Add model card

README.md

@@ -0,0 +1,68 @@
+---
+language:
+  - en
+  - zh
+license: mit
+tags:
+  - url-classification
+  - binary-classification
+  - autoresearch
+datasets:
+  - iowacat
+metrics:
+  - accuracy: 0.9962
+model_index:
+  - name: url-classifier
+    results:
+      - task:
+          type: text-classification
+          name: URL Binary Classification
+        dataset:
+          type: iowacat
+          name: URL Classification Dataset
+        metrics:
+          - type: accuracy
+            value: 0.9962
+---
+
+# URL Classifier — Autoresearch
+
+Binary classifier that predicts whether a URL is a **list page (A)** or a **detail page (B)**.
+
+## Model Details
+
+- **Architecture**: Custom transformer (Autoresearch framework)
+- **Parameters**: ~161M
+- **Depth**: 4 layers
+- **Model dim**: 384
+- **Vocab**: cl100k_base (100,277 tokens)
+- **Max seq len**: 64
+- **Training time**: 5 minutes on RTX 4060 Laptop
+
+## Training
+
+Trained with the Autoresearch framework, which combines:
+- **Muon** optimizer for attention/MLP layers
+- **AdamW** for embeddings
+- **Sliding window attention** (SSSL pattern)
+- **Value embeddings** for alternating layers
+
+Final loss: ~0.002 | Accuracy: **99.62%**
+
+## Usage
+
+```python
+from src.prepare import Tokenizer
+
+tokenizer = Tokenizer.from_directory()
+# Encode a URL
+ids = tokenizer.encode("https://example.com/product/123")
+# Run through model + class_head for classification
+```
+
+## Class Labels
+
+| Label | Meaning |
+|-------|---------|
+| 0 | A — List page |
+| 1 | B — Detail page |