food-not-food-classifier
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: 0.0006
- Accuracy: {'accuracy': 1.0}
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4535 | 1.0 | 7 | 0.0729 | {'accuracy': 1.0} |
| 0.0352 | 2.0 | 14 | 0.0079 | {'accuracy': 1.0} |
| 0.0053 | 3.0 | 21 | 0.0025 | {'accuracy': 1.0} |
| 0.0021 | 4.0 | 28 | 0.0013 | {'accuracy': 1.0} |
| 0.0013 | 5.0 | 35 | 0.0009 | {'accuracy': 1.0} |
| 0.0010 | 6.0 | 42 | 0.0007 | {'accuracy': 1.0} |
| 0.0008 | 7.0 | 49 | 0.0006 | {'accuracy': 1.0} |
| 0.0007 | 8.0 | 56 | 0.0006 | {'accuracy': 1.0} |
| 0.0007 | 9.0 | 63 | 0.0006 | {'accuracy': 1.0} |
| 0.0007 | 10.0 | 70 | 0.0006 | {'accuracy': 1.0} |
Framework versions
- Transformers 5.8.1
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2