Duplicate from gpriday/job-titles

Co-authored-by: Greg Priday <gpriday@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-classification
+- feature-extraction
+pretty_name: Comprehensive Job Titles Dataset
+tags:
+- jobs
+- occupations
+- employment
+- career
+- human-resources
+---
+
+# Comprehensive Job Titles Dataset
+
+A high-quality, deduplicated dataset of 65,248 unique job titles compiled from authoritative sources including ESCO (European Skills, Competences, Qualifications and Occupations), O*NET (Occupational Information Network), and OSCA (Occupational Skills and Competencies Australia).
+
+## Dataset Description
+
+This dataset provides a comprehensive collection of job titles that have been carefully processed to remove duplicates and near-duplicates using semantic similarity matching. It serves as a valuable resource for:
+
+- **Job matching and recommendation systems**
+- **Resume parsing and analysis**
+- **Labor market research**
+- **Career counseling applications**
+- **HR technology development**
+- **Natural language processing tasks related to employment**
+
+## Dataset Structure
+
+The dataset is provided in Parquet format with a single column:
+
+- `job_title` (string): The standardized job title
+
+### Example entries:
+```
+.NET Developer
+2D Animation Artist
+Accounting Clerk
+Administrative Assistant
+Agricultural Engineer
+AI Research Scientist
+Business Analyst
+Chef
+Data Scientist
+```
+
+## Sources
+
+The dataset combines job titles from three major occupational classification systems:
+
+1. **ESCO v1.2.0** (European Commission)
+   - ~33,000 occupations with multilingual support
+   - Includes preferred labels, alternative labels, and hidden labels
+   - Structured according to ISCO-08 classification
+
+2. **O*NET Database v29.3** (U.S. Department of Labor)
+   - ~1,000 detailed occupational descriptions
+   - Comprehensive taxonomy of U.S. occupations
+   - Includes detailed job characteristics and requirements
+
+3. **OSCA** (Australian Government)
+   - Australian occupational classifications
+   - Principal titles, alternative titles, and specializations
+
+## Processing Pipeline
+
+### 1. Extraction
+Job titles were extracted from multiple source files:
+- ESCO: Preferred labels and alternative labels from `occupations_en.csv`
+- O*NET: Occupation titles from `Occupation Data.txt`
+- OSCA: Principal titles and alternative titles from Excel files
+
+### 2. Deduplication
+A sophisticated deduplication process was applied:
+
+- **Embedding Model**: `sentence-transformers/all-mpnet-base-v2`
+- **Similarity Threshold**: 0.85 (cosine similarity)
+- **Strategy**: Length-based blocking for efficiency
+- **Preference**: Shorter titles retained (typically more general/common)
+
+The deduplication process identified semantically similar job titles such as:
+- "Software Developer" and "Software Engineer"
+- "Administrative Assistant" and "Admin Assistant"
+- "Customer Service Representative" and "Customer Service Rep"
+
+### 3. Quality Control
+- Removed exact duplicates (case-insensitive)
+- Filtered out malformed entries
+- Standardized formatting and capitalization
+- Preserved diversity while eliminating redundancy
+
+## Statistics
+
+- **Total unique job titles**: 65,248
+- **Original titles before deduplication**: ~100,000+
+- **Reduction rate**: ~35% (semantic duplicates removed)
+- **File size**: 756.7 KB (Parquet format with Snappy compression)
+
+## Use Cases
+
+### 1. Job Search and Matching
+```python
+import pandas as pd
+
+# Load the dataset
+df = pd.read_parquet('jobs.parquet')
+
+# Search for data-related jobs
+data_jobs = df[df['job_title'].str.contains('Data', case=False)]
+```
+
+### 2. Building Job Title Embeddings
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer('all-mpnet-base-v2')
+job_titles = df['job_title'].tolist()
+embeddings = model.encode(job_titles)
+```
+
+### 3. Job Title Standardization
+Use this dataset as a reference for standardizing job titles in your organization or application.
+
+## Limitations
+
+- **Language**: English only (though source data includes multilingual options)
+- **Geographic bias**: Stronger coverage of European, U.S., and Australian job markets
+- **Temporal**: Reflects job titles as of 2025; emerging roles may not be included
+- **Granularity**: Some highly specific or niche job titles may have been merged during deduplication
+
+## License
+
+This dataset combines data from multiple sources, each with their own licensing:
+- ESCO: European Union Public License (EUPL)
+- O*NET: Public domain (U.S. Government work)
+- OSCA: Creative Commons Attribution 3.0 Australia
+
+Please review the original source licenses for commercial use.
+
+## Citation
+
+If you use this dataset in your research or applications, please cite:
+
+```bibtex
+@dataset{jobs_dataset_2025,
+  author = {Greg Priday},
+  title = {Comprehensive Job Titles Dataset},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/gpriday/jobs}
+}
+```
+
+## Acknowledgments
+
+This dataset builds upon the excellent work of:
+- European Commission (ESCO)
+- U.S. Department of Labor (O*NET)
+- Australian Government (OSCA)
+
+## Contact
+
+For questions, suggestions, or contributions, please open an issue on the dataset repository.

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