metadata

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

legal-NER

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

Loss: 0.0068
Accuracy: 0.9990
Precision: 0.9931
Recall: 0.9944
F1: 0.9938

Classification Report: precision recall f1-score support

   LOC       1.00      1.00      1.00      1837
  MISC       0.98      0.98      0.98       922
   ORG       1.00      0.99      0.99      1341
   PER       1.00      1.00      1.00      1842

micro avg 0.99 0.99 0.99 5942 macro avg 0.99 0.99 0.99 5942

weighted avg 0.99 0.99 0.99 5942

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Classification Report
0.1501	1.0	217	0.0704	0.9810	0.8615	0.8901	0.8756	precision recall f1-score support

     LOC       0.86      0.95      0.91      1837
    MISC       0.74      0.70      0.72       922
     ORG       0.80      0.82      0.81      1341
     PER       0.97      0.97      0.97      1842

micro avg 0.86 0.89 0.88 5942 macro avg 0.84 0.86 0.85 5942 weighted avg 0.86 0.89 0.87 5942 | | 0.0682 | 2.0 | 434 | 0.0266 | 0.9929 | 0.9513 | 0.9631 | 0.9572 | precision recall f1-score support

     LOC       0.98      0.98      0.98      1837
    MISC       0.88      0.91      0.89       922
     ORG       0.92      0.96      0.94      1341
     PER       0.99      0.97      0.98      1842

micro avg 0.95 0.96 0.96 5942 macro avg 0.94 0.96 0.95 5942 weighted avg 0.95 0.96 0.96 5942 | | 0.0362 | 3.0 | 651 | 0.0137 | 0.9970 | 0.9776 | 0.9850 | 0.9813 | precision recall f1-score support

     LOC       0.98      1.00      0.99      1837
    MISC       0.94      0.95      0.94       922
     ORG       0.98      0.98      0.98      1341
     PER       0.99      1.00      1.00      1842

micro avg 0.98 0.99 0.98 5942 macro avg 0.97 0.98 0.98 5942 weighted avg 0.98 0.99 0.98 5942 | | 0.0209 | 4.0 | 868 | 0.0079 | 0.9986 | 0.9894 | 0.9918 | 0.9906 | precision recall f1-score support

     LOC       0.99      1.00      1.00      1837
    MISC       0.98      0.97      0.97       922
     ORG       0.99      0.99      0.99      1341
     PER       1.00      1.00      1.00      1842

micro avg 0.99 0.99 0.99 5942 macro avg 0.99 0.99 0.99 5942 weighted avg 0.99 0.99 0.99 5942 | | 0.0143 | 5.0 | 1085 | 0.0068 | 0.9990 | 0.9931 | 0.9944 | 0.9938 | precision recall f1-score support

     LOC       1.00      1.00      1.00      1837
    MISC       0.98      0.98      0.98       922
     ORG       1.00      0.99      0.99      1341
     PER       1.00      1.00      1.00      1842

micro avg 0.99 0.99 0.99 5942 macro avg 0.99 0.99 0.99 5942 weighted avg 0.99 0.99 0.99 5942 |

Framework versions

Transformers 4.30.2
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3