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ashield-ai/jailbreak-prompt-classification

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  • files 8
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  • author_summary 2 models
  • readme_text full
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Abliteration classifier · v1.0.0
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  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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Downloads · lifetime
42
3 last 30d - cooling
Likes
0
Model age
21mo ago
created 2025-01-17
Downloads over time
Now44→from2↑2,100%
03061912 on Jan 15, 202544 on Oct 1183 on Sep 10, 2025Jan '25Apr '25Jul '25Oct '25JanAprJulOct
Jan 15, 2025 → Oct 11 · 130 snapshots · spans 634 days

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Metadata

License
apache-2.0
Tags
transformers tensorboard safetensors modernbert text-classification generated_from_trainer base_model:ashield-ai/prompt-classification-bert base_model:finetune:ashield-ai/prompt-classification-bert license:apache-2.0 text-embeddings-inference endpoints_compatible region:us

Related

Total size
571 MB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-01-18 08:22

Files by quantization

Auxiliary files 8 files 574 MB
model.safetensors 571 MB 141228a0 download
training_args.bin 5.30 KB d00ce039 download
tokenizer.json 3.42 MB 4d3c8ca0 download
tokenizer_config.json 20.5 KB 45f58537 download
README.md 1.80 KB 9df268c0 download
config.json 1.49 KB de1f94df download
.gitattributes 1.48 KB a6344aac download
special_tokens_map.json 694 B 6bf65ea1 download

README current version from Hugging Face


library_name: transformers
license: apache-2.0
base_model: ashield-ai/prompt-classification-bert
tags:

  • generated_from_trainer
    metrics:
  • f1
    model-index:
  • name: jailbreak-prompt-classification
    results: []

jailbreak-prompt-classification

This model is a fine-tuned version of ashield-ai/prompt-classification-bert on an unknown dataset.
It achieves the following results on the evaluation set:

  • Loss: nan
  • F1: 0.4955

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 5
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1
0.0 1.0 6532 nan 0.4955
0.0 2.0 13064 nan 0.4955
0.0 3.0 19596 nan 0.4955
0.0 4.0 26128 nan 0.4955
0.0 5.0 32660 nan 0.4955

Framework versions

  • Transformers 4.48.0
  • Pytorch 2.4.0+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0

README history 2 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2025-01-18End of trainingd9717341.8 KB
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  2. 2025-01-18End of trainingc26c7aa1.8 KB
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