food_reranker4

Food Re-ranker Model

This is a fine-tuned BGE (BAAI General Embedding) model trained for binary classification of food description pairs. The model determines whether two food descriptions refer to the same item, enabling accurate re-ranking of search results.

Model Details

Model Description

• Base Model: BAAI/bge-base-en-v1.5
• Model Type: bert
• Task: Binary classification (food description matching)
• Output: Binary classification scores (0 = different foods, 1 = same food)

Architecture

The model uses the BGE architecture with the following specifications:

• Hidden Size: 768
• Number of Layers: 12
• Number of Attention Heads: 12
• Intermediate Size: 3072
• Maximum Position Embeddings: 512
• Vocabulary Size: 30522
• Hidden Act: gelu

Performance Metrics

Key evaluation metrics on the test set:

Use Case

Designed for improving food search accuracy by re-ranking initial search results, this model:

• Takes pairs of food descriptions as input
• Determines if they refer to the same food item
• Enables more accurate matching of food descriptions
• Helps surface the most relevant matches in search results

Training Configuration

• Batch Size: 32
• Learning Rate: 2e-05
• Number of Epochs: 10
• Warmup Steps: 0
• Weight Decay: 0.01
• Dropout: 0.1
• Attention Dropout: 0.1
• Layer Norm Eps: 1e-12

Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model
tokenizer = AutoTokenizer.from_pretrained("jonny9f/food_reranker2")
model = AutoModelForSequenceClassification.from_pretrained("jonny9f/food_reranker2")

# Prepare input
query = "chicken breast"
candidate = "grilled chicken breast"
inputs = tokenizer(query, candidate, padding=True, truncation=True, return_tensors="pt")

# Get prediction
outputs = model(**inputs)
score = outputs.logits.softmax(dim=1)[0][1].item()  # Score for positive class