| | --- |
| | title: MiniEmbed Product Matcher |
| | emoji: "" |
| | colorFrom: blue |
| | colorTo: indigo |
| | pinned: false |
| | license: mit |
| | library_name: generic |
| | tags: |
| | - embeddings |
| | - product-matching |
| | --- |
| | |
| | # MiniEmbed: Product Matching Model |
| |
|
| | This model uses the same **MiniEmbed** architecture, trained **from scratch** exclusively for **high-accuracy product matching** (entity resolution). |
| |
|
| | It is designed to determine if two product listings—often with different titles, specifications, or formatting—refer to the **exact same physical item**. |
| |
|
| | ## Use Case |
| | **E-commerce Product Matching & Entity Resolution** |
| |
|
| | This model is trained to solve the "Same Product, Different Description" problem in e-commerce: |
| |
|
| | * **Marketplace Aggregation**: Unifying listings from Amazon, Walmart, and eBay into a single catalog. |
| | * **Competitor Analysis**: Matching your inventory against competitors to track pricing. |
| | * **Data Cleaning**: Removing duplicates in databases where titles vary slightly (e.g., "Nike Air Max" vs "Nike Men's Air Max Shoe"). |
| |
|
| | **Example Challenges Handled:** |
| | * **Variations**: "iPhone 14 128GB" vs "Apple iPhone 14 Midnight 128GB" |
| | * **Missing Attributes**: "Sony Headphones" vs "Sony WH-1000XM5 Noise Canceling Headphones" |
| | * **Formatting Differences**: "5-Pack T-Shirts" vs "T-Shirt (Pack of 5)" |
| |
|
| | ## Interactive Demo |
| |
|
| | This repository includes a **Streamlit** app to demonstrate the matching capability. |
| |
|
| | To run locally: |
| |
|
| | ```bash |
| | pip install -r requirements.txt |
| | streamlit run demo.py |
| | ``` |
| |
|
| | ## Model Architecture |
| |
|
| | * **Type**: Transformer Bi-Encoder (BERT-style) |
| | * **Parameters**: ~10.8M (Mini) |
| | * **Dimensions**: 256 |
| | * **Max Sequence Length**: 128 tokens |
| | * **Format**: `SafeTensors` (Hugging Face ready) |
| |
|
| | ## Usage |
| |
|
| | Since this is a custom model, you need to download the code and weights from the Hub: |
| |
|
| | ```python |
| | from huggingface_hub import snapshot_download |
| | import sys |
| | |
| | # 1. Download model (one-time) |
| | model_dir = snapshot_download("surazbhandari/miniembed-product") |
| | |
| | # 2. Add to path so we can import 'src' |
| | sys.path.insert(0, model_dir) |
| | |
| | # 3. Load Model |
| | from src.inference import EmbeddingInference |
| | model = EmbeddingInference.from_pretrained(model_dir) |
| | |
| | # Define two product titles |
| | product_a = "Sony WH-1000XM5 Wireless Noise Canceling Headphones, Black" |
| | product_b = "Sony WH1000XM5/B Headphones" |
| | |
| | # Calculate similarity (0 to 1) |
| | score = model.similarity(product_a, product_b) |
| | |
| | print(f"Similarity: {score:.4f}") |
| | ``` |
| |
|
| | ## Automated Sync |
| |
|
| | This repository is automatically synced to Hugging Face Spaces via GitHub Actions. |
| |
|
| |
|
| | MIT |
