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metadata
license: cc-by-sa-4.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: legal-bert-small-NER
    results: []

legal-bert-small-NER

This model is a fine-tuned version of nlpaueb/legal-bert-small-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2334

  • Accuracy: 0.9558

  • Precision: 0.7587

  • Recall: 0.7950

  • F1: 0.7764

  • Classification Report: precision recall f1-score support

       LOC       0.85      0.86      0.86      1668
      MISC       0.56      0.67      0.61       702
       ORG       0.68      0.67      0.68      1661
       PER       0.83      0.91      0.87      1617
    

    micro avg 0.76 0.79 0.78 5648 macro avg 0.73 0.78 0.75 5648

weighted avg 0.76 0.79 0.78 5648

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Classification Report
0.0289 1.0 434 0.2151 0.9555 0.7592 0.7890 0.7738 precision recall f1-score support
     LOC       0.86      0.85      0.86      1668
    MISC       0.57      0.67      0.62       702
     ORG       0.69      0.64      0.67      1661
     PER       0.81      0.92      0.86      1617

micro avg 0.76 0.79 0.77 5648 macro avg 0.73 0.77 0.75 5648 weighted avg 0.76 0.79 0.77 5648 | | 0.0193 | 2.0 | 868 | 0.2334 | 0.9558 | 0.7587 | 0.7950 | 0.7764 | precision recall f1-score support

     LOC       0.85      0.86      0.86      1668
    MISC       0.56      0.67      0.61       702
     ORG       0.68      0.67      0.68      1661
     PER       0.83      0.91      0.87      1617

micro avg 0.76 0.79 0.78 5648 macro avg 0.73 0.78 0.75 5648 weighted avg 0.76 0.79 0.78 5648 |

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.13.3