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--- |
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dataset_info: |
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features: |
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- name: Position |
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dtype: string |
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- name: Long Description |
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dtype: string |
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- name: Company Name |
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dtype: string |
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- name: Exp Years |
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dtype: string |
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- name: Primary Keyword |
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dtype: string |
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- name: English Level |
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dtype: string |
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- name: Published |
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dtype: string |
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- name: Long Description_lang |
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dtype: string |
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- name: id |
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dtype: string |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 281256096 |
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num_examples: 141897 |
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download_size: 145859589 |
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dataset_size: 281256096 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: mit |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Djinni Dataset (English Job Descriptions part) |
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## Overview |
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The [Djinni Recruitment Dataset](https://github.com/Stereotypes-in-LLMs/recruitment-dataset) (English Job Descriptions part) contains 150,000 job descriptions and 230,000 anonymized candidate CVs, posted between 2020-2023 on the [Djinni](https://djinni.co/) IT job platform. The dataset includes samples in English and Ukrainian. |
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The dataset contains various attributes related to job descriptions, including position titles, job descriptions, company names, experience requirements, keywords, English proficiency levels, publication dates, language of job descriptions, and unique identifiers. |
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## Intended Use |
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The Djinni dataset is designed with versatility in mind, supporting a wide range of applications: |
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- **Recommender Systems and Semantic Search:** It serves as a key resource for enhancing job recommendation engines and semantic search functionalities, making the job search process more intuitive and tailored to individual preferences. |
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- **Advancement of Large Language Models (LLMs):** The dataset provides invaluable training data for both English and Ukrainian domain-specific LLMs. It is instrumental in improving the models' understanding and generation capabilities, particularly in specialized recruitment contexts. |
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- **Fairness in AI-assisted Hiring:** By serving as a benchmark for AI fairness, the Djinni dataset helps mitigate biases in AI-assisted recruitment processes, promoting more equitable hiring practices. |
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- **Recruitment Automation:** The dataset enables the development of tools for automated creation of resumes and job descriptions, streamlining the recruitment process. |
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- **Market Analysis:** It offers insights into the dynamics of Ukraine's tech sector, including the impacts of conflicts, aiding in comprehensive market analysis. |
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- **Trend Analysis and Topic Discovery:** The dataset facilitates modeling and classification for trend analysis and topic discovery within the tech industry. |
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- **Strategic Planning:** By enabling the automatic identification of company domains, the dataset assists in strategic market planning. |
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## BibTeX entry and citation info |
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*When publishing results based on this dataset please refer to:* |
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```bibtex |
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@inproceedings{djinni, |
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title = "Introducing the {D}jinni {R}ecruitment {D}ataset: A Corpus of Anonymized {CV}s and Job Postings", |
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author = "Drushchak, Nazarii and |
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Romanyshyn, Mariana", |
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booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @LREC-COLING-2024", |
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month = may, |
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year = "2024", |
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address = "Torino, Italy", |
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publisher = "European Language Resources Association", |
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} |
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``` |
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## Attribution |
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Special thanks to [Djinni](https://djinni.co/) for providing this invaluable dataset. Their contribution is crucial in advancing research and development in AI, machine learning, and the broader tech industry. Their effort in compiling and sharing this dataset is greatly appreciated by the community. |
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