Searchmap Preview

🚀 VectorPath SearchMap: Conversational E-commerce Search Embedding Model

VectorPath SearchMap is a specialized embedding model tailored for e-commerce search. It makes search more conversational and intuitive, enabling users to find relevant products with natural language queries.

✨ Features

• Optimized for conversational e-commerce queries
• Handles complex, natural language search intents
• Supports multi-attribute product search
• Efficient 1024-dimensional embeddings (configurable up to 8192)
• Specialized for product and hotel search scenarios

🚀 Quick Start

Try out the model in our interactive Colab Demo!

📦 Installation

Using Sentence Transformers

# Install required packages
!pip install -U torch==2.5.1 transformers==4.44.2 sentence-transformers==2.7.0 xformers==0.0.28.post3

from sentence_transformers import SentenceTransformer

# Initialize the model
model = SentenceTransformer('vectopath/SearchMap_Preview', trust_remote_code=True)

# Encode queries
query = "A treat my dog and I can eat together"
query_embedding = model.encode(query)

# Encode products
product_description = "Organic peanut butter dog treats, safe for human consumption..."
product_embedding = model.encode(product_description)

Using with FAISS for Vector Search

import numpy as np
import faiss

# Create FAISS index
embedding_dimension = 1024  # or your chosen dimension
index = faiss.IndexFlatL2(embedding_dimension)

# Add product embeddings
product_embeddings = model.encode(product_descriptions, show_progress_bar=True)
index.add(np.array(product_embeddings).astype('float32'))

# Search
query_embedding = model.encode([query])
distances, indices = index.search(
    np.array(query_embedding).astype('float32'), 
    k=10
)

💻 Usage Examples

Basic Usage

# Encode a single query
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('vectopath/SearchMap_Preview', trust_remote_code=True)
query = "A treat my dog and I can eat together"
query_embedding = model.encode(query)

Advanced Usage

# Use with FAISS for vector search
import numpy as np
import faiss
embedding_dimension = 1024
index = faiss.IndexFlatL2(embedding_dimension)
product_embeddings = model.encode(product_descriptions, show_progress_bar=True)
index.add(np.array(product_embeddings).astype('float32'))
query_embedding = model.encode([query])
distances, indices = index.search(
    np.array(query_embedding).astype('float32'), 
    k=10
)

Example Search Queries

The model excels at understanding natural language queries like:

• "A treat my dog and I can eat together"
• "Lightweight waterproof hiking backpack for summer trails"
• "Eco-friendly kitchen gadgets for a small apartment"
• "Comfortable shoes for standing all day at work"
• "Cereal for my 4 year old son that likes to miss breakfast"

📚 Documentation

Model Details

Performance and Limitations

Evaluation

The model's evaluation metrics are available on the MTEB Leaderboard.

• The model is currently by far the best embedding model under 1B parameters size and very easy to run locally on a small GPU due to its memory size.
• The model also is No. 1 by a far margin on the SemRel24STS task with an accuracy of 81.12% beating Google Gemini embedding model (second place) 73.14% (as at 30th March 2025). SemRel24STS evaluates the ability of systems to measure the semantic relatedness between two sentences over 14 different languages.
• We noticed the model does exceptionally well with legal and news retrieval and similarity task from the MTEB leaderboard.

Strengths

• Excellent at understanding conversational and natural language queries.
• Strong performance in e-commerce and hotel search scenarios.
• Handles complex multi-attribute queries.
• Efficient computation with configurable embedding dimensions.

Current Limitations

• May not fully prioritize weighted terms in queries.
• Limited handling of slang and colloquial language.
• Regional language variations might need fine-tuning.

Training Details

The model was trained using:

• Supervised learning with Sentence Transformers.
• 100,000+ product dataset across 32 categories.
• AI-generated conversational search queries.
• Positive and negative product examples for contrast learning.

Intended Use

This model is designed for:

• E-commerce product search and recommendations.
• Hotel and accommodation search.
• Product catalog vectorization.
• Semantic similarity matching.
• Query understanding and intent detection.

🔧 Technical Details

The model is fine-tuned on the Stella Embed 400M v5 base model. It uses supervised learning with Sentence Transformers and a large e-commerce product dataset for training. The configurable embedding dimensions allow for efficient computation in different scenarios.

📄 License

This model is released under the MIT License. See the LICENSE file for more details.

📚 Citation

If you use this model in your research, please cite:

@misc{vectorpath2025searchmap,
  title={SearchMap: Conversational E-commerce Search Embedding Model},
  author={VectorPath Research Team},
  year={2025},
  publisher={Hugging Face},
  journal={HuggingFace Model Hub},
}

🤝 Contact and Community

• Discord Community: Join our Discord
• GitHub Issues: Report bugs and feature requests
• Interactive Demo: Try it on Colab