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# ribesstefano/RuleBert-v0.1-k3

This model is a fine-tuned version of [classla/xlm-roberta-base-multilingual-text-genre-classifier](https://huggingface.co/classla/xlm-roberta-base-multilingual-text-genre-classifier) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3285
- F1: 0.4638
- Roc Auc: 0.6576
- Accuracy: 0.0714

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3423        | 0.13  | 250  | 0.3539          | 0.4497 | 0.6562  | 0.0670   |
| 0.3231        | 0.27  | 500  | 0.3425          | 0.4596 | 0.6594  | 0.0670   |
| 0.3248        | 0.4   | 750  | 0.3364          | 0.4495 | 0.6541  | 0.0714   |
| 0.3283        | 0.54  | 1000 | 0.3351          | 0.4529 | 0.6555  | 0.0714   |
| 0.3237        | 0.67  | 1250 | 0.3315          | 0.4600 | 0.6581  | 0.0625   |
| 0.325         | 0.81  | 1500 | 0.3313          | 0.4681 | 0.6624  | 0.0312   |
| 0.3316        | 0.94  | 1750 | 0.3290          | 0.4595 | 0.6564  | 0.0714   |
| 0.3239        | 1.08  | 2000 | 0.3310          | 0.4592 | 0.6572  | 0.0625   |
| 0.3085        | 1.21  | 2250 | 0.3280          | 0.4614 | 0.6567  | 0.0670   |
| 0.3161        | 1.35  | 2500 | 0.3303          | 0.4623 | 0.6574  | 0.0670   |
| 0.314         | 1.48  | 2750 | 0.3289          | 0.4613 | 0.6566  | 0.0714   |
| 0.3187        | 1.62  | 3000 | 0.3293          | 0.4594 | 0.6554  | 0.0714   |
| 0.3145        | 1.75  | 3250 | 0.3295          | 0.4629 | 0.6569  | 0.0714   |
| 0.3128        | 1.89  | 3500 | 0.3285          | 0.4629 | 0.6569  | 0.0714   |
| 0.3135        | 2.02  | 3750 | 0.3285          | 0.4615 | 0.6566  | 0.0714   |
| 0.3171        | 2.16  | 4000 | 0.3285          | 0.4638 | 0.6576  | 0.0714   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"license": "mit", "tags": ["Italian", "legal ruling", "generated_from_trainer"], "metrics": ["f1", "accuracy"], "base_model": "classla/xlm-roberta-base-multilingual-text-genre-classifier", "model-index": [{"name": "ribesstefano/RuleBert-v0.1-k3", "results": []}]}
transformers tensorboard safetensors xlm-roberta text-classification Italian legal ruling generated_from_trainer base_model:classla/xlm-roberta-base-multilingual-text-genre-classifier license:mit autotrain_compatible endpoints_compatible region:us