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potemin/jailbreak_detector_v2

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  • files 8
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  • author_summary 2 models
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Abliteration classifier · v1.0.0
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Downloads · lifetime
807
13 last 30d - cooling
Likes
1
Model age
2.1y ago
created 2024-09-13
Downloads over time
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Metadata

License
apache-2.0
Tags
transformers tensorboard safetensors deberta-v2 text-classification generated_from_trainer base_model:protectai/deberta-v3-base-prompt-injection-v2 base_model:finetune:protectai/deberta-v3-base-prompt-injection-v2 license:apache-2.0 text-embeddings-inference endpoints_compatible region:us

Related

Total size
704 MB
Files
8
Quantizations
1
Registered
2026-08-26 13:02
Last updated on HF
2024-09-14 07:27

Files by quantization

Auxiliary files 8 files 712 MB
model.safetensors 704 MB e0a3f149 download
training_args.bin 5.12 KB ef17b4de download
tokenizer.json 8.25 MB f5c72bef download
README.md 1.55 KB 3bc55773 download
.gitattributes 1.48 KB a6344aac download
tokenizer_config.json 1.33 KB 7642f93f download
config.json 1014 B 9fffbbc4 download
special_tokens_map.json 970 B 83fb22de download

README current version from Hugging Face


library_name: transformers
license: apache-2.0
base_model: protectai/deberta-v3-base-prompt-injection-v2
tags:

  • generated_from_trainer
    metrics:
  • accuracy
  • f1
    model-index:
  • name: jailbreak_detector_v2
    results: []

jailbreak_detector_v2

This model is a fine-tuned version of protectai/deberta-v3-base-prompt-injection-v2 on the None dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.3056
  • Accuracy: 0.8642
  • F1: 0.8523

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: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 1.0 182 0.3056 0.8642 0.8523
No log 2.0 364 0.3350 0.8889 0.8824

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1

README history 1 version

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

  1. 2024-09-14End of trainingf9bbb811.6 KB
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