Text Classification
Transformers
Safetensors
Turkish
English
modernbert
prompt-injection
jailbreak-detection
llm-security
ai-security
guardrails
turkish
prompt-injection-detection
llm-guardrails
rag-security
mmbert
multilingual
Eval Results (legacy)
text-embeddings-inference
Instructions to use halilneed/turkish-prompt-injection-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use halilneed/turkish-prompt-injection-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="halilneed/turkish-prompt-injection-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("halilneed/turkish-prompt-injection-classifier") model = AutoModelForSequenceClassification.from_pretrained("halilneed/turkish-prompt-injection-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
12 days ago
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v2.1: recommended threshold 0.3 -> 0.05 (re-checked on the released seed, no test data); seed and soup results
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Add recall mode (v1 + v2 average) to detect.py; link the false-alarm benchmark
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Release v2: mmBERT-base, obfuscation augmentation, decoder; selection protocol kept apart from test
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Release v1: Turkish prompt injection classifier (BERTurk, source-held-out evaluation)
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