Text Classification
Transformers
Safetensors
qwen3
content-safety
prompt-injection
multi-label-classification
safety
multilingual
hikmaai
Instructions to use HikmaAI/HikmaGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HikmaAI/HikmaGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HikmaAI/HikmaGuard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HikmaAI/HikmaGuard") model = AutoModelForSequenceClassification.from_pretrained("HikmaAI/HikmaGuard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 894 Bytes
6604218 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | {
"schema_version": 1,
"taxonomy_version": "unified-guard-v1",
"policy_version": "unified-guard-v1",
"classifier_name": "unified_guard",
"model_config_sha256": "f1b8025a331a052b2117acb5c0ec3ccbded6904655c41b1ea7365de78277e138",
"model_artifact_id": "d21042f88a814cc19011421f0cf972be",
"heads": {
"hate": {
"threshold": 0.15102779865264893
},
"abuse": {
"threshold": 0.32167303562164307
},
"violence": {
"threshold": 0.5765419602394104
},
"self_harm": {
"threshold": 0.14318770170211792
},
"sexual_harm": {
"threshold": 0.1919327825307846
},
"misconduct": {
"threshold": 0.7745833992958069
},
"dangerous_capability": {
"threshold": 0.7549149990081787
},
"prompt_injection": {
"threshold": 0.98
},
"indirect_prompt_injection": {
"threshold": 0.992
}
}
}
|