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README.md

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+---
+language: en
+license: mit
+library_name: transformers
+tags:
+- ai-detection
+- text-classification
+- onnx
+- education
+---
+
+# AI Detector PGX
+
+BERT-based classifier for detecting AI-generated text in student essays. Trained on PG assignments.
+
+## Quick Start
+
+### Python
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_id = "darwinkernelpanic/ai-detector-pgx"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+text = "The mitochondria is the powerhouse of the cell..."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    ai_prob = probs[0][1].item()
+
+print(f"AI Probability: {ai_prob:.2%}")
+```
+
+### JavaScript (ONNX)
+```javascript
+import * as ort from 'onnxruntime-web';
+
+const session = await ort.InferenceSession.create('model.onnx');
+// Tokenize with @xenova/transformers, then run inference
+const results = await session.run({ input_ids, attention_mask });
+const logits = results.logits.data;
+const aiProb = Math.exp(logits[1]) / (Math.exp(logits[0]) + Math.exp(logits[1]));
+```
+
+## Model Details
+
+- **Base:** prajjwal1/bert-tiny (4.4M params)
+- **Classes:** human (0), ai (1)  
+- **Sequence length:** 512 tokens
+- **ONNX size:** 255MB
+
+## Limitations
+
+Trained on academic essays — may not generalize to all text types.