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distilbert-legal-chunk
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: 0.0699
- Precision: 0.8994
- Recall: 0.8721
- Macro F1: 0.8855
- Micro F1: 0.8855
- Accuracy: 0.9789
- Marker F1: 0.9804
- Marker Precision: 0.9687
- Marker Recall: 0.9925
- Reference F1: 0.9791
- Reference Precision: 0.9804
- Reference Recall: 0.9778
- Term F1: 0.8670
- Term Precision: 0.8844
- Term Recall: 0.8502
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 | Marker F1 | Marker Precision | Marker Recall | Reference F1 | Reference Precision | Reference Recall | Term F1 | Term Precision | Term Recall |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0857 | 1.0 | 3125 | 0.0966 | 0.8374 | 0.7889 | 0.8124 | 0.8124 | 0.9676 | 0.6143 | 0.5874 | 0.6437 | 0.9628 | 0.9423 | 0.9842 | 0.8291 | 0.8656 | 0.7955 |
| 0.058 | 2.0 | 6250 | 0.0606 | 0.8869 | 0.9146 | 0.9006 | 0.9006 | 0.9814 | 0.9405 | 0.9126 | 0.9702 | 0.9689 | 0.9511 | 0.9873 | 0.8923 | 0.8805 | 0.9045 |
| 0.0415 | 3.0 | 9375 | 0.0642 | 0.9077 | 0.9131 | 0.9104 | 0.9104 | 0.9823 | 0.9524 | 0.9262 | 0.9801 | 0.9742 | 0.9614 | 0.9873 | 0.9021 | 0.9026 | 0.9016 |
| 0.0283 | 4.0 | 12500 | 0.0646 | 0.9066 | 0.9089 | 0.9077 | 0.9077 | 0.9819 | 0.9564 | 0.9326 | 0.9815 | 0.9712 | 0.9555 | 0.9873 | 0.8986 | 0.9008 | 0.8965 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
