|
|
--- |
|
|
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. |
