Create README.md

README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen3-Embedding-0.6B
+pipeline_tag: text-classification
+---
+
+# Argus
+
+**AI-Generated Text Detection Classifier**
+
+Argus is a binary text classifier that detects whether text was written by a human or generated by AI. It is fine-tuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) using a classification head with last-token pooling.
+
+## Features
+
+- **High Accuracy**: Achieves near-perfect classification on held-out test data
+- **Long Context**: Supports sequences up to 4,096 tokens with automatic chunking for longer texts
+- **Fast Inference**: Optimized with Flash Attention 2 and bfloat16 precision on CUDA
+- **Batch Processing**: Parallel tokenization and batched inference for high throughput
+
+## Installation
+
+```bash
+pip install torch transformers
+```
+
+## Minimal Inference Example
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import AutoModel, AutoTokenizer, AutoConfig
+
+class Qwen3ForSequenceClassification(nn.Module):
+    """Qwen3-Embedding with classification head using last-token pooling."""
+    
+    def __init__(self, model_name="Qwen/Qwen3-Embedding-0.6B", num_labels=2):
+        super().__init__()
+        self.encoder = AutoModel.from_pretrained(
+            model_name,
+            torch_dtype=torch.bfloat16,
+            trust_remote_code=True,
+        )
+        hidden_size = AutoConfig.from_pretrained(model_name).hidden_size
+        self.classifier = nn.Linear(hidden_size, num_labels)
+    
+    def forward(self, input_ids, attention_mask):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        # Last-token pooling (not CLS token)
+        pooled = outputs.last_hidden_state[:, -1]
+        return self.classifier(pooled)
+
+
+# Load model and weights
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = Qwen3ForSequenceClassification()
+model.load_state_dict(torch.load("weights/model.pt", map_location=device, weights_only=True))
+model.to(device).eval()
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("weights/", padding_side="left", trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Inference
+text = "Your text to classify here."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=4096)
+inputs = {k: v.to(device) for k, v in inputs.items()}
+
+with torch.no_grad():
+    logits = model(inputs["input_ids"], inputs["attention_mask"])
+    probs = F.softmax(logits.float(), dim=-1)[0]
+    
+label = "ai" if probs[1] > probs[0] else "human"
+confidence = probs[1].item() if label == "ai" else probs[0].item()
+
+print(f"Label: {label}, Confidence: {confidence:.2%}")
+# Example output: Label: human, Confidence: 94.32%
+```
+
+## Model Architecture
+
+| Component | Details |
+|-----------|---------|
+| Base Model | Qwen/Qwen3-Embedding-0.6B |
+| Hidden Size | 1024 |
+| Parameters | ~600M |
+| Pooling | Last-token (not CLS) |
+| Classification Head | Linear (1024 → 2) |
+| Precision | bfloat16 (CUDA) / float32 (CPU) |
+
+## Performance
+
+| Metric | Score |
+|--------|-------|
+| Accuracy | 98.86% |
+
+*Note: These metrics are from the validation set during training. Real-world performance may vary depending on the domain and AI models used to generate text.*
+
+## Training Data
+
+Trained on a combination of:
+- [RAID](https://huggingface.co/datasets/liamdugan/raid) - Multi-domain, multi-model dataset
+- [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) - Human vs ChatGPT responses
+- [AI-human-text](https://huggingface.co/datasets/andythetechnerd03/AI-human-text)
+- [AI Text Detection Pile](https://huggingface.co/datasets/artem9k/ai-text-detection-pile)
+
+## License
+
+MIT License
+
+## Citation
+
+If you use Argus in your research, please cite:
+
+```bibtex
+@software{,
+  title={Argus: AI-Generated Text Detection Classifier},
+  author={Xi Nai Lai},
+  year={2026},
+  url={https://huggingface.co/johnbean393/argus/}
+}
+```