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--- |
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language: en |
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tags: |
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- phi-2 |
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- customer-service |
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- transcript-analysis |
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- multi-issue |
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license: mit |
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--- |
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# Phi-2 Multi-Issue Transcript Analysis Model |
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This model is based on Microsoft's Phi-2 for analyzing customer service transcripts with multiple issues. It can: |
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1. Identify primary and secondary issues |
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2. Analyze customer sentiment |
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3. Rate agent performance |
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4. Track resolution status |
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5. Predict CSAT scores |
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6. Extract key actions and outcomes |
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## Model Details |
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- **Base Model**: microsoft/phi-2 |
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- **Task**: Multi-issue customer service transcript analysis |
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- **Training Data**: Customer service transcripts with multiple issues |
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- **Output Format**: Structured JSON with detailed analysis |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained("chendren/phi2-multi-issue-analysis") |
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tokenizer = AutoTokenizer.from_pretrained("chendren/phi2-multi-issue-analysis") |
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# Prepare input |
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transcript = """[Your customer service transcript here]""" |
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# Generate analysis |
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inputs = tokenizer(transcript, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=512) |
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analysis = tokenizer.decode(outputs[0]) |
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``` |
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## Example Output |
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```json |
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{ |
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"primary_issue": "Internet connection drops", |
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"secondary_issues": [ |
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"Signal interference", |
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"Router firmware outdated" |
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], |
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"customer_sentiment": "negative", |
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"agent_performance": { |
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"rating": 4, |
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"justification": "Agent was helpful and provided clear instructions" |
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}, |
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"resolution_status": "resolved", |
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"follow_up_needed": false, |
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"key_points": [ |
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"Customer experienced internet drops", |
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"Agent guided through troubleshooting", |
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"Issue resolved with firmware update" |
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], |
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"issues": [ |
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"Intermittent connection drops", |
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"WiFi interference", |
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"Outdated firmware" |
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], |
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"actions": [ |
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"Diagnosed signal fluctuations", |
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"Updated router firmware", |
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"Provided monitoring instructions" |
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], |
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"outcomes": [ |
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"Connection stability improved", |
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"Firmware updated successfully" |
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], |
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"predicted_csat": 4 |
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} |
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``` |
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## Limitations |
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- Designed specifically for customer service transcripts |
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- Best performance with clear dialogue format |
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- May require adjustment for different transcript formats |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{phi2-multi-issue-analysis, |
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author = {args.username}, |
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title = {Phi-2 Multi-Issue Transcript Analysis Model}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Model Hub}, |
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howpublished = {https://huggingface.co/chendren/phi2-multi-issue-analysis} |
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} |
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``` |
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