Text Classification
Transformers
Safetensors
English
qwen3
text-generation
agent
text-embeddings-inference
Instructions to use Rakancorle1/PolicyGuard-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rakancorle1/PolicyGuard-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rakancorle1/PolicyGuard-4B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Rakancorle1/PolicyGuard-4B") model = AutoModelForCausalLM.from_pretrained("Rakancorle1/PolicyGuard-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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It identifies whether an agent’s long-horizon trajectory complies with externally imposed or human-specified policies.
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Built on the **PolicyGuardBench** dataset, it achieves **high accuracy**, **cross-domain generalization**, and **remarkable efficiency** — proving that compact guardrails can be both **accurate** and **deployable**.
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## 🧩 Key Objectives
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It identifies whether an agent’s long-horizon trajectory complies with externally imposed or human-specified policies.
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Built on the **PolicyGuardBench** dataset, it achieves **high accuracy**, **cross-domain generalization**, and **remarkable efficiency** — proving that compact guardrails can be both **accurate** and **deployable**.
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For more details, refer to our paper: *[Towards Policy-Compliant Agents: Learning Efficient Guardrails For Policy Violation Detection](https://arxiv.org/abs/2510.03485)*.
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## 🧩 Key Objectives
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