Chiraag-P-V/E-commerce-customer-query-classifier

🛍️ E-commerce Customer Support Classification

Model Description

This model is a fine-tuned version of distilbert-base-uncased for classifying e-commerce customer complaints and queries into multiple categories.
It was trained on the Ataur77/ecommerce-customer-support dataset and can identify a wide range of e-commerce support issues.

The goal is to help customer service teams automatically route tickets to the correct department for faster resolution.

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Categories

The model can classify queries into the following 15 categories:

Label ID	Category
0	Account & Shipping
1	Promotions
2	Payments
3	Order Tracking
4	Technical Issues
5	Product Issues
6	Refunds
7	Order Issues
8	Subscriptions
9	Checkout
10	Inventory
11	Product Quality
12	Returns
13	Account
14	Shipping
15	Special Requests

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Intended Uses

• Customer Support Automation: Automatically route customer support tickets.
• CRM Integration: Enhance ticket management systems.
• Analytics: Categorize historical support data for insights.

Limitations

• Works best with English-language customer queries.
• Performance depends on dataset quality — the training dataset is small, so real-world accuracy may vary.

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Quick Usage

from transformers import pipeline

# Load the model from Hugging Face
classifier = pipeline(
    "text-classification",
    model="Chiraag-P-V/E-commerce-customer-query-classifier"
)

# Example query
query = "I have not received my delivery yet."
result = classifier(query)

print(result)