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distilbert-base-uncased-SpamFilter-LG
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: 0.0500
- Accuracy: 0.9845
- F1: 0.9848
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-%20Larger%20Dataset/DunnBC22-distilbert-base-uncased-SpamFilter-LG.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complx problem using technology.
The main limitation is the quality of the data source.
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.0568 | 1.0 | 65 | 0.0568 | 0.9787 | 0.9791 |
| 0.03 | 2.0 | 130 | 0.0533 | 0.9806 | 0.9811 |
| 0.0241 | 3.0 | 195 | 0.0500 | 0.9845 | 0.9848 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
