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spam_classifier_phobert
Thanhhhung/spam_classifier_phobert
spam_classifier_phobert is a text classification model from Thanhhhung. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Aug 8, 2025
- Updated
- Aug 8, 2025
- SHA
- 0a3b0568
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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. -->
spam_classifier_phobert
This model is a fine-tuned version of vinai/phobert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1581
- Accuracy: 0.9551
- F1: 0.8345
- Precision: 0.8286
- Recall: 0.8406
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: 16
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 288 | 0.1648 | 0.9434 | 0.7914 | 0.7857 | 0.7971 |
| 0.2188 | 2.0 | 576 | 0.1659 | 0.9492 | 0.8060 | 0.8308 | 0.7826 |
| 0.2188 | 3.0 | 864 | 0.1581 | 0.9551 | 0.8345 | 0.8286 | 0.8406 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Aug 8, 2025
- Updated
- Aug 8, 2025