flood_detection
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1504
- Accuracy: 0.9625
- F1: 0.9388
- Precision: 0.9718
- Recall: 0.9079
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2372 | 1.0 | 1078 | 0.3109 | 0.9372 | 0.8993 | 0.8933 | 0.9054 |
| 0.1676 | 2.0 | 2156 | 0.2451 | 0.9456 | 0.9116 | 0.9178 | 0.9054 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2