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---
license: cc
base_model: joelniklaus/legal-swiss-roberta-large
tags:
- generated_from_trainer
datasets:
- swiss_judgment_prediction
metrics:
- accuracy
model-index:
- name: my_fine_tuned_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: swiss_judgment_prediction
type: swiss_judgment_prediction
config: de
split: test
args: de
metrics:
- name: Accuracy
type: accuracy
value: 0.831362467866324
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_fine_tuned_model
This model is a fine-tuned version of [joelniklaus/legal-swiss-roberta-large](https://huggingface.co/joelniklaus/legal-swiss-roberta-large) on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4358
- Accuracy: 0.8314
## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4692 | 1.0 | 2217 | 0.4277 | 0.8305 |
| 0.4261 | 2.0 | 4434 | 0.4358 | 0.8314 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|