| --- |
| 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-v0.5-k0 |
| 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-v0.5-k0 |
|
|
| 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: 0.3610 |
| - F1: 0.4972 |
| - Roc Auc: 0.6720 |
| - Accuracy: 0.0 |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
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|
| More information needed |
|
|
| ## Training and evaluation data |
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|
| More information needed |
|
|
| ## Training procedure |
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|
| ### Training hyperparameters |
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|
| 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
| | 0.4117 | 0.06 | 250 | 0.3990 | 0.4972 | 0.6720 | 0.0 | |
| | 0.3571 | 0.12 | 500 | 0.3592 | 0.4972 | 0.6720 | 0.0 | |
| | 0.3339 | 0.19 | 750 | 0.3529 | 0.4972 | 0.6720 | 0.0 | |
| | 0.3293 | 0.25 | 1000 | 0.3531 | 0.4972 | 0.6720 | 0.0 | |
| | 0.3396 | 0.31 | 1250 | 0.3591 | 0.4972 | 0.6720 | 0.0 | |
| | 0.3549 | 0.37 | 1500 | 0.3610 | 0.4972 | 0.6720 | 0.0 | |
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| ### Framework versions |
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|
| - Transformers 4.36.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
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|
