---
license: mit
base_model: papluca/xlm-roberta-base-language-detection
tags:
- Italian
- legal ruling
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: ribesstefano/RuleBert-v0.3-k3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ribesstefano/RuleBert-v0.3-k3

This model is a fine-tuned version of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3332
- F1: 0.4507
- Roc Auc: 0.6503
- 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4445        | 0.06  | 250  | 0.4053          | 0.4824 | 0.6769  | 0.0      |
| 0.3665        | 0.12  | 500  | 0.3428          | 0.4528 | 0.6516  | 0.0714   |
| 0.3587        | 0.18  | 750  | 0.3332          | 0.4507 | 0.6503  | 0.0714   |


### Framework versions

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