Update README.md

README.md

@@ -1,3 +1,68 @@
 ---
+language: en
 license: mit
+tags:
+- pytorch
+- regression
+- dot-product
+- bilinear-networks
+metrics:
+- loss
+pipeline_tag: table-regression
 ---
+
+# SBL-NET (Scalar Bilinear Linear Network)
+
+A hybrid PyTorch neural network designed to **highly accurately compute the scalar (dot) product** of split sub-vectors without any data normalization (Z-score, etc.).
+
+## 🔬 Architecture & Features
+
+The main highlight of this model is the integration of a rare **bilinear layer (`nn.Bilinear`)** at the input stage, combined with classic fully connected layers (`nn.Linear`) and the `SELU` activation function.
+* The network accepts an input tensor of shape `[batch, 4]` and splits it into two vectors: `A [batch, 2]` and `B [batch, 2]`.
+* The bilinear layer efficiently extracts cross-features between the vectors, allowing the model to reduce the error to an impressive **0.0569%**.
+
+## 📊 Training Results
+
+* **Loss Function:** Smooth L1 Loss
+* **Optimizer:** Adam (with StepLR scheduler)
+* **Error Rate:** ~0.0569% (Accuracy ~99.94%)
+* **Extreme Test Case:** 
+  * Input: `[[-6.0, 70.0, 4.0, -196.0]]`
+  * Expected Mathematical Answer: `-13744.0000`
+  * Actual Network Prediction: `-13769.5225`
+
+## 🧮 Model Statistics
+
+* **Total Parameters:** 52,101
+* **Trainable Parameters:** 52,101
+* **Non-trainable Parameters:** 0
+* **Model Size:** ~208 KB (Weights in FP32)
+* **Input Shape:** `[batch_size, 4]`
+* **Output Shape:** `[batch_size, 1]`
+
+## 💻 How to Use
+
+You can download the architecture file and the model weights directly from this repository:
+
+```python
+import torch as t
+from huggingface_hub import hf_hub_download
+
+# 1. Download the architecture and weights files (replace YOUR_USERNAME)
+REPO_ID = "YOUR_USERNAME/sbl-net"
+hf_hub_download(repo_id=REPO_ID, filename="model.py", local_dir=".")
+hf_hub_download(repo_id=REPO_ID, filename="model_weights_hybrid.pth", local_dir=".")
+
+# 2. Import the model class and load the weights
+from model import WebAISC
+
+model = WebAISC()
+model.load_state_dict(t.load("model_weights_hybrid.pth", map_location=t.device('cpu')))
+model.eval()
+
+# 3. Inference
+test_input = t.tensor([[-6.0, 70.0, 4.0, -196.0]], dtype=t.float32)
+with t.no_grad():
+    prediction = model(test_input)
+    print(f"Model prediction: {prediction.item():.4f}")
+```