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")# 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
Download final_report.json from rootfs/function-call-sentinel: direct link, hf CLI and curl.
- Browser
- Download file 227 Bytes
-
https://huggingface.co/rootfs/function-call-sentinel/resolve/main/final_report.json
- Command line
-
hf download hf://rootfs/function-call-sentinel/final_report.json
-
curl -L -o final_report.json https://huggingface.co/rootfs/function-call-sentinel/resolve/main/final_report.json
227 Bytes
| { | |
| "accuracy": 0.9599819637019502, | |
| "injection_precision": 0.9714614499424626, | |
| "injection_recall": 0.9481132075471698, | |
| "injection_f1": 0.959645333636467, | |
| "roc_auc": 0.9928215719631005, | |
| "macro_f1": 0.9599791788361205 | |
| } |