Shopping Assistant Model

This model is trained to classify shopping deals into categories. It can be used to provide better recommendations to users based on their queries.

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

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}")