my_awesome_wnut_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2781
- Precision: 0.5892
- Recall: 0.3031
- F1: 0.4002
- Accuracy: 0.9408
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2862 | 0.5458 | 0.2373 | 0.3307 | 0.9378 |
| No log | 2.0 | 426 | 0.2781 | 0.5892 | 0.3031 | 0.4002 | 0.9408 |
Framework versions
- Transformers 5.20.1
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.3