ditilbert-spamEmail
This model is a fine-tuned version of distilbert-base-uncased on an enron_spam dataset. It achieves the following results on the evaluation set:
- Loss: 0.0462
- Accuracy: 0.9925
Model description
By calling the API, label 0 means ham message while 1 means spam message.
Intended uses & limitations
This model is used for spam email detection powered by distilbert and sequence classification.
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0307 | 1.0 | 1983 | 0.0561 | 0.989 |
| 0.007 | 2.0 | 3966 | 0.0462 | 0.9925 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for nathan-marquez/distilbert-spamEmail-base
Base model
distilbert/distilbert-base-uncased