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Ramesh10/medical-emails-noncase-dataset

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… See the full description on the dataset page: https://huggingface.co/datasets/Ramesh10/medical-emails-noncase-dataset.

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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. 1.Login or Access Issues - System access problems, password resets, authentication failures
  2. 2.Application Support Requests - Technical issues with software tools and systems
  3. 3.Training or Onboarding Inquiries - Training sessions, onboarding materials, competency assessments
  4. 4.Scheduling or Operational Coordination - Meeting rescheduling, room bookings, deadline extensions
  5. 5.User Management or Account Setup - Account creation, deactivation, permission changes
  6. 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. 1.Each email body contains sufficient narrative and contextual detail
  2. 2.No compressed keyword-style statements
  3. 3.No simple concatenations of one sentence per label
  4. 4.Realistic contextual detail with timing and context
  5. 5.Paragraph-style composition preferred over telegraphic fragments
  6. 6.All topics woven naturally into the message with realistic transitions
  7. 7.Emails feel like coherent human-written messages
  8. 8.No escape characters in the dataset
  9. 9.Strict numeric normalization (e.g., "3 weeks" not "three weeks")

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Generated by ML Intern

This dataset repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

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  • —Source code: https://github.com/huggingface/ml-intern

Usage

python
from datasets import load_dataset

dataset = load_dataset('Ramesh10/medical-emails-noncase-dataset')