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

mhmmterts/fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens

fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mhmmterts/
fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens