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legal-NER
easwar03/legal-NER
legal-NER is a machine learning model from easwar03. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-sa-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- cc-by-sa-4.0
- Model type
- bert
- Access
- Public
- Created
- Nov 2, 2024
- Updated
- Nov 2, 2024
- SHA
- 53f76216
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
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
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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
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 1842micro 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
- Type
- bert
- License
- cc-by-sa-4.0
- Created
- Nov 2, 2024
- Updated
- Nov 2, 2024