|
|
---
|
|
|
library_name: transformers
|
|
|
license: cc-by-sa-4.0
|
|
|
base_model: nlpaueb/legal-bert-base-uncased
|
|
|
tags:
|
|
|
- generated_from_trainer
|
|
|
metrics:
|
|
|
- accuracy
|
|
|
- precision
|
|
|
- recall
|
|
|
- f1
|
|
|
model-index:
|
|
|
- name: legal-NER
|
|
|
results: []
|
|
|
---
|
|
|
|
|
|
<!-- 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](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
|
|
|
It achieves the following results on the evaluation set:
|
|
|
- Loss: 0.0922
|
|
|
- Accuracy: 0.9840
|
|
|
- Precision: 0.9221
|
|
|
- Recall: 0.9260
|
|
|
- F1: 0.9240
|
|
|
- Classification Report: precision recall f1-score support
|
|
|
|
|
|
LOC 0.94 0.96 0.95 1837
|
|
|
MISC 0.88 0.87 0.87 922
|
|
|
ORG 0.88 0.87 0.88 1341
|
|
|
PER 0.96 0.96 0.96 1842
|
|
|
|
|
|
micro avg 0.92 0.93 0.92 5942
|
|
|
macro avg 0.91 0.91 0.91 5942
|
|
|
weighted avg 0.92 0.93 0.92 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: 8
|
|
|
- eval_batch_size: 8
|
|
|
- seed: 42
|
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
|
|
- lr_scheduler_type: linear
|
|
|
- num_epochs: 5
|
|
|
- mixed_precision_training: Native AMP
|
|
|
|
|
|
### Training results
|
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Classification Report |
|
|
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
|
|
|
| 0.1354 | 0.2668 | 500 | 0.1185 | 0.9686 | 0.8405 | 0.8304 | 0.8354 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.83 0.94 0.88 1837
|
|
|
MISC 0.78 0.70 0.74 922
|
|
|
ORG 0.81 0.62 0.70 1341
|
|
|
PER 0.90 0.94 0.92 1842
|
|
|
|
|
|
micro avg 0.84 0.83 0.84 5942
|
|
|
macro avg 0.83 0.80 0.81 5942
|
|
|
weighted avg 0.84 0.83 0.83 5942
|
|
|
|
|
|
|
| 0.0971 | 0.5336 | 1000 | 0.1045 | 0.9744 | 0.8578 | 0.8721 | 0.8649 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.86 0.96 0.91 1837
|
|
|
MISC 0.89 0.71 0.79 922
|
|
|
ORG 0.78 0.74 0.76 1341
|
|
|
PER 0.89 0.96 0.93 1842
|
|
|
|
|
|
micro avg 0.86 0.87 0.86 5942
|
|
|
macro avg 0.86 0.84 0.85 5942
|
|
|
weighted avg 0.86 0.87 0.86 5942
|
|
|
|
|
|
|
| 0.097 | 0.8004 | 1500 | 0.0849 | 0.9776 | 0.8884 | 0.8812 | 0.8848 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.93 0.91 0.92 1837
|
|
|
MISC 0.77 0.82 0.79 922
|
|
|
ORG 0.82 0.83 0.82 1341
|
|
|
PER 0.96 0.92 0.94 1842
|
|
|
|
|
|
micro avg 0.89 0.88 0.88 5942
|
|
|
macro avg 0.87 0.87 0.87 5942
|
|
|
weighted avg 0.89 0.88 0.89 5942
|
|
|
|
|
|
|
| 0.0522 | 1.0672 | 2000 | 0.0838 | 0.9791 | 0.9014 | 0.8955 | 0.8984 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.93 0.95 0.94 1837
|
|
|
MISC 0.82 0.81 0.82 922
|
|
|
ORG 0.88 0.79 0.83 1341
|
|
|
PER 0.93 0.97 0.95 1842
|
|
|
|
|
|
micro avg 0.90 0.90 0.90 5942
|
|
|
macro avg 0.89 0.88 0.88 5942
|
|
|
weighted avg 0.90 0.90 0.90 5942
|
|
|
|
|
|
|
| 0.0491 | 1.3340 | 2500 | 0.0734 | 0.9814 | 0.9021 | 0.9088 | 0.9054 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.92 0.95 0.93 1837
|
|
|
MISC 0.86 0.82 0.84 922
|
|
|
ORG 0.84 0.85 0.84 1341
|
|
|
PER 0.95 0.96 0.96 1842
|
|
|
|
|
|
micro avg 0.90 0.91 0.91 5942
|
|
|
macro avg 0.89 0.89 0.89 5942
|
|
|
weighted avg 0.90 0.91 0.91 5942
|
|
|
|
|
|
|
| 0.0435 | 1.6009 | 3000 | 0.0891 | 0.9776 | 0.8685 | 0.8972 | 0.8826 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.93 0.94 0.94 1837
|
|
|
MISC 0.78 0.82 0.80 922
|
|
|
ORG 0.74 0.90 0.81 1341
|
|
|
PER 0.97 0.89 0.93 1842
|
|
|
|
|
|
micro avg 0.87 0.90 0.88 5942
|
|
|
macro avg 0.86 0.89 0.87 5942
|
|
|
weighted avg 0.88 0.90 0.89 5942
|
|
|
|
|
|
|
| 0.0341 | 1.8677 | 3500 | 0.0777 | 0.9813 | 0.9072 | 0.9111 | 0.9092 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.91 0.96 0.94 1837
|
|
|
MISC 0.87 0.84 0.85 922
|
|
|
ORG 0.86 0.83 0.85 1341
|
|
|
PER 0.95 0.96 0.95 1842
|
|
|
|
|
|
micro avg 0.91 0.91 0.91 5942
|
|
|
macro avg 0.90 0.90 0.90 5942
|
|
|
weighted avg 0.91 0.91 0.91 5942
|
|
|
|
|
|
|
| 0.0246 | 2.1345 | 4000 | 0.0838 | 0.9813 | 0.8991 | 0.9174 | 0.9081 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.92 0.96 0.94 1837
|
|
|
MISC 0.86 0.82 0.84 922
|
|
|
ORG 0.87 0.85 0.86 1341
|
|
|
PER 0.92 0.97 0.95 1842
|
|
|
|
|
|
micro avg 0.90 0.92 0.91 5942
|
|
|
macro avg 0.89 0.90 0.90 5942
|
|
|
weighted avg 0.90 0.92 0.91 5942
|
|
|
|
|
|
|
| 0.0205 | 2.4013 | 4500 | 0.0764 | 0.9830 | 0.9104 | 0.9204 | 0.9154 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.96 0.94 0.95 1837
|
|
|
MISC 0.84 0.86 0.85 922
|
|
|
ORG 0.82 0.88 0.85 1341
|
|
|
PER 0.96 0.96 0.96 1842
|
|
|
|
|
|
micro avg 0.91 0.92 0.92 5942
|
|
|
macro avg 0.90 0.91 0.90 5942
|
|
|
weighted avg 0.91 0.92 0.92 5942
|
|
|
|
|
|
|
| 0.022 | 2.6681 | 5000 | 0.0856 | 0.9819 | 0.9051 | 0.9192 | 0.9121 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.92 0.96 0.94 1837
|
|
|
MISC 0.87 0.84 0.85 922
|
|
|
ORG 0.85 0.85 0.85 1341
|
|
|
PER 0.95 0.97 0.96 1842
|
|
|
|
|
|
micro avg 0.91 0.92 0.91 5942
|
|
|
macro avg 0.90 0.90 0.90 5942
|
|
|
weighted avg 0.90 0.92 0.91 5942
|
|
|
|
|
|
|
| 0.0244 | 2.9349 | 5500 | 0.0850 | 0.9829 | 0.9142 | 0.9194 | 0.9168 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.94 0.96 0.95 1837
|
|
|
MISC 0.88 0.84 0.86 922
|
|
|
ORG 0.86 0.85 0.86 1341
|
|
|
PER 0.95 0.96 0.95 1842
|
|
|
|
|
|
micro avg 0.91 0.92 0.92 5942
|
|
|
macro avg 0.91 0.91 0.91 5942
|
|
|
weighted avg 0.91 0.92 0.92 5942
|
|
|
|
|
|
|
| 0.0166 | 3.2017 | 6000 | 0.0861 | 0.9834 | 0.9187 | 0.9191 | 0.9189 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.94 0.96 0.95 1837
|
|
|
MISC 0.90 0.84 0.87 922
|
|
|
ORG 0.86 0.87 0.87 1341
|
|
|
PER 0.94 0.96 0.95 1842
|
|
|
|
|
|
micro avg 0.92 0.92 0.92 5942
|
|
|
macro avg 0.91 0.91 0.91 5942
|
|
|
weighted avg 0.92 0.92 0.92 5942
|
|
|
|
|
|
|
| 0.0094 | 3.4685 | 6500 | 0.0905 | 0.9840 | 0.9202 | 0.9236 | 0.9219 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.95 0.96 0.95 1837
|
|
|
MISC 0.89 0.86 0.88 922
|
|
|
ORG 0.85 0.88 0.86 1341
|
|
|
PER 0.96 0.95 0.96 1842
|
|
|
|
|
|
micro avg 0.92 0.92 0.92 5942
|
|
|
macro avg 0.91 0.91 0.91 5942
|
|
|
weighted avg 0.92 0.92 0.92 5942
|
|
|
|
|
|
|
| 0.0123 | 3.7353 | 7000 | 0.0927 | 0.9837 | 0.9239 | 0.9219 | 0.9229 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.95 0.95 0.95 1837
|
|
|
MISC 0.86 0.85 0.86 922
|
|
|
ORG 0.90 0.87 0.88 1341
|
|
|
PER 0.95 0.97 0.96 1842
|
|
|
|
|
|
micro avg 0.92 0.92 0.92 5942
|
|
|
macro avg 0.91 0.91 0.91 5942
|
|
|
weighted avg 0.92 0.92 0.92 5942
|
|
|
|
|
|
|
| 0.0097 | 4.0021 | 7500 | 0.0947 | 0.9839 | 0.9279 | 0.9221 | 0.9250 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.95 0.96 0.95 1837
|
|
|
MISC 0.88 0.85 0.87 922
|
|
|
ORG 0.90 0.86 0.88 1341
|
|
|
PER 0.95 0.96 0.96 1842
|
|
|
|
|
|
micro avg 0.93 0.92 0.92 5942
|
|
|
macro avg 0.92 0.91 0.91 5942
|
|
|
weighted avg 0.93 0.92 0.92 5942
|
|
|
|
|
|
|
| 0.0049 | 4.2689 | 8000 | 0.0903 | 0.9840 | 0.9248 | 0.9251 | 0.9250 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.94 0.96 0.95 1837
|
|
|
MISC 0.90 0.85 0.87 922
|
|
|
ORG 0.87 0.88 0.88 1341
|
|
|
PER 0.95 0.96 0.96 1842
|
|
|
|
|
|
micro avg 0.92 0.93 0.92 5942
|
|
|
macro avg 0.92 0.91 0.91 5942
|
|
|
weighted avg 0.92 0.93 0.92 5942
|
|
|
|
|
|
|
| 0.0037 | 4.5358 | 8500 | 0.0903 | 0.9843 | 0.9235 | 0.9283 | 0.9259 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.94 0.96 0.95 1837
|
|
|
MISC 0.89 0.86 0.88 922
|
|
|
ORG 0.88 0.88 0.88 1341
|
|
|
PER 0.95 0.96 0.96 1842
|
|
|
|
|
|
micro avg 0.92 0.93 0.93 5942
|
|
|
macro avg 0.92 0.92 0.92 5942
|
|
|
weighted avg 0.92 0.93 0.93 5942
|
|
|
|
|
|
|
| 0.0038 | 4.8026 | 9000 | 0.0922 | 0.9840 | 0.9221 | 0.9260 | 0.9240 | precision recall f1-score support
|
|
|
|
|
|
LOC 0.94 0.96 0.95 1837
|
|
|
MISC 0.88 0.87 0.87 922
|
|
|
ORG 0.88 0.87 0.88 1341
|
|
|
PER 0.96 0.96 0.96 1842
|
|
|
|
|
|
micro avg 0.92 0.93 0.92 5942
|
|
|
macro avg 0.91 0.91 0.91 5942
|
|
|
weighted avg 0.92 0.93 0.92 5942
|
|
|
|
|
|
|
|
|
|
|
|
|
### Framework versions
|
|
|
|
|
|
- Transformers 4.47.1
|
|
|
- Pytorch 2.3.1+cpu
|
|
|
- Datasets 3.2.0
|
|
|
- Tokenizers 0.21.0
|
|
|
|
