hemanjan/customer-callcenter-transformer

📞 Franchise CallCenter Transformer Model

🧠 Overview

This model is a fine-tuned Transformer designed to classify customer support conversations into different intent or topic categories.
It was developed as part of the CallCenter AI project, which automates analysis and categorization of incoming customer calls.

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🧩 Model Details

• Developed by: Hemanjan Reddy Pundla & Lalith kumar Gali
• Model Type: Text Classification
• Base Model: distilbert-base-uncased
• Language(s): English
• Framework: 🤗 Transformers
• License: Apache 2.0
• Fine-tuned on: Custom dataset of call transcripts labeled by topic/intent

🏷️ Classification Labels

The model classifies franchise call center conversations into Multiple intent categories:

• Service Types: Appliance Repair, Plumbing, Electrical, Painting, HVAC & Dryer Vent
• Call Intents: Scheduling, Billing Inquiry, Complaint, Cancellation, General Inquiry

These labels are designed for home services franchise call centers, enabling automatic routing, analytics, and customer support automation.

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💡 Use Cases

Use Case	How It Helps
Detecting calls	Detect intent → route to correct brand context based on intent
Real-time tagging	Tag live calls with service type
Analytics	Categorize historical calls for reporting
Customer persona	Understand what services a customer typically calls about

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🧰 Intended Uses

✅ Direct Use

• Predicting the intent or topic group of a customer message in a call center.
• Supporting automated routing of customer requests.
• Enhancing analytics for customer support interactions.

⚙️ Downstream Use

Can be integrated in:

• Chatbots or virtual assistants
• Customer feedback analysis tools
• Call center automation dashboards

🚫 Out-of-Scope Use

• Do not use this model for sentiment analysis or emotion detection — it is trained for intent classification only.
• Not suitable for multilingual text unless fine-tuned further.

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⚖️ Bias, Risks, and Limitations

• The dataset is domain-specific (call center context), so performance might degrade outside that domain.
• Potential bias from class imbalance — ensure to retrain or calibrate if used on new data.
• English-only model.

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⚠️ Note: This model is currently based on DistilBERT and configured for franchise call center classification. Fine-tuning on domain-specific data is in progress.

🚀 Getting Started

You can load the model in Python with:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "hemanjan/customer-callcenter-transformer"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "I want to book my house painting and lawn moving service"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logit