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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
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 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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