Shopping Assistant

🚀 Shopping Assistant Model

This model is designed to classify shopping deals into various categories and offer better user recommendations based on their queries.

🚀 Quick Start

This Shopping Assistant Model can classify shopping deals and provide user recommendations. Here is a basic usage example:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("selvaonline/shopping-assistant")
model = AutoModelForSequenceClassification.from_pretrained("selvaonline/shopping-assistant")

# Prepare the input
text = "I'm looking for headphones"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

# Get the model prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.sigmoid(outputs.logits)

# Load the categories
categories = ["electronics", "clothing", "home", "kitchen", "toys", "other"]

# Get the top categories
top_categories = []
for i, score in enumerate(predictions[0]):
    if score > 0.5:  # Threshold for multi-label classification
        top_categories.append((categories[i], score.item()))

# Sort by score
top_categories.sort(key=lambda x: x[1], reverse=True)

# Print the results
for category, score in top_categories:
    print(f"{category}: {score:.4f}")

✨ Features

• Classify shopping deals into categories such as electronics, clothing, home, kitchen, toys, and other.
• Provide better user recommendations based on their queries.

📚 Documentation

Model description

The model is based on DistilBERT and is fine-tuned on a dataset of shopping deals. It is trained to classify deals into the following categories: electronics, clothing, home, kitchen, toys, other.

Intended uses & limitations

This model is intended to be used for classifying shopping deals and providing recommendations to users. It is not intended to be used for any other purpose.

Training data

The model is trained on a dataset of shopping deals from DealsFinders.com. The dataset is automatically labeled based on keywords in the deal titles and descriptions.

Training procedure

The model is trained using the Hugging Face Transformers library. It is fine-tuned on the deals dataset using a multi-label classification approach.

Evaluation results

The model is evaluated on a held-out validation set. The evaluation metrics include accuracy, precision, recall, and F1 score.

Usage

The usage example has been provided in the "Quick Start" section.

🔧 Technical Details

• Model Type: Based on DistilBERT, fine - tuned for multi - label classification.
• Training Data: Shopping deals from DealsFinders.com, automatically labeled by keywords.
• Training Library: Hugging Face Transformers library.
• Evaluation Metrics: Accuracy, precision, recall, and F1 score.

📄 License

This project is licensed under the MIT license.