---
license: cc
base_model: joelniklaus/legal-swiss-roberta-large
tags:
- generated_from_trainer
datasets:
- swiss_judgment_prediction
metrics:
- accuracy
model-index:
- name: my_model_all
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: swiss_judgment_prediction
      type: swiss_judgment_prediction
      config: all
      split: test
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.808088955464654
---

<!-- 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_model_all

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: 0.4914
- Accuracy: 0.8081

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5561        | 1.0   | 1866 | 0.5090          | 0.8081   |
| 0.5522        | 2.0   | 3732 | 0.4914          | 0.8081   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1