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
File size: 376 Bytes
d23f948 4a82add d23f948 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"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
]
} |