Upload intent classifier v2 - Accuracy: 100.00%

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- text-classification
+- intent-classification
+- conversational-ai
+- bert
+- distilbert
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+model-index:
+- name: intent-classifier-v2
+  results:
+  - task:
+      type: text-classification
+      name: Intent Classification
+    metrics:
+    - type: accuracy
+      value: 1.0000
+      name: Test Accuracy
+    - type: f1
+      value: 1.0000
+      name: Weighted F1
+---
+
+# DAPA Intent Classifier v2
+
+## Model Description
+
+This model classifies user intents for the DAPA AI conversational assistant system. It supports 13 different intents including 12 agentic workflows and 1 general Q&A fallback.
+
+**Model Type:** DistilBERT for Sequence Classification  
+**Training Date:** 2025-10-25  
+**Accuracy:** 100.00%  
+**F1 Score:** 1.0000
+
+## Supported Intents
+
+The model classifies queries into 13 intents:
+
+### Agentic Intents (12)
+1. **generate-offer** - Generate job offers, NDAs, contracts
+2. **schedule-interview** - Schedule candidate interviews
+3. **update-employee-profile** - Update employee information
+4. **access-employee-record** - Access employee records
+5. **approve-expense** - Approve expense reports
+6. **check-leave-balance** - Check leave balances
+7. **confirm-training-completion** - Confirm training completion
+8. **provide-candidate-feedback** - Provide candidate feedback
+9. **request-leave** - Request time off
+10. **request-training** - Request training enrollment
+11. **review-contract** - Review contracts
+12. **submit-expense** - Submit expense reports
+
+### Q&A Intent (1)
+13. **general-query** - Generic queries (email lookups, status checks, policy questions)
+
+## Training Data
+
+- **Total Samples:** 1,240
+- **Training Split:** 70% (868 samples)
+- **Validation Split:** 15% (186 samples)
+- **Test Split:** 15% (186 samples)
+- **Data Balance:** 80-200 examples per intent
+
+## Performance
+
+### Overall Metrics
+- **Test Accuracy:** 100.00%
+- **Weighted Precision:** 1.0000
+- **Weighted Recall:** 1.0000
+- **Weighted F1:** 1.0000
+
+### Per-Intent Performance
+
+| Intent | Precision | Recall | F1 | Support |
+|--------|-----------|--------|-----|---------|
+| access-employee-record | 1.000 | 1.000 | 1.000 | 12 |\n| approve-expense | 1.000 | 1.000 | 1.000 | 12 |\n| check-leave-balance | 1.000 | 1.000 | 1.000 | 12 |\n| confirm-training-completion | 1.000 | 1.000 | 1.000 | 12 |\n| general-query | 1.000 | 1.000 | 1.000 | 30 |\n| generate-offer | 1.000 | 1.000 | 1.000 | 18 |\n| provide-candidate-feedback | 1.000 | 1.000 | 1.000 | 12 |\n| request-leave | 1.000 | 1.000 | 1.000 | 12 |\n| request-training | 1.000 | 1.000 | 1.000 | 12 |\n| review-contract | 1.000 | 1.000 | 1.000 | 12 |\n| schedule-interview | 1.000 | 1.000 | 1.000 | 15 |\n| submit-expense | 1.000 | 1.000 | 1.000 | 12 |\n| update-employee-profile | 1.000 | 1.000 | 1.000 | 15 |\n
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("SantmanKT/intent-classifier-v2")
+model = AutoModelForSequenceClassification.from_pretrained("SantmanKT/intent-classifier-v2")
+
+# Predict intent
+query = "send offer to John"
+context = "{domain: hr}"
+input_text = f"{query} [context: {context}]"
+
+inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=128)
+outputs = model(**inputs)
+probs = torch.softmax(outputs.logits, dim=-1)
+confidence, predicted_idx = torch.max(probs, dim=-1)
+
+intent = model.config.id2label[predicted_idx.item()]
+print(f"Intent: {intent}, Confidence: {confidence.item():.2%}")
+```
+
+## Routing Logic
+
+- **High Confidence (≥70%) + Agentic Intent** → Route to Domain Service
+- **Low Confidence (<70%) OR general-query** → Route to Q&A Service
+
+## Model Details
+
+- **Base Model:** distilbert-base-uncased
+- **Max Sequence Length:** 128 tokens
+- **Training Epochs:** 5
+- **Batch Size:** 16
+- **Learning Rate:** 2e-5
+- **Framework:** HuggingFace Transformers
+
+## Limitations
+
+- Optimized for English language only
+- Requires context formatting: `[context: {...}]`
+- Performance may degrade on queries significantly different from training data
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{dapa-intent-classifier-v2,
+  author = {SantmanKT},
+  title = {DAPA Intent Classifier v2},
+  year = {2025},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/SantmanKT/intent-classifier-v2}
+}
+```
+
+## License
+
+Apache 2.0

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