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leomaurodesenv/roberta-base-trustairlab-jailbreak

Abliteration classifier · v1.0.0
?
Primary method

Unclassified

No clear signals of an abliteration technique in this model.
Confidence
UNKNOWN
Why this label 1 signal
No classification signals present. This may not be an abliterated model at all - it could be a repackaging, a merge with unrelated goals, or unrelated content that mentions the term.
  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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Downloads · lifetime
205
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0
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 13:36

Files by quantization

Auxiliary files 7 files 479 MB
model.safetensors 476 MB 250cd823 download
training_args.bin 5.14 KB a4e97162 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 trainingf92a72e1.9 KB
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  2. 2026-09-13Training in progress, epoch 191bd1872 KB
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  3. 2026-09-13End of training54de4ce1.9 KB
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  4. 2026-09-13Training in progress, epoch 1e4bc7c82 KB
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  5. 2026-09-12End of training99cc18e1.9 KB
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  6. 2026-09-12Training in progress, epoch 1b309c9b2.1 KB
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  7. 2026-09-12End of training623fdf11.9 KB
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  8. 2026-09-12Training in progress, epoch 1cff21ee2 KB
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  9. 2026-09-12End of training43e36a71.9 KB
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  10. 2026-09-12Training in progress, epoch 1a6f30132.1 KB
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