README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: train.json
      - split: test
        path: test.json
dataset_info:
  features:
    - name: recruitment_date
      dtype: string
    - name: opening_position
      dtype: string
    - name: description
      dtype: string
    - name: requirement
      dtype: string
    - name: keywords_for_recruitment
      sequence: string
    - name: cv
      dtype: string
    - name: experience_years
      dtype: int64
    - name: keywords_from_applicants
      sequence: string
    - name: result
      dtype: string
    - name: reason_for_result
      dtype: string

Fictional IT Recruitment Dataset

This dataset contains 10,000 synthetically generated recruitment records for a fictional IT company from 2020 to 2026. It is designed to be used for Information Retrieval and Classification tasks, such as predicting candidate acceptance based on their CV embeddings.

Dataset Structure

Each record contains the following fields:

• recruitment_date: Date the recruitment took place (YYYY-MM-DD).
• opening_position: The title of the job position (e.g., Software Engineer, Data Scientist).
• description: A brief description of the job role.
• requirement: The minimum requirements for the position.
• keywords_for_recruitment: A list of required skills/keywords.
• cv: A synthetic candidate's curriculum vitae (CV) summary text.
• experience_years: The number of years of experience the candidate has.
• keywords_from_applicants: A list of skills/keywords found in the candidate's CV.
• result: The final recruitment decision ("Accepted" or "Rejected").
• reason_for_result: A brief explanation of why the candidate was accepted or rejected.

Potential Use Cases

• Semantic Search: Finding candidates whose CVs closely match the job description.
• Text Classification: Training models to predict the result given the cv text.

Generation

The data was generated using a custom Python script that pairs job requirements with randomized candidate profiles, introducing variations in experience and skill overlap.