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.

🧩 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

🧰 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.

⚖️ 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.

🚀 Getting Started

You can load the model in Python with:

python

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