Text Classification
Transformers
Safetensors
English
modernbert
security
jailbreak-detection
prompt-injection
llm-safety
Eval Results (legacy)
text-embeddings-inference
Instructions to use rootfs/function-call-sentinel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootfs/function-call-sentinel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rootfs/function-call-sentinel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rootfs/function-call-sentinel") model = AutoModelForSequenceClassification.from_pretrained("rootfs/function-call-sentinel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add YAML metadata to model card
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Update model with enhanced jailbreak detection (F1: 95.96%)
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Add hidden_act for Candle compatibility
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Update model with improved training (97.71% F1)
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Upload folder using huggingface_hub
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Upload folder using huggingface_hub
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initial commit
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