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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from omanshb/NLP_Project_Input_Guard_Model: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/omanshb/NLP_Project_Input_Guard_Model/resolve/main/tokenizer.json
- Command line
-
hf download hf://omanshb/NLP_Project_Input_Guard_Model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/omanshb/NLP_Project_Input_Guard_Model/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 20fe20ee628db393257f9e2bef6647cb751415cbcc12110ab2dd253987898905
- Size of remote file:
- 11.4 MB
- SHA256:
- bae3e39d56cfdb7b650cb318344d5c0f071d19fc9868ce086fef0cee78d5e7ff
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