legal-small-NER

• Loss: 0.1923
• Accuracy: 0.9546
• Precision: 0.7409
• Recall: 0.7826
• F1: 0.7612
• Classification Report: precision recall f1-score support

     LOC       0.84      0.86      0.85      1668
    MISC       0.54      0.62      0.58       702
     ORG       0.64      0.67      0.65      1661
     PER       0.84      0.89      0.86      1617

  micro avg 0.74 0.78 0.76 5648 macro avg 0.71 0.76 0.74 5648 weighted avg 0.74 0.78 0.76 5648

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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: 3

Training results

     LOC       0.81      0.87      0.84      1668
    MISC       0.52      0.64      0.58       702
     ORG       0.65      0.58      0.61      1661
     PER       0.82      0.89      0.85      1617

     LOC       0.84      0.86      0.85      1668
    MISC       0.53      0.59      0.56       702
     ORG       0.62      0.68      0.65      1661
     PER       0.84      0.88      0.86      1617

     LOC       0.84      0.86      0.85      1668
    MISC       0.54      0.62      0.58       702
     ORG       0.64      0.67      0.65      1661
     PER       0.84      0.89      0.86      1617

Framework versions

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