Training
This model is a fine-tuned version of DistilRoBERTa-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1793
- Precision: 0.9780
- Recall: 0.9221
- F1: 0.9492
- Roc Auc: 0.9811
- Krippendorff Alpha: 0.8581
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: 6.7e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Roc Auc | Krippendorff Alpha |
|---|---|---|---|---|---|---|---|---|
| 0.4512 | 1.0 | 281 | 0.4029 | 0.8631 | 0.9135 | 0.8876 | 0.9159 | 0.6416 |
| 0.3139 | 2.0 | 562 | 0.2793 | 0.9558 | 0.8676 | 0.9096 | 0.9517 | 0.7514 |
| 0.2286 | 3.0 | 843 | 0.2247 | 0.9522 | 0.9140 | 0.9327 | 0.9661 | 0.8057 |
| 0.1852 | 4.0 | 1124 | 0.2174 | 0.9605 | 0.9113 | 0.9353 | 0.9687 | 0.8151 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Downloads last month
- -