Ataur77/ecommerce-customer-support

Model Card for Model ID

Model Details

Model Description

This model is designed to classify and respond to customer queries in an eCommerce domain. It processes and categorizes customer queries based on their intent, associated product department, and urgency level, which helps in building an intelligent, automated eCommerce assistant.

• Intent: The purpose or type of request in the customer’s query (e.g., Product Inquiry, Order Status, Return Request).
• Product Category: The product category that the query is related to (e.g., Electronics, Clothing, Home Appliances).
• Urgency Level: The priority of the query (e.g., Immediate, Soon, Medium).

The model is based on a T5 architecture fine-tuned on a dataset of eCommerce customer queries. This model is useful for building chatbots, virtual assistants, or automated systems that provide customers with efficient responses regarding products, orders, and other services.

• Developed by: [Ataur Rahman Likon]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: Text Classification
• Language(s) (NLP): English
• License: [More Information Needed]
• Finetuned from model [optional]: T5

Model Sources [optional]

• Repository: [Link to the model repository]
• Paper [optional]: [Link to the associated paper]
• Demo [optional]: [Link to demo]

Uses

Direct Use

This model can be directly used in customer service applications in the eCommerce industry to categorize queries, route them to the appropriate departments, and automate responses to customer queries such as Order Status, Product Availability, or Return Process.

Downstream Use [optional]

The model can be integrated into larger systems such as eCommerce platforms, chatbots, customer support software, or virtual assistants. It can also be further fine-tuned for specific eCommerce businesses to classify more specialized queries.

Out-of-Scope Use

The model is not designed for use in non-eCommerce contexts or any domain outside of customer support automation for online shopping platforms.

Bias, Risks, and Limitations

The model may struggle with ambiguous or complex queries that don't fall into one of the predefined categories. There may be challenges if a query contains information about a product or service not covered in the training data. Additionally, the model may be biased towards certain types of queries if the dataset used for training wasn't sufficiently diverse.

Recommendations

Users should ensure that their query categories are regularly updated to reflect new products, services, or issues. This can improve the model's accuracy and relevance for ongoing customer service tasks. Continuous monitoring and feedback will help in reducing the model's biases and improving its performance.

How to Get Started with the Model

To get started with the model, you can use the following code snippet:

from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load pre-trained model and tokenizer
model = T5ForConditionalGeneration.from_pretrained('path_to_model')
tokenizer = T5Tokenizer.from_pretrained('path_to_tokenizer')

# Example usage
input_text = "What is the status of my order for a laptop?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs['input_ids'])
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded_output)

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

The model achieved:

• Accuracy: 92% for intent classification

• Precision: 90% for product category classification

• Recall: 88% for urgency level classification

• F1-Score: 89% overall across all tasks

Summary

The model shows strong performance in classifying customer queries across multiple categories and levels of urgency. However, there is room for improvement in handling edge cases or complex queries not covered in the training data.

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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