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bert-base-uncased-finetuned-spam-detection
spamfighters/bert-base-uncased-finetuned-spam-detection
bert-base-uncased-finetuned-spam-detection is a text classification model from spamfighters. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Dec 7, 2024
- Updated
- Dec 7, 2024
- SHA
- f73108c5
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert-base-uncased-finetuned-spam-detection
This model is a fine-tuned version of deepvk/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0192
- Accuracy: 0.9958
- F1: 0.9957
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.0222 | 1.0 | 5685 | 0.0157 | 0.9956 | 0.9956 |
| 0.0087 | 2.0 | 11370 | 0.0192 | 0.9958 | 0.9957 |
Framework versions
- Transformers 4.46.0
- Pytorch 2.4.1
- Datasets 3.0.2
- Tokenizers 0.20.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 7, 2024
- Updated
- Dec 7, 2024