DunnBC22/distilbert-base-uncased-SpamFilter-sm

distilbert-base-uncased-SpamFilter-DunnBC22

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: 0.1007
• Accuracy: 0.9907
• F1: 0.9906

Model description

This is a binary classification of whether the inputs are spam or not.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Spam%20Filter%20-%20Smaller%20Dataset/DunnBC22-distilbert-base-uncased-SpamFilter-sm.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

The main limitation is the quality of the data source.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/datatattle/email-classification-nlp

Input Word Length By Class:

Confusion Matrix:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5039	1.0	7	0.3920	0.8333	0.7576
0.3008	2.0	14	0.2010	0.9722	0.9719
0.113	3.0	21	0.1007	0.9907	0.9906

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1
• Datasets 2.4.0
• Tokenizers 0.12.1

License Notice

This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.

Dataset Notice

This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.