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leomaurodesenv/nli-MiniLM2-L6-H768-jailbreakv-28k-augmented

Abliteration classifier · v1.0.0
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138
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Model age
2d ago
created 2026-09-14
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Metadata

License
apache-2.0
Tags
transformers safetensors roberta text-classification generated_from_trainer base_model:cross-encoder/nli-MiniLM2-L6-H768 base_model:finetune:cross-encoder/nli-MiniLM2-L6-H768 license:apache-2.0 text-embeddings-inference endpoints_compatible region:us

Related

Total size
313 MB
Files
7
Quantizations
1
Registered
2026-09-14 01:56
Last updated on HF
2026-09-16 04:31

Files by quantization

Auxiliary files 7 files 317 MB
model.safetensors 313 MB 4644da5f download
training_args.bin 5.14 KB d4eb2ecf download
tokenizer.json 3.39 MB 72a0cbc8 download
README.md 2.14 KB 919ba337 download
.gitattributes 1.48 KB a6344aac download
config.json 922 B f36112ed download
tokenizer_config.json 359 B a6d24b8e download

README current version from Hugging Face


library_name: transformers
license: apache-2.0
base_model: cross-encoder/nli-MiniLM2-L6-H768
tags:

  • generated_from_trainer
    metrics:
  • accuracy
    model-index:
  • name: nli-MiniLM2-L6-H768-jailbreakv-28k
    results: []

nli-MiniLM2-L6-H768-jailbreakv-28k

This model is a fine-tuned version of cross-encoder/nli-MiniLM2-L6-H768 on an unknown dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.0000
  • Accuracy: 1.0

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
0.0002 1.0 1121 0.0001 1.0
0.0001 2.0 2242 0.0000 1.0
0.0000 3.0 3363 0.0000 1.0
0.0000 4.0 4484 0.0000 1.0
0.0000 5.0 5605 0.0000 1.0
0.0000 6.0 6726 0.0000 1.0
0.0000 7.0 7847 0.0000 1.0
0.0000 8.0 8968 0.0000 1.0
0.0000 9.0 10089 0.0000 1.0
0.0000 10.0 11210 0.0000 1.0

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. 2026-09-16End of trainingb6fd3092.2 KB
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  2. 2026-09-16Training in progress, epoch 1c9dc0792.1 KB
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  3. 2026-09-15End of training42e17802.1 KB
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  4. 2026-09-15Training in progress, epoch 1b4339082.1 KB
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  5. 2026-09-15End of training8c7d9712 KB
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  6. 2026-09-15Training in progress, epoch 16cb4cd02.1 KB
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  7. 2026-09-14End of training7ae603a2.1 KB
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  8. 2026-09-14Training in progress, epoch 1f6b9be82.1 KB
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  9. 2026-09-14End of trainingd973d532.2 KB
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  10. 2026-09-14Training in progress, epoch 1dcbe6622.1 KB
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