Instructions to use nurlanm/UniSkill_Bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nurlanm/UniSkill_Bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nurlanm/UniSkill_Bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nurlanm/UniSkill_Bert") model = AutoModelForSequenceClassification.from_pretrained("nurlanm/UniSkill_Bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signatureUniSkill: A Dataset for Matching University Curricula to Professional Competencies Nurlan Musazade, Joszef Mezei, Mike Zhang Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this work, we address the scarcity of publicly available datasets by releasing both manually annotated and synthetic datasets of skills from the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and university course pairs and publishing corresponding annotation guidelines. Specifically, we match graduate-level university courses with skills from the Systems Analysts and Management and Organization Analyst ESCO occupation groups at two granularities: course title with a skill, and course sentence with a skill. We train language models on this dataset to serve as a baseline for retrie…a07a690
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