Text Classification
Transformers
Safetensors
English
distilbert
safety
guardrail
llm-guardrails
text-embeddings-inference
Instructions to use urbanspr1nter/search-query-safety-guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use urbanspr1nter/search-query-safety-guard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="urbanspr1nter/search-query-safety-guard")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("urbanspr1nter/search-query-safety-guard") model = AutoModelForSequenceClassification.from_pretrained("urbanspr1nter/search-query-safety-guard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload final model (iter5): eval1 F1 0.994, eval2 (fresh) F1 1.000, 0 dangerous leaks. Added id2label/label2id + model card.
f182ccf verified - Xet hash:
- 43d44320bcd6ab53c0b0c7c6f4621b1166f782103c5335169dec59be6908f5d0
- Size of remote file:
- 268 MB
- SHA256:
- 23ff526bba82b2e070fed0000fc6ccf0eba4bc8e4e364ee3cf1bb390d7369e9a
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