gemma-3-270m-classifier

Gemma-3-270M Threat Classifier

Model Description

Fine-tuned version of google/gemma-3-270m for binary threat classification (Safe vs Unsafe prompts).

Training Details

• Base Model: google/gemma-3-270m
• Task: Binary Text Classification
• Training Date: 2025-12-31
• Training Framework: Hugging Face Transformers

Hyperparameters

• Learning Rate: 2e-05
• Batch Size: 16
• Epochs: 10
• Max Length: 512
• Optimizer: adamw_torch

Performance (Test Set)

• Accuracy: 0.8363
• Precision: 0.8232
• Recall: 0.8882
• F1 Score: 0.8544
• AUC-ROC: 0.9101

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("path/to/model")
tokenizer = AutoTokenizer.from_pretrained("path/to/model")

text = "Your prompt here"
inputs = tokenizer(text, return_tensors="pt", max_length=256, truncation=True)
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
label = "unsafe" if prediction == 1 else "safe"

Labels

• 0: Safe
• 1: Unsafe (Threat/Jailbreak)