---
language: en
license: apache-2.0
tags:
  - text-classification
  - intent-detection
  - distilbert
  - nlu
datasets:
  - custom
metrics:
  - accuracy
  - f1
pipeline_tag: text-classification
---

# DistilBERT NLU Intent Classification

Fine-tuned DistilBERT model for intent classification in Natural Language Understanding (NLU) systems.

## Model Details

- **Base Model:** distilbert-base-uncased
- **Task:** Intent Classification (Sequence Classification)
- **Number of Labels:** 8
- **Framework:** PyTorch + Transformers

## Supported Intents

| ID | Intent | Description |
|----|--------|-------------|
| 0 | BILLING_ISSUE | Problems with bills or charges |
| 1 | CANCEL_SUBSCRIPTION | Cancel service requests |
| 2 | CHECK_BALANCE | Balance inquiry |
| 3 | GOODBYE | Farewell messages |
| 4 | GREETING | Hello/welcome messages |
| 5 | MODIFY_PROFILE | Update account details |
| 6 | ROAMING_ACTIVATION | Enable roaming |
| 7 | ROAMING_DEACTIVATION | Disable roaming |

## Usage

    from transformers import pipeline

    classifier = pipeline("text-classification", model="sidde/distilbert-nlu-intent-classification")

    # Single prediction
    result = classifier("I want to check my balance")
    print(result)
    # [{"label": "CHECK_BALANCE", "score": 0.98}]

## Training Details

- **Dataset:** 772 examples (custom intent dataset)
- **Train/Eval Split:** 80/20 with stratification
- **Epochs:** 10
- **Batch Size:** 16
- **Learning Rate:** 2e-5
- **Hardware:** NVIDIA L4 GPU on OpenShift AI

## Deployment

This model is deployed on OpenShift AI using KServe.

## License

Apache 2.0