bert-base-uncased-finetuned-smsspam
This model is a fine-tuned version of bert-base-uncased on the sms_spam dataset. It achieves the following results on the evaluation set:
- Loss: 0.0637
- Accuracy: 0.9904
- Precision: 0.9815
- Recall: 0.9464
- F1: 0.9636
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0828 | 1.0 | 593 | 0.0538 | 0.9892 | 0.9725 | 0.9464 | 0.9593 |
| 0.0269 | 2.0 | 1186 | 0.1792 | 0.9677 | 0.8244 | 0.9643 | 0.8889 |
| 0.0229 | 3.0 | 1779 | 0.0623 | 0.9916 | 0.9817 | 0.9554 | 0.9683 |
| 0.0043 | 4.0 | 2372 | 0.0637 | 0.9904 | 0.9815 | 0.9464 | 0.9636 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for shre-db/bert-base-uncased-finetuned-smsspam
Base model
google-bert/bert-base-uncasedDataset used to train shre-db/bert-base-uncased-finetuned-smsspam
Evaluation results
- Accuracy on sms_spamself-reported0.990
- Precision on sms_spamself-reported0.981
- Recall on sms_spamself-reported0.946
- F1 on sms_spamself-reported0.964