my_awesome_wnut_model_lrx2
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: 0.3524
- Precision: 0.6255
- Recall: 0.3049
- F1: 0.4100
- Accuracy: 0.9449
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: 4e-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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2599 | 0.6709 | 0.3401 | 0.4514 | 0.9431 |
| No log | 2.0 | 426 | 0.2835 | 0.6239 | 0.3197 | 0.4228 | 0.9452 |
| 0.1381 | 3.0 | 639 | 0.3524 | 0.6255 | 0.3049 | 0.4100 | 0.9449 |
Framework versions
- Transformers 5.15.0
- Pytorch 2.11.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2