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distilbert-legal-definitions

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0034
  • Precision: 0.9668
  • Recall: 0.9707
  • Macro F1: 0.9688
  • Micro F1: 0.9688
  • Accuracy: 0.9994
  • Term F1: 0.9688
  • Term Precision: 0.9668
  • Term Recall: 0.9707

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Precision Recall Macro F1 Micro F1 Accuracy Term F1 Term Precision Term Recall
0.0049 1.0 2325 0.0034 0.9790 0.9580 0.9684 0.9684 0.9993 0.9684 0.9790 0.9580
0.0023 2.0 4650 0.0032 0.9669 0.9786 0.9727 0.9727 0.9994 0.9727 0.9669 0.9786
0.0013 3.0 6975 0.0018 0.9836 0.9794 0.9815 0.9815 0.9997 0.9815 0.9836 0.9794
0.0006 4.0 9300 0.0016 0.9879 0.9828 0.9854 0.9854 0.9997 0.9854 0.9879 0.9828

Framework versions

  • Transformers 4.21.3
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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