fine_tuned_model_balanced_512_tokens_it
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: 0.7007
- Accuracy: 0.7771
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: 32
- eval_batch_size: 32
- 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 96 | 0.6889 | 0.6736 |
| No log | 2.0 | 192 | 0.7007 | 0.7771 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
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Finetuned from
Dataset used to train mhmmterts/fine_tuned_model_balanced_512_tokens_it
Evaluation results
- Accuracy on swiss_judgment_predictiontest set self-reported0.777