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fine_tuned_spam_model
rationalenterprise/fine_tuned_spam_model
fine_tuned_spam_model is a text classification model from rationalenterprise. Use it when you need a label for a piece of text. It is set up for transformers.
This model is a fine-tuned version of huawei-noah/TinyBERTGeneral4L312D on a dataset of batch-labeled emails and SMS messages that were identified to be spam (Enron, spamassassin, sms-spam, etc.). It achieves the foll…
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 13, 2025
- Updated
- Mar 19, 2025
- SHA
- 5e0fa8d3
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Model card
From the Hugging Face model README
fine_tuned_spam_model
This model is a fine-tuned version of huawei-noah/TinyBERT_General_4L_312D on a dataset of batch-labeled emails and SMS messages that were identified to be spam (Enron, spamassassin, sms-spam, etc.). It achieves the following results on the evaluation set:
- Loss: 0.6292
- Accuracy: 0.7664
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6875 | 1.0 | 364 | 0.7508 | 0.7226 |
| 0.5574 | 2.0 | 728 | 0.6804 | 0.7292 |
| 0.5481 | 3.0 | 1092 | 0.6292 | 0.7664 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cpu
- Datasets 3.3.2
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Mar 13, 2025
- Updated
- Mar 19, 2025