| --- |
| tags: |
| - classification |
| - emails |
| - non-case |
| - operational |
| - pharmacovigilance |
| - jsonl |
| - ml-intern |
| dataset_info: |
| features: |
| - name: instruction |
| dtype: string |
| - name: output |
| dtype: string |
| config_name: default |
| --- |
| |
| # Medical Email Classification Dataset - Non-Case Category |
|
|
| This dataset contains **200 unique synthetic emails** classified as **Non-Case** for pharmaceutical/pharmacovigilance email classification tasks. |
|
|
| ## Classification Category |
|
|
| ### Non-Case |
| A Non-Case is an email that is purely operational, administrative, internal or logistical in nature. It does not involve any drug, medication, medical product, patient, health-related event, safety, quality, or commercial evaluation. |
|
|
| ## Sub-Types (6 Types) |
|
|
| 1. **Login or Access Issues** - System access problems, password resets, authentication failures |
| 2. **Application Support Requests** - Technical issues with software tools and systems |
| 3. **Training or Onboarding Inquiries** - Training sessions, onboarding materials, competency assessments |
| 4. **Scheduling or Operational Coordination** - Meeting rescheduling, room bookings, deadline extensions |
| 5. **User Management or Account Setup** - Account creation, deactivation, permission changes |
| 6. **Workflow or Process Clarification** - Process queries, SLA questions, procedure confirmations |
|
|
| ## Dataset Format |
|
|
| Each example is in strict JSONL format: |
|
|
| ```json |
| { |
| "instruction": "SUBJECT: ...\nBODY:\n...", |
| "output": "{\"Classification_of_request\": {\"Classification\": \"Non-Case\", \"Confidence_percentage\": \"95%\"}, \"Analysis\": \"...\"}" |
| } |
| ``` |
|
|
| ## Dataset Statistics |
|
|
| - **Total emails**: 200 |
| - **Number of types**: 6 (distributed across all emails) |
|
|
| ## Quality Requirements Met |
|
|
| 1. Each email body contains sufficient narrative and contextual detail |
| 2. No compressed keyword-style statements |
| 3. No simple concatenations of one sentence per label |
| 4. Realistic contextual detail with timing and context |
| 5. Paragraph-style composition preferred over telegraphic fragments |
| 6. All topics woven naturally into the message with realistic transitions |
| 7. Emails feel like coherent human-written messages |
| 8. No escape characters in the dataset |
| 9. Strict numeric normalization (e.g., "3 weeks" not "three weeks") |
|
|
| <!-- ml-intern-provenance --> |
| ## Generated by ML Intern |
|
|
| This dataset repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. |
|
|
| - Try ML Intern: https://smolagents-ml-intern.hf.space |
| - Source code: https://github.com/huggingface/ml-intern |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset('Ramesh10/medical-emails-noncase-dataset') |
| ``` |
|
|
