| 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 | |