| --- |
| 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. |
|
|
