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Llama-Prompt-Guard-2-22M-ft-custom
alilf/Llama-Prompt-Guard-2-22M-ft-custom
Llama-Prompt-Guard-2-22M-ft-custom is a text classification model from alilf. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Nov 28, 2025
- Updated
- Nov 28, 2025
- SHA
- 3cdbf9f8
Try a prompt
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. -->
Llama-Prompt-Guard-2-22M-ft-custom
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1352
- Accuracy: 1.0
- Precision: 1.0000
- Recall: 1.0000
- F1: 1.0000
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: 32
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 8 | 0.3232 | 0.9667 | 0.9500 | 1.0000 | 0.9744 |
| No log | 2.0 | 16 | 0.1534 | 1.0 | 1.0000 | 1.0000 | 1.0000 |
| No log | 3.0 | 24 | 0.1194 | 1.0 | 1.0000 | 1.0000 | 1.0000 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Nov 28, 2025
- Updated
- Nov 28, 2025