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---
license: mit
tags:
- product-search
- semantic-search
- bert
- pytorch
- information-retrieval
---
# Semantic Product Search Model
This model performs semantic product search using BERT embeddings and a dual-encoder neural network architecture.
## Model Architecture
- **Base Model**: BERT-base-uncased for text embeddings
- **Encoder**: Dual-encoder architecture with separate query and product encoders
- **Similarity Network**: Multi-layer perceptron for relevance scoring
- **Input Dimension**: 768 (BERT embedding size)
- **Hidden Dimensions**: [512, 256, 128]
- **Dropout**: 0.3
## Usage
See the `load_and_run_frontend.py` script for loading and using this model.
## Files
- `pytorch_model.bin`: Model weights
- `config.json`: Model configuration
- `tokenizer files`: BERT tokenizer files
- `product_catalog.parquet`: Product catalog for search
- `product_embeddings.npy`: Precomputed product embeddings (optional)
## Performance
Trained on Amazon Shopping Queries Dataset with the following metrics:
- NDCG@10: ~0.54
- MAP: ~0.54
- Precision@10: ~0.50
- Recall@10: ~0.54
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