Food Re-ranker Model

This is a fine-tuned DistilBERT 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: distilbert-base-uncased
Model Type: distilbert
Task: Binary classification (food description matching)
Output: Binary classification scores (0 = different foods, 1 = same food)

Architecture

Hidden Dimension: 768
Number of Layers: 6
Number of Attention Heads: 12
Intermediate Size: 3072
Maximum Position Embeddings: 512
Vocabulary Size: 30522

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: 5e-05
Number of Epochs: 3
Warmup Steps: 0
Weight Decay: 0.0
Dropout: 0.1
Attention Dropout: 0.1
Classification Dropout: 0.2

Example Usage

from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification

# Load model
tokenizer = DistilBertTokenizerFast.from_pretrained("jonny9f/food_reranker2")
model = DistilBertForSequenceClassification.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