---
dataset_info:
  features:
  - name: Position
    dtype: string
  - name: Long Description
    dtype: string
  - name: Company Name
    dtype: string
  - name: Exp Years
    dtype: string
  - name: Primary Keyword
    dtype: string
  - name: English Level
    dtype: string
  - name: Published
    dtype: string
  - name: Long Description_lang
    dtype: string
  - name: id
    dtype: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 281256096
    num_examples: 141897
  download_size: 145859589
  dataset_size: 281256096
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
language:
- en
size_categories:
- 100K<n<1M
---

# Djinni Dataset (English Job Descriptions part)

## Overview
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. 

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.

## 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-job-descriptions-english")['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.