--- 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-k2 results: [] --- # ribesstefano/RuleBert-v0.3-k2 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: 0.3031 - F1: 0.5103 - Roc Auc: 0.6747 - 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: 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.452 | 0.06 | 250 | 0.3816 | 0.5227 | 0.6861 | 0.0 | | 0.3694 | 0.12 | 500 | 0.3191 | 0.5103 | 0.6747 | 0.0 | | 0.3523 | 0.18 | 750 | 0.3031 | 0.5103 | 0.6747 | 0.0 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0