Text Classification
Transformers
Safetensors
English
qwen3
prompt-injection
jailbreak-detection
security
palisade
Eval Results (legacy)
text-embeddings-inference
Instructions to use omanshb/NLP_Project_Input_Guard_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omanshb/NLP_Project_Input_Guard_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="omanshb/NLP_Project_Input_Guard_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("omanshb/NLP_Project_Input_Guard_Model") model = AutoModelForSequenceClassification.from_pretrained("omanshb/NLP_Project_Input_Guard_Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 910fe6fd68f249024bbcab7cc92f8cf6c6e43766593285f477ef9e2162ac651c
- Size of remote file:
- 1.19 GB
- SHA256:
- b28a3a16814d30ed1bb4f135c958ff0840bb46c00e911af74f3900041140257c
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