
<!-- 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. -->

# ribesstefano/RuleBert-v0.1-k1

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: 0.3207
- F1: 0.4762
- Roc Auc: 0.6657
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3316        | 0.14  | 250  | 0.3375          | 0.4771 | 0.6730  | 0.0      |
| 0.3343        | 0.28  | 500  | 0.3277          | 0.4724 | 0.6700  | 0.0      |
| 0.3328        | 0.41  | 750  | 0.3235          | 0.4624 | 0.6623  | 0.0      |
| 0.3365        | 0.55  | 1000 | 0.3227          | 0.4663 | 0.6635  | 0.0      |
| 0.3257        | 0.69  | 1250 | 0.3236          | 0.4669 | 0.6633  | 0.0      |
| 0.3194        | 0.83  | 1500 | 0.3243          | 0.4912 | 0.6768  | 0.0      |
| 0.3232        | 0.97  | 1750 | 0.3223          | 0.4714 | 0.6645  | 0.0      |
| 0.3151        | 1.11  | 2000 | 0.3216          | 0.4727 | 0.6650  | 0.0      |
| 0.3229        | 1.24  | 2250 | 0.3217          | 0.4756 | 0.6665  | 0.0      |
| 0.323         | 1.38  | 2500 | 0.3237          | 0.4736 | 0.6651  | 0.0      |
| 0.3175        | 1.52  | 2750 | 0.3222          | 0.4731 | 0.6647  | 0.0      |
| 0.3133        | 1.66  | 3000 | 0.3203          | 0.4739 | 0.6651  | 0.0      |
| 0.3089        | 1.8   | 3250 | 0.3205          | 0.4751 | 0.6654  | 0.0      |
| 0.3285        | 1.94  | 3500 | 0.3208          | 0.4759 | 0.6657  | 0.0      |
| 0.3119        | 2.07  | 3750 | 0.3207          | 0.4768 | 0.6660  | 0.0      |
| 0.3169        | 2.21  | 4000 | 0.3207          | 0.4762 | 0.6657  | 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-k1", "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