sentence-transformers How to use Mickele-hub/cv-ai-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Mickele-hub/cv-ai-embedding-model")
sentences = [
"SummaryAccountant for a Medium sized Company\nSkillsGuest servicesInventory control proceduresMerchandising expertiseLoss preventionCash register operationsProduct promotions\nExperienceAccountant,01/2009toPresentAnimal Health International, Inc.–Mount Joy,PA,Hired by their CPA firm to handle all accounting and job cost Reporting.Accountant,01/2007to01/2009Animal Health International, Inc.–Greeley,CO,Hired by their CPA firm to handle all accounting functions..Accountant,01/1997to01/2007Animal Health International, Inc.–Manchester,IA,Installed new Peachtree Accounting System.Installed new computer system using a local area network and Added a Web site.\nEducation and TrainingB.S:Business Administration Accounting,Expected in1974toUniversity of Cincinnati-Cincinnati,OHGPA:Business Administration Accounting\nSkillsaccounting, CPA, local area network, Peachtree Accounting, Reporting, Web site",
"Job Purpose: Perform designated tasks in the area of technical sub-ledger accounting related to all lines of business. Perform and review tasks related to period end financial closing.\nKey Responsibilities:Investigate and resolve discrepancies by actively collaborating and communicating across departments. Create policies and procedures for developing areas such as the Surety accounting.Review accounting entries posted to the general ledger to ensure proper accounting treatment.Work closely with other teams to ensure proper premium accounting and make manual corrections as needed.Prepare complex monthly bank reconciliations. Responsible for reconciling intercompany AP, AR, and P&L for all other BUs.Perform complex manual processes necessary to maintain proper accounting records and develop ways to automate them.Present to management premium results monthly including thorough explanations on insurance revenues, service revenues, and bad debt. Able to speak to analytical results.Perform regular analytics prior to, during, and post monthly close.Investigate and resolve discrepancies and fluctuations resulting from analytics performed by collaborating with other teams to determine accurate results.Responsible for preparing documentation and providing explanations as requested by internal and external auditors and regulators. \nKey Experience:7+ years accounting experience4+ years as a seasoned senior accountantPublic accounting a plusKnowledge of insurance accounting a plus\nKey Requirements:Bachelors Degree in Accounting Finance Accounting and technical know-how, basis for understanding of accounting, and advanced technical systems. Good knowledge of MS Office suiteWillingness to work overtime as needed to meet monthly deadlinesStrong analytical, critical thinking & problem solving skillsStrong aptitude for accuracy and attention to detailExcellent written and verbal communication skills.Strong organizational skills and ability to manage deadlines. Ability to collaborate and influence internal partners to set up corrective action plansHigh level of initiative & ability to be autonomousAble to multi task",
"Compensation: $30-$45hr\nLocation: Lakewood, CO (Hybrid)\nTITLE: Operational Accountant\nInceed has partnered with a great company to help find a skilled Operational Accountant to join their team!\nThe Operational Accountant is directly responsible for various aspects of closing the accounting books and billing, in accordance with GAAP and FERC, monthly for the crude segment.\nResponsibilities:\nPrepare adjusting close journal entries, including but not limited to: Revenue Accruals, Expense Accruals, Deferred Revenue. Analyze balance sheets and income statements monthly for accuracy.Assist the Accounting Manager with monthly, quarterly and yearly gross margin analysis.Responsible for certain monthly invoicing processes. Prepare balance sheet account reconciliations and maintain supporting schedules. Required Qualifications & Experience:\nBachelors degree in Finance, Accounting or a related discipline3-4 years of experience actively involved in a relevant and related roleMust have a fundamental understanding of accounting and financial principlesAdvanced Excel experience Nice to Have Skills & Experience:\nOil and gas industry experience preferred Perks & Benefits: \n3 different medical health insurance plans, dental, and vision insuranceVoluntary and Long-term disability insurancePaid Time Off, 401k, and Holiday Pay*Weekly direct deposit or pay card deposit Other Information:\nThis position is a 6 month contractThis position requires a drug and background check\nIf you are interested in learning more about the Operational Accountant opportunity, please submit your resume for consideration. We are unable to provide sponsorship at this time.\nWe are Inceed, a staffing and direct placement firm who believes in the possibility of something better. Our mission is simple: Were here to help every person, whether client, candidate, or employee, find and secure whats better for them. \nInceed is an equal opportunity employer. Inceed prohibits discrimination and harassment of any type and affords equal employment opportunities to employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law.\n",
"HiETLData EngineerDallas tx-Onsite\nNeed only Local to DALLAS, TX- Onsite 1) Need Data Engineer with heavy Python Development Background.2) Must have worked on UNIXLINUX, Extensive Experience with SQLUnixLinux scripting is a must.3) Strong exp on AWS Cloud, Experience in Automating ETL process with Python and Automation around AWS Data & Infrastructure4) AWS Glue is must, with hold on ETL Concepts.5) Good to have Testing exp and familiarity with Test Strategies. \nRegards,Nisha JhaTechnical RecruiterNisha.j@peritussoft.com"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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