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assoni2002/finetuned-wav2vec2-jailbreak-roleplay

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  • files 6
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  • author_summary 5 models
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Abliteration classifier · v1.0.0
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Downloads · lifetime
23
6 last 30d - stable
Likes
0
Model age
14mo ago
created 2025-08-02
Downloads over time
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Metadata

License
apache-2.0
Tags
transformers safetensors wav2vec2 audio-classification generated_from_trainer base_model:assoni2002/wav2vec2-jailbreak-classification_new base_model:finetune:assoni2002/wav2vec2-jailbreak-classification_new license:apache-2.0 endpoints_compatible region:us

Related

Total size
361 MB
Files
6
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-08-02 00:08

Files by quantization

Auxiliary files 6 files 361 MB
model.safetensors 361 MB 25b152c4 download
training_args.bin 5.70 KB 98fc7a19 download
config.json 2.29 KB c9a3d02f download
README.md 1.82 KB 640eb3cd download
.gitattributes 1.48 KB a6344aac download
preprocessor_config.json 215 B a0b7227f download

README current version from Hugging Face


library_name: transformers
license: apache-2.0
base_model: assoni2002/wav2vec2-jailbreak-classification_new
tags:

  • generated_from_trainer
    metrics:
  • accuracy
    model-index:
  • name: finetuned-wav2vec2-jailbreak-roleplay
    results: []

finetuned-wav2vec2-jailbreak-roleplay

This model is a fine-tuned version of assoni2002/wav2vec2-jailbreak-classification_new on an unknown dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.2063
  • Accuracy: 0.95

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use 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: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.4178 1.0 5 0.9412 0.6
0.4836 2.0 10 0.4918 0.8
0.3961 3.0 15 0.3995 0.85
0.3672 4.0 20 0.3872 0.85
0.3326 5.0 25 0.3846 0.85

Framework versions

  • Transformers 4.53.3
  • Pytorch 2.7.1+cu126
  • Datasets 4.0.0
  • Tokenizers 0.21.2

README history 1 version

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

  1. 2025-08-02End of trainingde217471.8 KB
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