metadata
language:
- en
base_model:
- scikit-learn/skorch-text-classification
tags:
- description
- python
- scikit-learn
library_name: sklearn
datasets:
- jacob-hugging-face/job-descriptions
- Data Collection Existing Companies: Gather a dataset of existing companies and their job descriptions. This could include various roles and responsibilities. New Companies: Create a mechanism to identify when a new company is mentioned (not in your existing dataset) and prompt the user for a description.
- Prompt Structure For Existing Companies: Input: “What is the job description for [Company Name]?” Output: Return a predefined job description for that company. For New Companies: Input: “What is the job description for [New Company]?” Output: "I don't have a description for [New Company]. Could you please provide a brief description or key details about the company?" Store the user-provided description for future reference.
- User Interaction Design a user-friendly interface where users can input company names and descriptions easily. Ensure that when a user provides a description for a new company, it's validated and stored properly for future queries.
- Learning Mechanism Implement a feedback loop where: The model refines its understanding of job descriptions based on user input. If multiple users provide descriptions for the same new company, you can aggregate this data to improve accuracy.
- Example Workflow User Input: User enters “Tech Innovations Inc.” Model Check: If “Tech Innovations Inc.” exists in the dataset, return its job description. If not, prompt the user: "I don't have a description for Tech Innovations Inc. Can you provide one?" User Response: User provides a description. Store Description: Save the description in the dataset for future queries.