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ArthurCisotto/
recipe-search-sbert

Name: recipe-search-sbert
Availability: InStock

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Recipe Search SBERT

This model combines SBERT embeddings with a denoising autoencoder to create recipe embeddings optimized for semantic search in Portuguese recipes.

Model Description

The model processes recipe texts into embeddings using a two-stage approach:

SBERT (distiluse-base-multilingual-cased-v2) generates initial 768-dimensional embeddings
A denoising autoencoder reduces dimensionality to 128 while preserving recipe relationships

Intended Use

Portuguese recipe semantic search
Recipe similarity analysis
Ingredient-based recipe recommendations

Training Data

The model trains on 15,000 recipes from TudoGostoso. Each recipe contains:

Title
Ingredient list
Cooking instructions
URL reference

Training Procedure

SBERT generates embeddings from concatenated recipe texts
Mean pooling creates single vector per recipe
Denoising autoencoder trains for 100 epochs with:
- Adam optimizer (lr=1e-4)
- MSE loss
- Noise factor=0.1
- Batch size=32

Evaluation Results

Embedding space evaluation via TSNE visualization shows:

Clear clustering of similar recipes
Sensible transitions between recipe types
Preserved relationships after dimensionality reduction

Example Usage

python

from transformers import AutoTokenizer
from recipe_search_sbert import RecipeSearchEngine

# Initialize model
search_engine = RecipeSearchEngine()

# Search recipes
results = search_engine.search("frango assado")

Limitations

Portuguese recipes only
Single-cuisine dataset bias
No support for cross-lingual search

ArthurCisotto/recipe-search-sbert