Update README.md

README.md

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-# Product Price Predictor Weights
-
-This repository contains the weights and configuration for a Bi-LSTM model trained to predict product prices based on descriptions.
-
-## Model Architecture
-- Bi-directional LSTM model
-- Text vectorization layer with 10,000 max tokens
-- Embedding dimension: 128
-- Two Bi-LSTM layers with 64 units each
-
-## Files
-- model_weights.h5: The trained model weights
-- model_config.json: Model architecture configuration
-- vectorizer_config.json: Text vectorization configuration
+---
+license: mit
+datasets:
+- Recompense/amazon-appliances-lite-data
+language:
+- en
+tags:
+- finance
+
+
+---
+# Product Price Predictor Weights
+
+A Bi-LSTM model trained to predict e-commerce product prices from textual descriptions.
+
+---
+
+## Model Details
+
+- **Model type:** Bi-directional LSTM (Keras)  
+- **Task:** Regression (price prediction)  
+- **Input:** Product description (text)  
+- **Output:** Predicted price (USD)  
+
+---
+
+## Intended Use
+
+This model is designed to provide quick, approximate pricing for small-to-medium sized e-commerce catalogs where descriptions follow a consistent style (e.g., electronics or appliances). It **should not** be used:
+
+- For precise financial appraisal or high-stakes bidding.  
+- On descriptions with highly technical jargon the model wasn’t trained on.  
+
+---
+
+## Limitations
+
+- **Domain sensitivity:** Trained on the `Recompense/amazon-appliances-lite-data` dataset—performance may degrade on other product categories.  
+- **Short descriptions:** Very long or unstructured text may reduce accuracy.  
+- **Price range:** Only learns the range present in the training data (~\$10–\$500).  
+
+---
+
+## Training
+
+- **Dataset:** `Recompense/amazon-appliances-lite-data`  
+- **Preprocessing:**  
+  - Text vectorization (max 10 000 tokens)  
+  - Embedding dimension: 128  
+- **Architecture:**  
+  1. Embedding → Bi-LSTM(64) → Bi-LSTM(64) → Dense(1)  
+- **Optimizer:** Adam, learning rate 1e-3  
+- **Epochs:** 20, batch size 32  
+
+---
+
+## Evaluation
+
+- **Metric:** Root Mean Squared Logarithmic Error (RMSLE)  
+- **Formula (display mode):**
+
+$$
+RMSLE = \sqrt{ \frac{1}{n} \sum_{i=1}^{n} \bigl(\log(1 + \hat{y}_i) - \log(1 + y_i)\bigr)^2 }
+$$
+
+- **Test RMSLE:** 0.145 on held-out validation set  
+
+---
+
+## Files
+
+- **`model_weights.h5`** – Trained Keras weights  
+- **`model_config.json`** – Model architecture config  
+- **`vectorizer_config.json`** – Text vectorization config  
+
+---
+
+## Usage
+
+Below is an end-to-end example showing how to load the model from the Hugging Face Hub, set your preferred Keras backend, and run inference using the helper function:
+
+```python
+# 1) Install dependencies (if needed)
+#    pip install tensorflow jax keras huggingface_hub
+
+# 2) Choose your backend: "jax", "torch", or "tensorflow"
+import os
+os.environ["KERAS_BACKEND"] = "jax"  # or "torch", or "tensorflow"
+
+# 3) Load Keras and the model from the Hub
+from keras.saving import load_model
+
+model = load_model("hf://Recompense/product-pricer-bilstm")
+
+# 4) Define your inference function
+import tensorflow as tf
+
+def bilstm_pricer(item_text: str) -> int:
+    """
+    Predict the price of a product given its description.
+    
+    Args:
+        item_text (str): The full prompt text, including any prefix. 
+                         Only the description (after the first blank line) is used.
+    
+    Returns:
+        int: The rounded, non-negative predicted price in USD.
+    """
+    # Extract just the product description (assuming a prefix question)
+    try:
+        description = item_text.split('\n\n', 1)[1]
+    except IndexError:
+        description = item_text
+    
+    # Vectorize and batch the text
+    text_tensor = tf.convert_to_tensor([description])
+    
+    # Model prediction
+    pred = model.predict(text_tensor, verbose=0)[0][0]
+    
+    # Post-process: clamp and round
+    pred = max(0.0, pred)
+    return round(pred)
+
+# 5) Example inference
+prompt = (
+    "What is a fair price for the following appliance?\n\n"
+    "Stainless steel 12-cup programmable coffee maker with auto-shutoff"
+)
+
+predicted_price = bilstm_pricer(prompt)
+print(f"Predicted price: ${predicted_price}")
+```