learn-abc
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magicSupport-intent-classifier

@@ -3,197 +3,193 @@ library_name: transformers
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 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 = "your-username/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).
+---
+## Citation
+If using this model in research or production, please cite appropriately.
+---
+## 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/)