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# ribesstefano/RuleBert-v0.1-k4

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: 0.3547
- F1: 0.4940
- Roc Auc: 0.6712
- Accuracy: 0.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3524        | 0.14  | 250  | 0.3526          | 0.4888 | 0.6731  | 0.0      |
| 0.3318        | 0.27  | 500  | 0.3489          | 0.4885 | 0.6698  | 0.0      |
| 0.3278        | 0.41  | 750  | 0.3517          | 0.4870 | 0.6709  | 0.0      |
| 0.3165        | 0.54  | 1000 | 0.3506          | 0.4953 | 0.6726  | 0.0      |
| 0.3243        | 0.68  | 1250 | 0.3501          | 0.4904 | 0.6693  | 0.0      |
| 0.3072        | 0.82  | 1500 | 0.3529          | 0.4979 | 0.6715  | 0.0      |
| 0.311         | 0.95  | 1750 | 0.3527          | 0.4855 | 0.6664  | 0.0      |
| 0.3277        | 1.09  | 2000 | 0.3542          | 0.4900 | 0.6693  | 0.0      |
| 0.3102        | 1.22  | 2250 | 0.3535          | 0.4881 | 0.6679  | 0.0      |
| 0.3159        | 1.36  | 2500 | 0.3533          | 0.4839 | 0.6663  | 0.0      |
| 0.3073        | 1.49  | 2750 | 0.3531          | 0.4994 | 0.6726  | 0.0      |
| 0.3108        | 1.63  | 3000 | 0.3542          | 0.4929 | 0.6701  | 0.0      |
| 0.3093        | 1.77  | 3250 | 0.3546          | 0.4925 | 0.6702  | 0.0      |
| 0.2981        | 1.9   | 3500 | 0.3547          | 0.4933 | 0.6703  | 0.0      |
| 0.3046        | 2.04  | 3750 | 0.3547          | 0.4929 | 0.6707  | 0.0      |
| 0.3085        | 2.17  | 4000 | 0.3547          | 0.4940 | 0.6712  | 0.0      |


### 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-k4", "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