--- 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](https://huggingface.co/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