callcenter-transformer

📞 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: Karimen Ben Romdhane & Tasnim Meriah
• 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

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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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🚀 Getting Started

You can load the model in Python with:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

text = "I want to cancel my subscription"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logit