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legal-NER

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bert
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+ ---
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+ license: cc-by-sa-4.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: legal-NER
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # legal-NER
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0068
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+ - Accuracy: 0.9990
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+ - Precision: 0.9931
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+ - Recall: 0.9944
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+ - F1: 0.9938
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+ - Classification Report: precision recall f1-score support
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+
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+ LOC 1.00 1.00 1.00 1837
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+ MISC 0.98 0.98 0.98 922
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+ ORG 1.00 0.99 0.99 1341
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+ PER 1.00 1.00 1.00 1842
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+
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+ micro avg 0.99 0.99 0.99 5942
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+ macro avg 0.99 0.99 0.99 5942
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+ weighted avg 0.99 0.99 0.99 5942
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+
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Classification Report |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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+ | 0.1501 | 1.0 | 217 | 0.0704 | 0.9810 | 0.8615 | 0.8901 | 0.8756 | precision recall f1-score support
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+
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+ LOC 0.86 0.95 0.91 1837
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+ MISC 0.74 0.70 0.72 922
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+ ORG 0.80 0.82 0.81 1341
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+ PER 0.97 0.97 0.97 1842
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+
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+ micro avg 0.86 0.89 0.88 5942
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+ macro avg 0.84 0.86 0.85 5942
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+ weighted avg 0.86 0.89 0.87 5942
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+ |
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+ | 0.0682 | 2.0 | 434 | 0.0266 | 0.9929 | 0.9513 | 0.9631 | 0.9572 | precision recall f1-score support
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+
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+ LOC 0.98 0.98 0.98 1837
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+ MISC 0.88 0.91 0.89 922
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+ ORG 0.92 0.96 0.94 1341
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+ PER 0.99 0.97 0.98 1842
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+
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+ micro avg 0.95 0.96 0.96 5942
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+ macro avg 0.94 0.96 0.95 5942
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+ weighted avg 0.95 0.96 0.96 5942
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+ |
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+ | 0.0362 | 3.0 | 651 | 0.0137 | 0.9970 | 0.9776 | 0.9850 | 0.9813 | precision recall f1-score support
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+
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+ LOC 0.98 1.00 0.99 1837
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+ MISC 0.94 0.95 0.94 922
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+ ORG 0.98 0.98 0.98 1341
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+ PER 0.99 1.00 1.00 1842
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+
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+ micro avg 0.98 0.99 0.98 5942
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+ macro avg 0.97 0.98 0.98 5942
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+ weighted avg 0.98 0.99 0.98 5942
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+ |
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+ | 0.0209 | 4.0 | 868 | 0.0079 | 0.9986 | 0.9894 | 0.9918 | 0.9906 | precision recall f1-score support
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+
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+ LOC 0.99 1.00 1.00 1837
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+ MISC 0.98 0.97 0.97 922
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+ ORG 0.99 0.99 0.99 1341
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+ PER 1.00 1.00 1.00 1842
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+
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+ micro avg 0.99 0.99 0.99 5942
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+ macro avg 0.99 0.99 0.99 5942
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+ weighted avg 0.99 0.99 0.99 5942
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+ |
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+ | 0.0143 | 5.0 | 1085 | 0.0068 | 0.9990 | 0.9931 | 0.9944 | 0.9938 | precision recall f1-score support
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+
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+ LOC 1.00 1.00 1.00 1837
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+ MISC 0.98 0.98 0.98 922
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+ ORG 1.00 0.99 0.99 1341
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+ PER 1.00 1.00 1.00 1842
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+
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+ micro avg 0.99 0.99 0.99 5942
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+ macro avg 0.99 0.99 0.99 5942
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+ weighted avg 0.99 0.99 0.99 5942
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+ |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.2
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+ - Pytorch 2.0.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.13.3