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 training_args.bin from omanshb/NLP_Project_Input_Guard_Model: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/omanshb/NLP_Project_Input_Guard_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://omanshb/NLP_Project_Input_Guard_Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/omanshb/NLP_Project_Input_Guard_Model/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 7df4678be0ec9597f8680b88e0ce5ae828041b584dcb94d3867f6c598be7109a
- Size of remote file:
- 5.27 kB
- SHA256:
- 8ab26a72e9d92b61613e2ec9b1c0853d484b68394b0cc45420f1293030252abe
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