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
- Downloads last month
- 7
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for mhmmterts/fine_tuned_model_balanced_512_tokens_it
Base model
joelniklaus/legal-swiss-roberta-largeDataset used to train mhmmterts/fine_tuned_model_balanced_512_tokens_it
Evaluation results
- Accuracy on swiss_judgment_predictiontest set self-reported0.777