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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
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