sidde/distilbert-nlu-intent-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

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