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leomaurodesenv/roberta-base-trustairlab-jailbreak-augmented

Abliteration classifier · v1.0.0
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Unclassified

No clear signals of an abliteration technique in this model.
Confidence
UNKNOWN
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  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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Downloads · lifetime
255
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1
Model age
4d ago
created 2026-09-12
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Metadata

License
mit
Tags
transformers safetensors roberta text-classification generated_from_trainer base_model:FacebookAI/roberta-base base_model:finetune:FacebookAI/roberta-base license:mit text-embeddings-inference endpoints_compatible region:us

Related

Total size
476 MB
Files
7
Quantizations
1
Registered
2026-09-12 03:55
Last updated on HF
2026-09-13 15:30

Files by quantization

Auxiliary files 7 files 479 MB
model.safetensors 476 MB 883b208a download
training_args.bin 5.14 KB 5ba5cd2b download
tokenizer.json 3.39 MB 7e19d907 download
README.md 1.86 KB 1e5bbb8e download
.gitattributes 1.48 KB a6344aac download
config.json 847 B 513a45f3 download
tokenizer_config.json 359 B a6d24b8e download

README current version from Hugging Face


library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:

  • generated_from_trainer
    metrics:
  • accuracy
    model-index:
  • name: roberta-base-trustairlab-jailbreak
    results: []

roberta-base-trustairlab-jailbreak

This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.1915
  • Accuracy: 0.9417

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • 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: 50
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2976 1.0 605 0.2343 0.9379
0.1646 2.0 1210 0.2263 0.9384
0.2915 3.0 1815 0.1913 0.9417
0.1348 4.0 2420 0.2106 0.9371
0.0820 5.0 3025 0.2849 0.9379
0.0598 6.0 3630 0.3135 0.9404

Framework versions

  • Transformers 5.2.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.5.0
  • Tokenizers 0.22.2

README history 10 versions

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

  1. 2026-09-13End of training20e0dca2 KB
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  2. 2026-09-13Training in progress, epoch 1bb506971.9 KB
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  3. 2026-09-13End of training3714dfa1.9 KB
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  4. 2026-09-13Training in progress, epoch 192cabe21.9 KB
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  5. 2026-09-12End of trainingd2ca6af1.9 KB
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  6. 2026-09-12Training in progress, epoch 12a8975f1.9 KB
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  7. 2026-09-12End of trainingaf88af91.9 KB
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  8. 2026-09-12Training in progress, epoch 10f866911.9 KB
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  9. 2026-09-12End of training84ca68e2.1 KB
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  10. 2026-09-12Training in progress, epoch 15ff7e921.9 KB
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