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Recompense/
product-pricer-bilstm

Name: product-pricer-bilstm
Availability: InStock

keras

finance

Recompense/amazon-appliances-lite-data

mit

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Amazon Appliances Product Price Predictor

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: 0.51 on held-out validation set hit 90.4% on validation set with margin of $40

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}")