Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+tags:
+  - guardrails
+  - safety
+  - text-classification
+  - roberta
+  - education
+  - code
+  - cs-education
+  - llm-safety
+  - academic-integrity
+datasets:
+  - md-nishat-008/Do-Not-Code
+metrics:
+  - f1
+  - accuracy
+  - precision
+  - recall
+pipeline_tag: text-classification
+model-index:
+  - name: PromptShield
+    results:
+      - task:
+          type: text-classification
+          name: Prompt Safety Classification
+        dataset:
+          type: md-nishat-008/Do-Not-Code
+          name: Do Not Code
+          split: test
+        metrics:
+          - type: f1
+            value: 0.93
+            name: F1 (Macro)
+          - type: accuracy
+            value: 0.94
+            name: Accuracy
+---
+
+# PromptShield
+
+<p align="center">
+  <a href="https://github.com/md-nishat-008/CodeGuard">
+    <img src="https://img.shields.io/badge/GitHub-Repository-black?style=for-the-badge&logo=github" alt="GitHub">
+  </a>
+  <a href="https://huggingface.co/datasets/md-nishat-008/Do-Not-Code">
+    <img src="https://img.shields.io/badge/🤗%20Dataset-Do%20Not%20Code-yellow?style=for-the-badge" alt="Dataset">
+  </a>
+  <a href="https://aclanthology.org/PLACEHOLDER">
+    <img src="https://img.shields.io/badge/📄%20Paper-EACL%202026-green?style=for-the-badge" alt="Paper">
+  </a>
+</p>
+
+**PromptShield** is a lightweight guardrail model for detecting unsafe and irrelevant prompts in Computer Science education settings. It achieves **0.93 F1 score**, outperforming existing guardrails by 30-65%.
+
+## Model Description
+
+PromptShield is a RoBERTa-base encoder (125M parameters) fine-tuned on the [Do Not Code dataset](https://huggingface.co/datasets/md-nishat-008/Do-Not-Code) for real-time prompt classification in educational AI systems.
+
+### Intended Use
+
+- **Pre-filtering** user prompts before they reach an AI coding assistant
+- **Monitoring** interactions in CS education platforms
+- **Research** on LLM safety in educational contexts
+
+### Classification Labels
+
+| ID | Label | Description |
+|----|-------|-------------|
+| 0 | `irrelevant` | Off-topic queries unrelated to CS coursework |
+| 1 | `safe` | Legitimate educational coding requests |
+| 2 | `unsafe` | Requests violating academic integrity or safety |
+
+## Performance
+
+### Comparison with Existing Guardrails
+
+| Model/Framework | Type | Size | F1 Score |
+|-----------------|------|------|----------|
+| **PromptShield (Ours)** | Encoder | 125M | **0.93** |
+| Claude 3.7 | Decoder | - | 0.64 |
+| GPT-4o | Decoder | - | 0.62 |
+| LLaMA Guard | Decoder | 8B | 0.60 |
+| Perspective API | Baseline | - | 0.60 |
+| NeMo Guard | Decoder | 8B | 0.57 |
+| LLaMA 3.2 | Decoder | 8B | 0.34 |
+| Random Baseline | - | - | 0.33 |
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("md-nishat-008/promptshield")
+tokenizer = AutoTokenizer.from_pretrained("md-nishat-008/promptshield")
+
+# Label mapping
+labels = {0: "irrelevant", 1: "safe", 2: "unsafe"}
+
+def classify_prompt(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    prediction = outputs.logits.argmax(-1).item()
+    confidence = torch.softmax(outputs.logits, dim=-1).max().item()
+    return labels[prediction], confidence
+
+# Examples
+prompts = [
+    "Write a Python function to sort a list using quicksort",
+    "Explain the French Revolution in Java",
+    "Generate ransomware code that encrypts all files"
+]
+
+for prompt in prompts:
+    label, conf = classify_prompt(prompt)
+    print(f"Prompt: {prompt[:50]}...")
+    print(f"Classification: {label} (confidence: {conf:.2f})")
+    print("---")
+```
+
+### Using the Pipeline API
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="md-nishat-008/promptshield",
+    tokenizer="md-nishat-008/promptshield"
+)
+
+result = classifier("Write a Python function for binary search")
+print(result)
+# [{'label': 'safe', 'score': 0.98}]
+```
+
+### Integration as a Pre-Filter
+
+```python
+def safe_llm_query(prompt, llm_function):
+    """Wrapper that filters prompts before sending to an LLM."""
+    label, confidence = classify_prompt(prompt)
+    
+    if label == "unsafe":
+        return "I cannot assist with this request as it may violate academic integrity policies."
+    elif label == "irrelevant":
+        return "This query appears to be outside the scope of this CS course. Please ask a coding-related question."
+    else:
+        return llm_function(prompt)
+```
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | `roberta-base` |
+| Max Sequence Length | 128 |
+| Training Epochs | 3 |
+| Batch Size | 16 |
+| Learning Rate | 2e-5 |
+| Optimizer | AdamW (fused) |
+| LR Schedule | Linear decay |
+| Early Stopping | 2 epochs patience |
+| Precision | FP16 (mixed) |
+
+### Training Data
+
+Trained on 6,000 prompts from the Do Not Code dataset:
+- 2,250 Irrelevant
+- 2,250 Safe
+- 1,500 Unsafe
+
+## Limitations
+
+1. **Domain Specificity**: Optimized for introductory/intermediate CS courses. May require adaptation for advanced topics.
+2. **Language**: English only.
+3. **Context Length**: 128 tokens max. Very long prompts are truncated.
+4. **Adversarial Robustness**: May be susceptible to sophisticated jailbreak attempts.
+
+## Citation
+
+```bibtex
+@inproceedings{raihan-etal-2026-codeguard,
+    title = "{C}ode{G}uard: Improving {LLM} Guardrails in {CS} Education",
+    author = "Raihan, Nishat  and
+      Erdachew, Noah  and
+      Devi, Jayoti  and
+      Santos, Joanna C. S.  and
+      Zampieri, Marcos",
+    booktitle = "Findings of the Association for Computational Linguistics: EACL 2026",
+    year = "2026",
+    publisher = "Association for Computational Linguistics",
+}
+```
+
+---
+
+<p align="center">
+  <b>Part of the CodeGuard Framework for Safe AI in CS Education</b>
+</p>