Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,67 @@
+---
+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