sentence-transformers How to use GrishaKushnir/jobbert-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("GrishaKushnir/jobbert-onnx")
sentences = [
"系统管理员技术员——TS/SCI级别并拥有多项式验证",
"support development of annual budget, create a financial report, report analysis results, Microsoft Access, accounting, use presentation software, interpret financial statements, synthesise financial information, develop vaccines, handle financial overviews of the store, produce statistical financial records, develop financial statistics reports, explain accounting records, financial analysis, SAP R3, represent the company, examine budgets, prepare presentation material, use spreadsheets software, forecast account metrics, meet deadlines, prepare financial projections, manage budgets, exercise self-control, financial statements",
"ensure cross-department cooperation, establish customer rapport, improve business processes, manage technical security systems, handle incidents, maintain ICT system, explain characteristics of computer peripheral equipment, gather technical information, collaborate in company's daily operations , apply change management, maintain technical equipment, communicate with customers, solve technical problems, perform ICT troubleshooting, use ICT equipment in maintenance activities, manage major incidents, build business relationships, computer engineering, perform software recovery testing, identify process improvements, maintain relationship with customers, carry out project activities, collaborate in the development of marketing strategies, computer technology, technical terminology",
"utilise machine learning, cloud technologies, develop predictive models, assess sportive performance, formulate findings , principles of artificial intelligence, perform business research, communicate with stakeholders, computer engineering, build predictive models, computer science, develop automated software tests, analyse business objectives, Agile development, cloud monitoring and reporting, provide written content, obtain relevant licenses, design prototypes, machine learning, e-learning software infrastructure, analyse education system, disseminate results to the scientific community, learning technologies, ML (computer programming), task algorithmisation"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
