---
library_name: transformers
tags: []
---

# MagicSupport Intent Classifier (BERT Fine-Tuned)

## Overview

This model is a fine-tuned `bert-base-uncased` model for multi-class intent classification in customer support environments.

It is optimized for:

* Fast inference
* High accuracy
* Low deployment cost
* Production-ready intent routing for support systems

The model is designed for the MagicSupport platform but is generalizable to structured customer support intent detection tasks.

---

## Model Details

* Base Model: `bert-base-uncased`
* Architecture: `BertForSequenceClassification`
* Task: Multi-class intent classification
* Number of Intents: 28
* Training Dataset: `bitext/Bitext-customer-support-llm-chatbot-training-dataset`
* Loss: CrossEntropy with class weights
* Framework: Hugging Face Transformers (PyTorch)

---

## Performance

### Validation Metrics (Epoch 5)

* Accuracy: **0.9983**
* F1 Micro: **0.9983**
* F1 Macro: **0.9983**
* Validation Loss: **0.0087**

The model demonstrates strong generalization and stable convergence across 5 epochs.

---

## Example Predictions

| Query                                 | Predicted Intent | Confidence |
| ------------------------------------- | ---------------- | ---------- |
| I want to cancel my order             | cancel_order     | 0.999      |
| How do I track my shipment            | delivery_options | 0.997      |
| I need a refund for my purchase       | get_refund       | 0.999      |
| I forgot my password                  | recover_password | 0.999      |
| I have a complaint about your service | complaint        | 0.996      |
| hello                                 | FALLBACK         | 0.999      |

The model also correctly identifies low-information inputs and maps them to a fallback intent.

---

## Intended Use

This model is intended for:

* Customer support intent classification
* Chatbot routing
* Support ticket categorization
* Voice-to-intent pipelines (after STT)
* Pre-routing before LLM or RAG systems

Typical production flow:

User Query → BERT Intent Classifier → Route to:

* Knowledge Base Retrieval
* Ticketing System
* Escalation to Human
* Fallback LLM

---

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer from HuggingFace Hub
model_name = "learn-abc/magicSupport-intent-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Prediction function
def predict_intent(text, confidence_threshold=0.75):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=-1)
        confidence, prediction = torch.max(probs, dim=-1)
    
    predicted_intent = model.config.id2label[prediction.item()]
    confidence_score = confidence.item()
    
    # Apply confidence threshold
    if confidence_score < confidence_threshold:
        predicted_intent = "FALLBACK"
    
    return {
        "intent": predicted_intent,
        "confidence": confidence_score
    }

# Example usage
queries = [
    "I want to cancel my order",
    "How do I track my package",
    "I need a refund",
    "hello there"
]

for query in queries:
    result = predict_intent(query)
    print(f"Query: {query}")
    print(f"Intent: {result['intent']}")
    print(f"Confidence: {result['confidence']:.3f}\n")
```

---

## Design Decisions

* BERT selected over larger LLMs for:

  * Low latency
  * Cost efficiency
  * Predictable inference
  * Edge deployability

* Class weighting applied to mitigate dataset imbalance.

* High confidence outputs indicate strong separation between intent classes.

---

## Known Limitations

* Designed for structured customer support queries.
* May struggle with:

  * Highly conversational multi-turn context
  * Extremely domain-specific enterprise terminology
  * Heavy slang or multilingual input
* Not trained for open-domain conversation.

---

## Future Improvements

* Add MagicSupport real production data for domain adaptation.
* Add hierarchical intent structure.
* Introduce confidence threshold calibration.
* Add OOD (Out-of-Distribution) detection.
* Quantized inference version for edge deployment.

---

## License

Specify your intended license here (e.g., MIT, Apache-2.0).

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## Citation

If using this model in research or production, please cite appropriately.

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## Model Card Author

For any inquiries or support, please reach out to:

* **Author:** [Abhishek Singh](https://github.com/SinghIsWriting/)
* **LinkedIn:** [My LinkedIn Profile](https://www.linkedin.com/in/abhishek-singh-bba2662a9)
* **Portfolio:** [Abhishek Singh Portfolio](https://portfolio-abhishek-singh-nine.vercel.app/)