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---
language:
- en
base_model:
- scikit-learn/skorch-text-classification
tags:
- description
- python
- scikit-learn
library_name: sklearn
datasets:
- jacob-hugging-face/job-descriptions
---
1. 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.
2. 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.
3. 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.
4. 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.
5. 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. |