automated_tech_support_ticketing_model

DistilBERT for Automated Tech-Support Classification

This model is a fine-tuned version of DistilBERT (distilbert-base-uncased) trained to classify customer support tickets into 27 specific intents across 11 major categories.

This model is the "Brain" of the Automated Tech-Support Ticketing System project.

🚀 Model Details

• Architecture: DistilBERT (Transformers)
• Task: Multi-class Text Classification
• Intents: 27 (e.g., cancel_order, recover_password, edit_account, etc.)
• Framework: PyTorch & Hugging Face Transformers

📊 Performance (Week 2 Results)

The model achieved near-perfect scores on the Bitext Customer Support Dataset:

• Training Accuracy: 100.00%
• Validation Accuracy: 99.76%
• Macro Average F1-Score: 1.00

📂 Artifacts in this Repo

• best_model_state.bin: The trained PyTorch model weights.
• tokenizer/: Full configuration for the BERT tokenizer.
• label_encoder.joblib: The mapping for the 27 intent classes.

🛠️ Integration with Project

This model is designed to be used in conjunction with a FastAPI backend and a Gemini 2.5-flash reasoning layer.

To use this model in your local setup, you can clone this repository or use the huggingface_hub library to download the artifacts into the models/ directory of the main project.

How to Load (Example):

from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
import torch

# Path to the downloaded model
model = DistilBertForSequenceClassification.from_pretrained("./models/tokenizer", num_labels=27)
model.load_state_dict(torch.load("./models/best_model_state.bin"))

🔗 Main Project Repository

For the full end-to-end implementation (FastAPI, Streamlit, and LLM Integration), please visit my GitHub: 👉 GitHub Repository

Developed by Baltasar Patrizhard Djata Part of the "Automated Tech-Support Ticketing System" Portfolio Project (2026).