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legal_bert_sm_cv_defined_4
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: 1.5637
- Accuracy: 0.817
- Precision: 0.5545
- Recall: 0.3128
- F1: 0.4
- D-index: 1.5629
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
- lr_scheduler_warmup_steps: 8000
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 250 | 0.4802 | 0.805 | 0.0 | 0.0 | 0.0 | 1.4370 |
| 0.5519 | 2.0 | 500 | 0.4506 | 0.809 | 0.8333 | 0.0256 | 0.0498 | 1.4518 |
| 0.5519 | 3.0 | 750 | 0.4347 | 0.828 | 0.7018 | 0.2051 | 0.3175 | 1.5411 |
| 0.4107 | 4.0 | 1000 | 0.4288 | 0.838 | 0.7037 | 0.2923 | 0.4130 | 1.5841 |
| 0.4107 | 5.0 | 1250 | 0.4980 | 0.834 | 0.7736 | 0.2103 | 0.3306 | 1.5510 |
| 0.3108 | 6.0 | 1500 | 0.4671 | 0.837 | 0.7353 | 0.2564 | 0.3802 | 1.5707 |
| 0.3108 | 7.0 | 1750 | 0.4817 | 0.835 | 0.6829 | 0.2872 | 0.4043 | 1.5784 |
| 0.2129 | 8.0 | 2000 | 0.6713 | 0.826 | 0.6981 | 0.1897 | 0.2984 | 1.5331 |
| 0.2129 | 9.0 | 2250 | 0.7290 | 0.825 | 0.6190 | 0.2667 | 0.3728 | 1.5581 |
| 0.1289 | 10.0 | 2500 | 0.8754 | 0.812 | 0.5402 | 0.2410 | 0.3333 | 1.5317 |
| 0.1289 | 11.0 | 2750 | 1.1026 | 0.822 | 0.6232 | 0.2205 | 0.3258 | 1.5383 |
| 0.0804 | 12.0 | 3000 | 1.2274 | 0.807 | 0.5109 | 0.2410 | 0.3275 | 1.5249 |
| 0.0804 | 13.0 | 3250 | 1.2411 | 0.824 | 0.5772 | 0.3641 | 0.4465 | 1.5894 |
| 0.0529 | 14.0 | 3500 | 1.2761 | 0.812 | 0.5263 | 0.3590 | 0.4268 | 1.5716 |
| 0.0529 | 15.0 | 3750 | 1.3524 | 0.823 | 0.5714 | 0.3692 | 0.4486 | 1.5897 |
| 0.0299 | 16.0 | 4000 | 1.6109 | 0.829 | 0.7308 | 0.1949 | 0.3077 | 1.5389 |
| 0.0299 | 17.0 | 4250 | 1.6461 | 0.824 | 0.6863 | 0.1795 | 0.2846 | 1.5269 |
| 0.0284 | 18.0 | 4500 | 1.7304 | 0.824 | 0.7879 | 0.1333 | 0.2281 | 1.5108 |
| 0.0284 | 19.0 | 4750 | 1.5481 | 0.809 | 0.5159 | 0.3333 | 0.4050 | 1.5590 |
| 0.0396 | 20.0 | 5000 | 1.5637 | 0.817 | 0.5545 | 0.3128 | 0.4 | 1.5629 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
