| --- |
| 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-v0.4-k1 |
| results: [] |
| --- |
| |
| <!-- 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-v0.4-k1 |
|
|
| 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: 0.3335 |
| - F1: 0.5287 |
| - Roc Auc: 0.7065 |
| - 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 |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0005 |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
| | 0.3599 | 0.13 | 250 | 0.3361 | 0.5157 | 0.6901 | 0.0 | |
| | 0.3426 | 0.25 | 500 | 0.3436 | 0.5031 | 0.6842 | 0.0667 | |
| | 0.3621 | 0.38 | 750 | 0.3340 | 0.4861 | 0.6679 | 0.0 | |
| | 0.3692 | 0.5 | 1000 | 0.3397 | 0.5409 | 0.7020 | 0.0 | |
| | 0.3485 | 0.63 | 1250 | 0.3318 | 0.4861 | 0.6679 | 0.0 | |
| | 0.3494 | 0.75 | 1500 | 0.3306 | 0.4861 | 0.6679 | 0.0 | |
| | 0.3464 | 0.88 | 1750 | 0.3353 | 0.4861 | 0.6679 | 0.0 | |
| | 0.3554 | 1.0 | 2000 | 0.3395 | 0.5632 | 0.7243 | 0.0 | |
| | 0.3509 | 1.13 | 2250 | 0.3303 | 0.4861 | 0.6679 | 0.0 | |
| | 0.3331 | 1.26 | 2500 | 0.3359 | 0.5302 | 0.6945 | 0.0 | |
| | 0.3373 | 1.38 | 2750 | 0.3334 | 0.4861 | 0.6679 | 0.0 | |
| | 0.3416 | 1.51 | 3000 | 0.3355 | 0.4861 | 0.6679 | 0.0 | |
| | 0.3492 | 1.63 | 3250 | 0.3335 | 0.5287 | 0.7065 | 0.0 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.36.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
|
|
