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distilbert-base-uncased-finetuned-ingredients
This model is a fine-tuned version of distilbert-base-uncased on the ingredients_yes_no dataset. It achieves the following results on the evaluation set:
- Loss: 0.0105
- Precision: 0.9899
- Recall: 0.9932
- F1: 0.9915
- Accuracy: 0.9978
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 47 | 0.2783 | 0.4 | 0.5492 | 0.4629 | 0.8910 |
| No log | 2.0 | 94 | 0.1089 | 0.8145 | 0.8780 | 0.8450 | 0.9718 |
| No log | 3.0 | 141 | 0.0273 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
| No log | 4.0 | 188 | 0.0168 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
| No log | 5.0 | 235 | 0.0156 | 0.9865 | 0.9898 | 0.9882 | 0.9957 |
| No log | 6.0 | 282 | 0.0129 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
| No log | 7.0 | 329 | 0.0121 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
| No log | 8.0 | 376 | 0.0115 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
| No log | 9.0 | 423 | 0.0108 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
| No log | 10.0 | 470 | 0.0105 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
