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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 |