Create README.md

README.md

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+---
+base_model:
+- distilbert/distilbert-base-uncased
+---
+# DistilBERT for Intent Classification
+
+## Overview
+- **Architecture:** DistilBERT (distilbert-base-uncased) for sequence classification
+- **Task:** Single-label intent classification of HR queries using merged user query and context
+- **Dataset:** ~133 samples, 12 intent classes, 80/20 train/validation split
+
+## Training Details
+- **Epochs:** 5
+- **Batch Size:** 8
+- **Learning Rate:** 5e-5
+- **Optimizer:** AdamW
+- **Loss:** CrossEntropyLoss
+
+## Evaluation Metrics (Validation Set)
+| Metric     | Value    |
+|------------|----------|
+| Accuracy   | 88.89%   |
+| Precision  | 100%     |
+| Recall     | 88.89%   |
+| Loss       | 1.4586   |
+
+## Usage Example
+text = "Share offer with Santhosh [context: {domain: HR, topic: onboarding, subject: offer letter}]"
+inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+with torch.no_grad():
+logits = model(**inputs).logits
+pred_id = logits.argmax(dim=1).item()
+
+## Comments
+- Consistent strong results on validation set.
+- Model is robust for HR chatbot/automation intent tasks.
+- Consider more data or further tuning for additional improvement.
+
+---
+
+*For best results, ensure your production inference pipeline preprocesses and tokenizes input exactly as done for the training data.*
+
+---
+
+**In summary:**  
+You’ve followed the right steps for distilbert-based intent classification and your documentation—combined with this detailed evaluation/usage section—will be clear and informative for anyone using your model!