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spam_message_classification
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: 0.0878
- Accuracy: 0.9884
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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 | Accuracy |
---|---|---|---|---|
No log | 1.0 | 297 | 0.0559 | 0.9816 |
0.0966 | 2.0 | 594 | 0.0833 | 0.9745 |
0.0966 | 3.0 | 891 | 0.0723 | 0.9819 |
0.0161 | 4.0 | 1188 | 0.0813 | 0.9859 |
0.0161 | 5.0 | 1485 | 0.0799 | 0.9852 |
0.0029 | 6.0 | 1782 | 0.0783 | 0.9873 |
0.0013 | 7.0 | 2079 | 0.0866 | 0.9882 |
0.0013 | 8.0 | 2376 | 0.0862 | 0.9884 |
0.0002 | 9.0 | 2673 | 0.0933 | 0.9873 |
0.0002 | 10.0 | 2970 | 0.0878 | 0.9884 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3