---
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')
```