modello_finetuning1
This model is a fine-tuned version of distilbert-base-multilingual-cased on the swiss_law_area_prediction dataset. It achieves the following results on the evaluation set:
- Loss: 0.0506
- Precision: 0.9922
- Recall: 0.9902
- F1: 0.9911
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: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.0834 | 0.38 | 500 | 0.1812 | 0.9793 | 0.9677 | 0.9730 |
| 0.1029 | 0.76 | 1000 | 0.0973 | 0.9875 | 0.9834 | 0.9854 |
| 0.0066 | 1.15 | 1500 | 0.0647 | 0.9864 | 0.9886 | 0.9875 |
| 0.0008 | 1.53 | 2000 | 0.0619 | 0.9913 | 0.9893 | 0.9902 |
| 0.0003 | 1.91 | 2500 | 0.0506 | 0.9922 | 0.9902 | 0.9911 |
Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Evaluation results
- Precision on swiss_law_area_predictionvalidation set self-reported0.992
- Recall on swiss_law_area_predictionvalidation set self-reported0.990
- F1 on swiss_law_area_predictionvalidation set self-reported0.991