| --- |
| language: en |
| tags: |
| - embedding |
| - transformers |
| - search |
| - e-commerce |
| - conversational-search |
| - semantic-search |
| license: mit |
| pipeline_tag: feature-extraction |
| --- |
| |
| # VectorPath SearchMap: Conversational E-commerce Search Embedding Model |
|
|
| ## Model Description |
|
|
| SearchMap is a specialized embedding model designed to change search by making it more conversational and intuitive. We test out this hypothesis by creating a model suitable for ecommerce search. Fine-tuned on the Stella Embed 400M v5 base model, it excels at understanding natural language queries and matching them with relevant products. |
|
|
| ## Key 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](https://colab.research.google.com/drive/1wUQlWgL5R65orhw6MFChxitabqTKIGRu?usp=sharing)! |
|
|
| ## Model Details |
|
|
| - Base Model: Stella Embed 400M v5 |
| - Embedding Dimensions: Configurable (512, 768, 1024, 2048, 4096, 6144, 8192) |
| - Training Data: 100,000+ e-commerce products across 32 categories |
| - License: MIT |
| - Framework: PyTorch / Sentence Transformers |
|
|
| ## Usage |
|
|
| ### Using Sentence Transformers |
|
|
| ```python |
| # 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 |
|
|
| ```python |
| 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 |
| ) |
| ``` |
|
|
| ### 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" |
|
|
| ## Performance and Limitations |
|
|
| ### Evaluation |
| The model's evaluation metrics are available on the [MTEB Leaderboard](https://huggingface.co/spaces/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 it's memory size |
| - The model also is No 1. by a far margin on the [SemRel24STS](https://huggingface.co/datasets/SemRel/SemRel2024) 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 |
|
|
| ## Citation |
|
|
| If you use this model in your research, please cite: |
|
|
| ```bibtex |
| @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](https://discord.gg/gXvVfqGD) |
| - GitHub Issues: Report bugs and feature requests |
| - Interactive Demo: [Try it on Colab](https://colab.research.google.com/drive/1wUQlWgL5R65orhw6MFChxitabqTKIGRu?usp=sharing) |
|
|
| ## License |
|
|
| This model is released under the MIT License. See the LICENSE file for more details. |
