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# ribesstefano/RuleBert-v0.1-k0

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: 0.3765
- F1: 0.5087
- Roc Auc: 0.6757
- Accuracy: 0.0333

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3621        | 0.14  | 250  | 0.3791          | 0.4861 | 0.6678  | 0.0458   |
| 0.3199        | 0.28  | 500  | 0.3754          | 0.4969 | 0.6730  | 0.0375   |
| 0.3135        | 0.41  | 750  | 0.3785          | 0.4965 | 0.6715  | 0.0458   |
| 0.3176        | 0.55  | 1000 | 0.3755          | 0.4988 | 0.6725  | 0.0333   |
| 0.3056        | 0.69  | 1250 | 0.3734          | 0.5100 | 0.6756  | 0.0667   |
| 0.3016        | 0.83  | 1500 | 0.3749          | 0.5076 | 0.6755  | 0.0333   |
| 0.3044        | 0.97  | 1750 | 0.3763          | 0.5014 | 0.6738  | 0.0125   |
| 0.3009        | 1.11  | 2000 | 0.3781          | 0.5073 | 0.6751  | 0.0333   |
| 0.3044        | 1.24  | 2250 | 0.3782          | 0.5089 | 0.6755  | 0.0417   |
| 0.2979        | 1.38  | 2500 | 0.3770          | 0.5047 | 0.6745  | 0.0167   |
| 0.2977        | 1.52  | 2750 | 0.3772          | 0.5114 | 0.6764  | 0.0417   |
| 0.2998        | 1.66  | 3000 | 0.3747          | 0.5107 | 0.6762  | 0.0375   |
| 0.2916        | 1.8   | 3250 | 0.3741          | 0.5096 | 0.6758  | 0.0375   |
| 0.3107        | 1.94  | 3500 | 0.3757          | 0.5056 | 0.6748  | 0.0208   |
| 0.3023        | 2.07  | 3750 | 0.3767          | 0.5069 | 0.6752  | 0.025    |
| 0.3069        | 2.21  | 4000 | 0.3765          | 0.5087 | 0.6757  | 0.0333   |


### 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-k0", "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