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