  • macro_sensitivity: 0.7957
  • f1_micro: 0.8358
  • f1_macro: 0.8020
  • f1_weighted: 0.8352
  • runtime: 22.0569
  • samples_per_second: 58.5300
  • steps_per_second: 7.3450

InLegalBERT

This model is a fine-tuned version of law-ai/InLegalBERT on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0763
  • Accuracy: 0.8304
  • Precision: 0.8363
  • Recall: 0.8304
  • Precision Macro: 0.7959
  • Recall Macro: 0.8029
  • Macro Fpr: 0.0150
  • Weighted Fpr: 0.0145
  • Weighted Specificity: 0.9774
  • Macro Specificity: 0.9871
  • Weighted Sensitivity: 0.8296
  • Macro Sensitivity: 0.8029
  • F1 Micro: 0.8296
  • F1 Macro: 0.7954
  • F1 Weighted: 0.8283

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
1.065 1.0 643 0.6395 0.7994 0.7818 0.7994 0.6194 0.6308 0.0185 0.0176 0.9714 0.9847 0.7994 0.6308 0.7994 0.6029 0.7804
0.5866 2.0 1286 0.6907 0.8187 0.8199 0.8187 0.7285 0.7366 0.0161 0.0156 0.9765 0.9864 0.8187 0.7366 0.8187 0.7276 0.8152
0.4622 3.0 1929 0.8056 0.8180 0.8137 0.8180 0.7227 0.7376 0.0162 0.0156 0.9764 0.9863 0.8180 0.7376 0.8180 0.7283 0.8150
0.2398 4.0 2572 0.9310 0.8172 0.8235 0.8172 0.7661 0.7425 0.0161 0.0157 0.9762 0.9862 0.8172 0.7425 0.8172 0.7407 0.8161
0.1611 5.0 3215 1.0763 0.8304 0.8363 0.8304 0.8174 0.7918 0.0148 0.0144 0.9784 0.9873 0.8304 0.7918 0.8304 0.7986 0.8304
0.1055 6.0 3858 1.1377 0.8257 0.8275 0.8257 0.8039 0.7810 0.0154 0.0149 0.9775 0.9869 0.8257 0.7810 0.8257 0.7863 0.8246
0.0463 7.0 4501 1.3215 0.8071 0.8111 0.8071 0.7692 0.7689 0.0172 0.0168 0.9761 0.9856 0.8071 0.7689 0.8071 0.7661 0.8078
0.031 8.0 5144 1.3483 0.8203 0.8170 0.8203 0.7773 0.7727 0.0161 0.0154 0.9751 0.9864 0.8203 0.7727 0.8203 0.7690 0.8175
0.0202 9.0 5787 1.3730 0.8280 0.8263 0.8280 0.7818 0.7803 0.0152 0.0146 0.9779 0.9871 0.8280 0.7803 0.8280 0.7753 0.8256
0.0133 10.0 6430 1.5407 0.8164 0.8163 0.8164 0.7688 0.7779 0.0165 0.0158 0.9751 0.9861 0.8164 0.7779 0.8164 0.7655 0.8135
0.0051 11.0 7073 1.5235 0.8226 0.8265 0.8226 0.7900 0.7680 0.0156 0.0152 0.9769 0.9866 0.8226 0.7680 0.8226 0.7744 0.8234
0.0027 12.0 7716 1.5643 0.8265 0.8259 0.8265 0.7805 0.7841 0.0154 0.0148 0.9772 0.9869 0.8265 0.7841 0.8265 0.7775 0.8245
0.002 13.0 8359 1.5516 0.8280 0.8273 0.8280 0.7882 0.7902 0.0152 0.0146 0.9779 0.9871 0.8280 0.7902 0.8280 0.7860 0.8262
0.0015 14.0 9002 1.5835 0.8273 0.8268 0.8273 0.7943 0.8022 0.0153 0.0147 0.9773 0.9870 0.8273 0.8022 0.8273 0.7943 0.8259
0.0007 15.0 9645 1.5914 0.8296 0.8293 0.8296 0.7959 0.8029 0.0150 0.0145 0.9774 0.9871 0.8296 0.8029 0.8296 0.7954 0.8283

Framework versions

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