Datasets:

lang-uk
/

recruitment-dataset-candidate-profiles-ukrainian

	---
	language:
	- uk
	license: mit
	size_categories:
	- 10K<n<100K
	dataset_info:
	features:
	- name: Position
	dtype: string
	- name: Moreinfo
	dtype: string
	- name: Looking For
	dtype: string
	- name: Highlights
	dtype: string
	- name: Primary Keyword
	dtype: string
	- name: English Level
	dtype: string
	- name: Experience Years
	dtype: float64
	- name: CV
	dtype: string
	- name: CV_lang
	dtype: string
	- name: id
	dtype: string
	- name: __index_level_0__
	dtype: int64
	splits:
	- name: train
	num_bytes: 61582148
	num_examples: 24230
	download_size: 32043828
	dataset_size: 61582148
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	---

	# Djinni Dataset (Ukrainian CVs part)

	## Overview
	The [Djinni Recruitment Dataset](https://github.com/Stereotypes-in-LLMs/recruitment-dataset) (Ukrainian CVs 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.

	The dataset contains various attributes related to candidate CVs, including position titles, candidate information, candidate highlights, job search preferences, job profile types, English proficiency levels, experience years, concatenated CV text, language of CVs, and unique identifiers.

	## Intended Use

	The Djinni dataset is designed with versatility in mind, supporting a wide range of applications:

	- 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.

	- 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.

	- 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.

	- Recruitment Automation: The dataset enables the development of tools for automated creation of resumes and job descriptions, streamlining the recruitment process.

	- Market Analysis: It offers insights into the dynamics of Ukraine's tech sector, including the impacts of conflicts, aiding in comprehensive market analysis.

	- Trend Analysis and Topic Discovery: The dataset facilitates modeling and classification for trend analysis and topic discovery within the tech industry.

	- Strategic Planning: By enabling the automatic identification of company domains, the dataset assists in strategic market planning.

	## Load Dataset

	```python
	from datasets import load_dataset

	data = load_dataset("lang-uk/recruitment-dataset-candidate-profiles-ukrainian")['train']
	```

	## BibTeX entry and citation info
	When publishing results based on this dataset please refer to:
	```bibtex
	@inproceedings{drushchak-romanyshyn-2024-introducing,
	title = "Introducing the Djinni Recruitment Dataset: A Corpus of Anonymized {CV}s and Job Postings",
	author = "Drushchak, Nazarii and
	Romanyshyn, Mariana",
	editor = "Romanyshyn, Mariana and
	Romanyshyn, Nataliia and
	Hlybovets, Andrii and
	Ignatenko, Oleksii",
	booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024",
	month = may,
	year = "2024",
	address = "Torino, Italia",
	publisher = "ELRA and ICCL",
	url = "https://aclanthology.org/2024.unlp-1.2",
	pages = "8--13",
	}
	```

	## Attribution

	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.