FAQs
General Usage
1. What is the Tabiya Livelihoods Classifier? The Tabiya Livelihoods Classifier is a tool that leverages advanced transformer-based neural networks to extract and categorize key entities from job descriptions. It supports tasks like occupation and skill classification using frameworks like ESCO.
2. Who can benefit from using this tool? It is designed for HR professionals, recruiters, career advisors, labor market researchers, and developers working on job-matching technologies or workforce analytics.
3. What types of entities can the tool extract? The classifier identifies and categorizes five entity types: Occupation, Skill, Qualification, Experience, and Domain.
4. Is this tool compatible with any specific standards or frameworks? Yes, it retrieves ESCO-related entries for Occupations and Skills, aligning with widely used European job classification systems. With minimal work, other taxonomies like O*Net, could be integrated.
Technical Functionality
5. How does the Tabiya Livelihoods Classifier work? The process involves two main steps:
Entity Extraction: Identifies relevant entities in job descriptions.
Similarity Vector Search: Matches extracted entities to entries in pre-defined frameworks or datasets.
6. Does the tool use machine learning models? Yes, it utilizes transformer-based models, which represent the state-of-the-art in natural language processing.
7. Can I customize the classifier for specific industries or datasets? The tool supports customization, allowing users to adapt the similarity search or integrate custom datasets to suit specific domains and use cases.
8. What is the difference between entity extraction and similarity vector search? Entity extraction identifies relevant entities from text, such as a job title or skill. Similarity vector search then matches these entities to related entries in a knowledge base, like ESCO, for standardization.
Integration and Setup
9. How do I install and use the classifier? Detailed installation and setup instructions are available in the user guide.
10. Can the classifier be integrated into existing HR systems? Yes, it is designed to be easily integrated into workflows or systems through APIs or library functions.
11. Are there any prerequisites for using this tool? A working knowledge of Python is recommended for setup and integration. Familiarity with natural language processing concepts is beneficial but not mandatory.
Performance and Limitations
12. How accurate is the entity classification? The classifier achieves state-of-the-art entity recognition results based on the dataset released by Green et al. Albeit, as with any machine learning model, the Entity Linker is not perfect. If you encounter bugs or inappropriate use-cases, please open an issue on GitHub!
13. Does the tool handle multilingual job descriptions? We are currently working on developing a method of expanding the tool's capabilities for multiple languages. As of right now, the tool supports only the English and French languages.
14. Are there limitations on the size of input text? The model uses the NLTK sentence tokenizer function to handle large texts, so theoretically, there is no limit to the input text size. In the current version, the BERT-based models used for entity extraction have a limit of 128 tokens (roughly 100 words). You can use the training script to retrain the model to fit your needs.
Support and Customization
15. Can I contribute to or extend the tool? Yes, developers are welcome to customize and extend the tool. Refer to the contributing guide in the documentation for guidelines.
16. Where can I find support or report issues? Support is available through the official repository or customer service channels. Issues can be reported on the GitHub issues page or via email.
17. Are updates and new features planned? Yes, the tool is actively maintained, with plans for additional features and improved integrations based on user feedback.
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