distilbert-legal-definitions
This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0034
- Precision: 0.9668
- Recall: 0.9707
- Macro F1: 0.9688
- Micro F1: 0.9688
- Accuracy: 0.9994
- Term F1: 0.9688
- Term Precision: 0.9668
- Term Recall: 0.9707
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Macro F1 | Micro F1 | Accuracy | Term F1 | Term Precision | Term Recall |
---|---|---|---|---|---|---|---|---|---|---|---|
0.0049 | 1.0 | 2325 | 0.0034 | 0.9790 | 0.9580 | 0.9684 | 0.9684 | 0.9993 | 0.9684 | 0.9790 | 0.9580 |
0.0023 | 2.0 | 4650 | 0.0032 | 0.9669 | 0.9786 | 0.9727 | 0.9727 | 0.9994 | 0.9727 | 0.9669 | 0.9786 |
0.0013 | 3.0 | 6975 | 0.0018 | 0.9836 | 0.9794 | 0.9815 | 0.9815 | 0.9997 | 0.9815 | 0.9836 | 0.9794 |
0.0006 | 4.0 | 9300 | 0.0016 | 0.9879 | 0.9828 | 0.9854 | 0.9854 | 0.9997 | 0.9854 | 0.9879 | 0.9828 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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