BERT-Tiny fine-tuned on Enron Spam Detection
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 (aka BERT-Tiny) on an SetFit/enron_spam for Spam Dectection downstream task.
It achieves the following results on the evaluation set:
- Loss: 0.0593
- Precision: 0.9851
- Recall: 0.9871
- Accuracy: 0.986
- F1: 0.9861
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- 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 | Precision | Recall | Accuracy | F1 |
|---|---|---|---|---|---|---|---|
| 0.1125 | 1.0 | 1983 | 0.0797 | 0.9839 | 0.9692 | 0.9765 | 0.9765 |
| 0.061 | 2.0 | 3966 | 0.0618 | 0.9822 | 0.9861 | 0.984 | 0.9842 |
| 0.0486 | 3.0 | 5949 | 0.0593 | 0.9851 | 0.9871 | 0.986 | 0.9861 |
| 0.048 | 4.0 | 7932 | 0.0588 | 0.9870 | 0.9821 | 0.9845 | 0.9846 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
- Downloads last month
- 12,858
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.