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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: fine_tuned_model_on_SJP_dataset_all_balanced_512_tokens
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: 0.7527222446275278
---
<!-- 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. -->
# fine_tuned_model_on_SJP_dataset_all_balanced_512_tokens
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: 0.6312
- Accuracy: 0.7527
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5895 | 1.0 | 1866 | 0.6069 | 0.7289 |
| 0.5262 | 2.0 | 3732 | 0.6312 | 0.7527 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
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