fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens
This model is a fine-tuned version of joelniklaus/legal-swiss-roberta-large on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set:
- Loss: 0.6456
- Accuracy: 0.8031
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6562 | 1.0 | 8865 | 0.6456 | 0.8031 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
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Model tree for mhmmterts/fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens
Base model
joelniklaus/legal-swiss-roberta-largeDataset used to train mhmmterts/fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens
Evaluation results
- Accuracy on swiss_judgment_predictiontest set self-reported0.803