ribesstefano/RuleBert-v0.2-k0
This model is a fine-tuned version of papluca/xlm-roberta-base-language-detection on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3669
- F1: 0.4972
- Roc Auc: 0.6720
- 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: 1e-05
- 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 |
---|---|---|---|---|---|---|
0.3463 | 0.06 | 250 | 0.3661 | 0.4972 | 0.6720 | 0.0 |
0.3384 | 0.12 | 500 | 0.3619 | 0.4972 | 0.6720 | 0.0 |
0.3266 | 0.19 | 750 | 0.3583 | 0.5162 | 0.6763 | 0.0533 |
0.3276 | 0.25 | 1000 | 0.3594 | 0.5152 | 0.6751 | 0.06 |
0.3412 | 0.31 | 1250 | 0.3669 | 0.4972 | 0.6720 | 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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