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# ditilbert-spamEmail

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an [enron_spam dataset](https://huggingface.co/datasets/SetFit/enron_spam).
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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "ditilbert-spamEmail", "results": []}]}
transformers pytorch distilbert text-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us