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") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_name": "answerdotai/ModernBERT-base", | |
| "num_labels": 2, | |
| "label2id": { | |
| "SAFE": 0, | |
| "INJECTION_RISK": 1 | |
| }, | |
| "id2label": { | |
| "0": "SAFE", | |
| "1": "INJECTION_RISK" | |
| }, | |
| "batch_size": 32, | |
| "epochs": 5, | |
| "learning_rate": 3e-05, | |
| "max_length": 512, | |
| "use_class_weights": true, | |
| "class_weights": [ | |
| 0.9990699887275696, | |
| 1.0009300708770752 | |
| ] | |
| } |