--- license: cc-by-sa-4.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: legal-bert-small results: [] --- # legal-bert-small This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1837 - Accuracy: 0.9548 - Precision: 0.7491 - Recall: 0.7882 - F1: 0.7682 - Classification Report: precision recall f1-score support LOC 0.84 0.86 0.85 1668 MISC 0.59 0.63 0.61 702 ORG 0.64 0.67 0.66 1661 PER 0.83 0.90 0.87 1617 micro avg 0.75 0.79 0.77 5648 macro avg 0.73 0.77 0.75 5648 weighted avg 0.75 0.79 0.77 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Classification Report | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.1443 | 1.0 | 434 | 0.1949 | 0.9462 | 0.6982 | 0.7466 | 0.7216 | precision recall f1-score support LOC 0.83 0.80 0.81 1668 MISC 0.59 0.62 0.60 702 ORG 0.56 0.58 0.57 1661 PER 0.75 0.92 0.83 1617 micro avg 0.70 0.75 0.72 5648 macro avg 0.68 0.73 0.70 5648 weighted avg 0.70 0.75 0.72 5648 | | 0.071 | 2.0 | 868 | 0.1764 | 0.9551 | 0.7548 | 0.7702 | 0.7624 | precision recall f1-score support LOC 0.81 0.88 0.84 1668 MISC 0.59 0.64 0.61 702 ORG 0.68 0.59 0.63 1661 PER 0.83 0.90 0.86 1617 micro avg 0.75 0.77 0.76 5648 macro avg 0.73 0.75 0.74 5648 weighted avg 0.75 0.77 0.76 5648 | | 0.0713 | 3.0 | 1302 | 0.1837 | 0.9548 | 0.7491 | 0.7882 | 0.7682 | precision recall f1-score support LOC 0.84 0.86 0.85 1668 MISC 0.59 0.63 0.61 702 ORG 0.64 0.67 0.66 1661 PER 0.83 0.90 0.87 1617 micro avg 0.75 0.79 0.77 5648 macro avg 0.73 0.77 0.75 5648 weighted avg 0.75 0.79 0.77 5648 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3