Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:812
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: Data engineering, AWS services, Big Data manipulation
+  sentences:
+  - 'Skills: SQL, PySpark, Databricks, Azure Synapse, Azure Data Factory.
+
+    Need hands-on coding
+
+    Requirements:1. Extensive knowledge of any of the big cloud services - Azure,
+    AWS or GCP with practical implementation (like S3, ADLS, Airflow, ADF, Lamda,
+    BigQuery, EC2, Fabric, Databricks or equivalent)2. Strong Hands-on experience
+    in SQL and Python/PySpark programming knowledge. Should be able to write code
+    during an interview with minimal syntax error.3. Strong foundational and architectural
+    knowledge of any of the data warehouses - Snowflake, Redshift. Synapse etc.4.
+    Should be able to drive and deliver projects with little or no guidance. Take
+    ownership, become a self-learner, and have leadership qualities.'
+  - "requirements, and general interpretation of dataMentor, teach, share knowledge\
+    \ and analytic techniques with your colleagues\n\nExperience And Preferred Qualifications\n\
+    \nMinimum of three years of relevant experience in developing analytic solutions\
+    \ with proficiency in SQL, Microsoft Excel, Power BI, or similar data analysis\
+    \ and ETL toolsBachelor's degree (B.S./B.A.) in an appropriate field from an accredited\
+    \ college or universityStrong verbal and written communication skills with the\
+    \ ability to convey highly complex technical concepts down to actionable objectives\
+    \ to advise stakeholders including attorneys, firm management, and firm colleaguesExperience\
+    \ in project management including planning, organizing, and supervising clients\
+    \ and colleagues towards successful project completionDemonstrated ability to\
+    \ learn and succeed in a fast-paced environmentExpert level of proficiency with\
+    \ T-SQL or equivalent including a high level of proficiency in database administrationHigh\
+    \ proficiency with Microsoft Excel including an ability to create pivot tables,\
+    \ power pivots & queries, formulas, and external data connectionsAbility to design\
+    \ and implement ETL solutionsExperience in developing client facing visualizations\
+    \ and reports using Power BI, SSRS or similar visualization tools is a plusKnowledge\
+    \ of coding in Python, R, DAX and/or MExperience in developing SSIS and/or SSAS\
+    \ solutions\n\nQualified candidates must apply online by visiting our website\
+    \ at www.morganlewis.com and selecting “Careers.”\n\nMorgan, Lewis & Bockius LLP\
+    \ is committed to \n\nPursuant to applicable state and municipal Fair Chance Laws\
+    \ and Ordinances, we will consider for employment qualified applicants with arrest\
+    \ and conviction records.\n\nCalifornia Applicants: Pursuant to the California\
+    \ Consumer Privacy Act, the following link contains the Firm's California Consumer\
+    \ Privacy Act Privacy Notice for Candidates which explains the categories of personal\
+    \ information that we collect and the purposes for which we use such personal\
+    \ information. CCPA Privacy Notice for Candidates\n\nMorgan Lewis & Bockius LLP\
+    \ is also \n\nIf You Are Interested In Applying For Employment With Morgan Lewis\
+    \ And Need Special Assistance Or An Accommodation To Use Our Website Or To Apply\
+    \ For a Position, Please Call Or Email The Following Contacts\n\nProfessional\
+    \ Staff positions – 1.888.534.5003 / [email protected] \n\nMorgan,\
+    \ Lewis & Bockius, LLP reasonably accommodates applicants and employees who need\
+    \ them to perform the essential functions of the job because of disability, religious\
+    \ belief, or other reason protected by applicable law. If you believe you need\
+    \ a reasonable accommodation during the application process, please contact Talent\
+    \ Acquisition at [email protected]."
+  - experience as a data engineer, data architect, with strong Python and SQL knowledge.
+    Experience with AWS services and Databricks, and ideal if they've developed data
+    pipelines in airflow or any streaming services (Kafka, Kinesis, etc). Expert-level
+    competency in Big Data manipulation and transformation, both within and outside
+    of a database. Need to have competency in API creation, and Machine Learning model
+    deployment. Experience mentoring others and can help as a field leader for newer
+    team members.Additional Skills & QualificationsExperience building decision-support
+    applications based on Data Science and Machine LearningExperience building effective,
+    efficient solutions in AWS, using Terraform and/or CloudFormation to build infrastructure
+    as codeFamiliarity with Snowflake, Airflow, and other Big Data and data pipeline
+    frameworksEducation, training, and certifications in engineering, computer science,
+    math, statistics, analytics, or cloud computing.
+- source_sentence: Digital advertising, MLOps, audience segmentation
+  sentences:
+  - "experience, skills and abilities will determine where an employee is ultimately\
+    \ placed in the pay range.\n\nCategory/Shift\n\nSalaried Full-Time\n\nPhysical\
+    \ Location:\n\n6420 Poplar Avenue\n\nMemphis, TN\n\nFlexible Remote Work Schedule\n\
+    \nThe Job You Will Perform\n\nLead the hands-on IT development and deployment\
+    \ of data science and advanced analytics solutions for the North American Container\
+    \ (NAC) division of International Paper to support business strategies across\
+    \ approximately 200 packaging and specialty plants in the US and MexicoBreak down\
+    \ complex data science methodologies to business leaders in a way that is applicable\
+    \ to our North American Container business strategy.Identify opportunities for\
+    \ improving business performance and present identified opportunities to senior\
+    \ leadership; proactively driving the discovery of business value through data.Collaborate\
+    \ directly with NAC business partners to produce user stories, analyze source\
+    \ data capabilities, identify issues and opportunities, develop data models, and\
+    \ test and deploy innovative analytics solutions and systemsLead the application\
+    \ of data science techniques to analyze and interpret complex data sets, providing\
+    \ insights and enabling data-driven decision-making for North American ContainerLead\
+    \ analytics projects through agile or traditional project management methodologiesInfluence\
+    \ IT projects/initiatives with project managers, business leaders and other IT\
+    \ groups without direct reporting relationships.Work closely with IT Application\
+    \ Services team members to follow standards, best practices, and consultation\
+    \ for data engineeringRole includes: Data analysis, predictive and prescriptive\
+    \ modeling, machine learning, and algorithm development; collaborating and cross-training\
+    \ with analytics and visualization teams.Under general direction works on complex\
+    \ technical issues/problems of a large scope, impact, or importance. Independently\
+    \ resolves complex problems that have significant cost. Leads new technology innovations\
+    \ that define new “frontiers” in technical direction\n\nThe Skills You Will Bring\
+    \ \n\nBachelor’s degree in Computer Science, Information Technology, Statistics,\
+    \ or a related field is required. A Masters degree and/or PhD is preferred.Minimum\
+    \ 12 years of relevant work experience, less if holding a Masters or PhD.Skills\
+    \ with Data Visualization using tools like Microsoft Power BIDemonstrated leadership\
+    \ in building and deploying advanced analytics models for solving real business\
+    \ problems.Strong Interpersonal and Communication SkillsAdaptable to a changing\
+    \ work environment and dealing with ambiguity as it arises. Data Science Skills:Data\
+    \ analysisPredictive and Prescriptive ModelingMachine Learning (Python / R)Artificial\
+    \ Intelligence and Large Language ModelsAlgorithm DevelopmentExperience with Azure\
+    \ Analytics ServicesCompetencies:Dealing with AmbiguityFunctional / Technical\
+    \ Skills Problem SolvingCreativity\nThe Benefits You Will Enjoy\n\nPaid time off\
+    \ including Vacation and Holidays Retirement and 401k Matching ProgramMedical\
+    \ & Dental Education & Development (including Tuition Reimbursement)Life & Disability\
+    \ Insurance\n\nThe Career You Will Build\n\nLeadership trainingPromotional opportunities\n\
+    \nThe Impact You Will Make\n\nWe continue to build a better future for people,\
+    \ the plant, and our company! IP has been a good steward of sustainable practices\
+    \ across communities around the world for more than 120 years. Join our team and\
+    \ you’ll see why our team members say they’re Proud to be IP.\n\nThe Culture You\
+    \ Will Experience\n\nInternational Paper promotes employee well-being by providing\
+    \ safe, caring and inclusive workplaces. You will learn Safety Leadership Principles\
+    \ and have the opportunity to opt into Employee Networking Circles such as IPVets,\
+    \ IPride, Women in IP, and the African American ENC. We invite you to bring your\
+    \ uniqueness, creativity, talents, experiences, and safety mindset to be a part\
+    \ of our increasingly diverse culture.\n\nThe Company You Will Join\n\nInternational\
+    \ Paper (NYSE: IP) is a leading global supplier of renewable fiber-based products.\
+    \ We produce corrugated packaging products that protect and promote goods, and\
+    \ enable worldwide commerce, and pulp for diapers, tissue and other personal care\
+    \ products that promote health and wellness. Headquartered in Memphis, Tenn.,\
+    \ we employ approximately 38,000 colleagues globally. We serve customers worldwide,\
+    \ with manufacturing operations in North America, Latin America, North Africa\
+    \ and Europe. Net sales for 2021 were $19.4 billion. Additional information can\
+    \ be found by visiting InternationalPaper.com.\n\nInternational Paper is an Equal\
+    \ Opportunity/Affirmative Action Employer. All qualified applicants will receive\
+    \ consideration for employment without regard to sex, gender identity, sexual\
+    \ orientation, race, color, religion, national origin, disability, protected veteran\
+    \ status, age, or any other characteristic protected by law."
+  - 'experience, education, geographic location, and other factors. Description: This
+    role is within an organization responsible for developing and maintaining a high-performance
+    Advertising Platform across various online properties, including streaming services.
+    The Ad Platform Research team focuses on transforming advertising with data and
+    AI, seeking a lead machine learning engineer to develop prediction and optimization
+    engines for addressable ad platforms.
+
+    Key responsibilities include driving innovation, developing scalable solutions,
+    collaborating with teams, and mentoring. Preferred qualifications include experience
+    in digital advertising, knowledge of ML operations, and proficiency in relevant
+    technologies like PyTorch and TensorFlow.
+
+    Basic Qualifications:MS or PhD in computer science or EE.4+ years of working experience
+    on machine learning, and statistics in leading internet companies.Experience in
+    the advertising domain is preferred.Solid understanding of ML technologies, mathematics,
+    and statistics.Proficient with Java, Python, Scala, Spark, SQL, large scale ML/DL
+    platforms and processing tech stack.
+
+    Preferred Qualifications:Experience in digital video advertising or digital marketing
+    domain.Experience with feature store, audience segmentation and MLOps.Experience
+    with Pytorch, TensorFlow, Kubeflow, SageMaker or Databricks.
+
+    If you are interested in this role, then please click APPLY NOW. For other opportunities
+    available at Akkodis, or any questions, please contact Amit Kumar Singh at [email protected].
+
+    Equal Opportunity Employer/Veterans/Disabled
+
+    Benefit offerings include medical, dental, vision, term life insurance, short-term
+    disability insurance, additional voluntary benefits, commuter benefits, and a
+    401K plan. Our program provides employees the flexibility to choose the type of
+    coverage that meets their individual needs. Available paid leave may include Paid
+    Sick Leave, where required by law; any other paid leave required by Federal, State,
+    or local law; and Holiday pay upon meeting eligibility criteria. Disclaimer: These
+    benefit offerings do not apply to client-recruited jobs and jobs which are direct
+    hire to a client.
+
+    To read our Candidate Privacy Information Statement, which explains how we will
+    use your information, please visit https://www.akkodis.com/en/privacy-policy.'
+  - 'Qualifications
+
+    Master''s degree is preferred in a Technical Field, Computer Science, Information
+    Technology, or Business ManagementGood understanding of data structures and algorithms,
+    ETL processing, large-scale data and machine-learning production, data and computing
+    infrastructure, automation and workflow orchestration.Hands-on experience in Python,
+    Pyspark, SQL, and shell scripting or similar programming languagesHands-on Experience
+    in using cloud-based technologies throughout data and machine learning product
+    development.Hands-on experience with code versioning, automation and workflow
+    orchestration tools such as Github, Ansible, SLURM, Airflow and TerraformGood
+    Understanding of data warehousing concepts such as data migration and data integration
+    in Amazon Web Services (AWS) cloud or similar platformExcellent debugging and
+    code-reading skills.Documentation and structured programming to support sustainable
+    development.Ability to describe challenges and solutions in both technical and
+    business terms.Ability to develop and maintain excellent working relationships
+    at all organizational levels.'
+- source_sentence: Geospatial data management, spatial analysis, PostGIS expertise
+  sentences:
+  - 'experiences, revenue generation, ad targeting, and other business outcomes.Conduct
+    data processing and analysis to uncover hidden patterns, correlations, and insights.Design
+    and implement A/B testing frameworks to test model quality and effectiveness.Collaborate
+    with engineering and product development teams to integrate data science solutions
+    into our products and services.Stay up-to-date with the latest technologies and
+    techniques in data science, machine learning, and artificial intelligence.
+
+    Technical Requirements:Strong proficiency in programming languages such as Python
+    or R for data analysis and modeling.Extensive experience with machine learning
+    techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.Knowledge
+    of advanced statistical techniques and concepts (regression, properties of distributions,
+    statistical tests, etc.).Experience with data visualization tools (e.g., Matplotlib,
+    Seaborn, Tableau).Familiarity with big data frameworks and tools (e.g., Hadoop,
+    Spark).Proficient in using query languages such as SQL.Experience with cloud computing
+    platforms (AWS, Azure, or Google Cloud) is a plus.Understanding of software development
+    practices and tools, including version control (Git).
+
+    Experience:3+ years of experience in a Data Scientist or similar role.Demonstrated
+    success in developing and deploying data models, algorithms, and predictive analytics
+    solutions.Experience working with large, complex datasets and solving analytical
+    problems using quantitative approaches.
+
+    Who You Are:Analytically minded with a passion for uncovering insights through
+    data analysis.Creative problem solver who is eager to tackle complex challenges.Excellent
+    communicator capable of explaining complex technical concepts to non-technical
+    stakeholders.Self-motivated and able to work independently in a remote environment.A
+    collaborative team player who thrives in a dynamic, fast-paced setting.
+
+    Join Us:At RTeams, you''ll be part of an innovative company that values the transformative
+    power of data. Enjoy the flexibility of remote work across the US, with standard
+    working hours that support work-life balance. Here, we believe in empowering our
+    team members to innovate, explore, and make a significant impact.'
+  - "Skills:Intermediate Level MS Excel (Pivot & Macros knowledge helpful)Intermediate\
+    \ Level MS PowerPoint (Presentation Slides & Charts)Familiarity with Data Storage\
+    \ platforms, directories and network drivesVBA ConceptsSQL BasicData Visualization\
+    \ Concepts\n\nSoft Skills:Punctuality is required due to the reporting deadlines\
+    \ & on time delivery of dataOrganizedTeam playerCurious & Quick Learner\n\nEducation/Experience:Associate\
+    \ Degree in a technical field such as computer science, computer engineering or\
+    \ related field required2 -3 years of experience requiredProcess certification,\
+    \ such as, Six Sigma, CBPP, BPM, ISO 20000, ITIL, CMMI\n\nSummary: The main function\
+    \ of the Data Analyst is to provide business intelligence support and supporting\
+    \ areas by means of both repeatable and ad hoc reporting delivery reports (charts,\
+    \ graphs, tables, etc.) that enable informed business decisions.  \nJob"
+  - 'experience.Support database architecture performance and optimization.Support,
+    and explore new ways to monetize Galehead’s geospatial tools, including entering
+    new verticals.Provide as-needed support for both technical and business issues
+    related to geospatial tools and outputs, including coaching/training other team
+    members, as needed.Collaborate to develop new analytic data productsWrite and
+    maintain a suite of automated data processesBring your best stuff: we need the
+    best from everyone.
+
+    KEY REQUIREMENTS:Ability to create reproducible data processes, products, and
+    visualizations using Python and SQL (or similar).Strong analytical and problem
+    solving skills.Experience with open source geospatial processing tools including
+    PostGIS (or other spatial SQL), GDAL/OGR, and/or Geopandas.Communications: Effective
+    and thoughtful written and verbal communications. Work through issues or differing
+    perspectives in a concise and professional manner.Organization: Maintain focus
+    and extract value from the high volume of opportunities through command of the
+    mission and meticulous organization of information, communications, resources
+    and responsibilities.Collaboration: Serve as a resource to the entire team and
+    facilitate getting work completed cross-functionally.
+
+    PREFERED SKILLS/CAPABILITIESExperience using Postgresql including complex analytic
+    queries and performance considerations.Energy industry experience.Experience in
+    software development practices including, but not limited to Git, Jira, Agileogr/gdalpostgres/postgispython
+    - (pandas/geopandas)
+
+    GALEHEAD CULTURE:Accountability: Set and maintain high standards for yourself
+    and your coworkers.Problem-Solving: Willingness to consider problems and find/drive
+    a path forward. Identify and recommend solutions.Our Values:Bold: Demonstrate
+    a bias for action and stretching conventional boundaries with a commensurate ability
+    to acknowledge, define, and mitigate risk.Driven: Demonstrate an inherent motivation
+    to succeed, regardless of externalities.True: Demonstrate transparency at all
+    times, provide and accept constructive feedback.'
+- source_sentence: Data analysis, statistical modeling, data visualization
+  sentences:
+  - "Skills: AWS, Spark, Adobe Analytics/AEP(Adobe Experience Platform) platform experience,\
+    \ Glue, Lamda, Python, Scala, EMR, Talend, PostgreSQL, Redshift\n\n Configure\
+    \ AEP to get the data set needed and then use spark (AWS glue ) to load data in\
+    \ the data lake Evaluate new use cases and design ETL technical solutions to meet\
+    \ requirements Develop ETL solutions to meet complex use cases\n\nAdobe Data Engineer\
+    \ || Remote"
+  - 'experience solutions and technologies.This is a hybrid position, with the ideal
+    candidate located near one of our regional hubs (New York, Chicago, Boston) and
+    able to travel to an office as needed for working sessions or team meetings.
+
+    Curinos is looking for a Senior Data Engineering Manager to lead the build and
+    expansion of our Retail Consumer product suite, relied on by our clients for precision
+    deposit analysis and optimization. Our Retail Consumer business covers the largest
+    suite of Curinos products and this position is a critical role within the Product
+    Development team, combining both hands-on technical work (architecture, roadmap,
+    code review, POC of new/complex methodologies) and team management.In this role,
+    you will lead a cross-functional Product Development team of Software, Data and
+    QA engineers covering all aspects of product development (UI/Middle Tier/API/Backend/ETL).
+    You will collaborate with product owners on business requirements and features,
+    work with the development team to identify scalable architecture and methodologies
+    needed to implement, and own the timely and error-free delivery of those features.
+    You will be expected to be “hands-on-keys” in this role, leading the team by example
+    and helping to establish and model quality software development practices as the
+    team, products and business continues to grow.
+
+    ResponsibilitiesBuilding and leading a Product Engineering team consisting of
+    Software, Data and QA EngineersModeling quality software development practices
+    to the team by taking on user stories and writing elegant and scalable codeConducting
+    code reviews and providing feedback to help team members advance their skillsLeading
+    the design and development of performant, extendable and maintainable product
+    functionality, and coaching the team on the principles of efficient and scalable
+    designEngaging with product owner and LOB head to understand client needs and
+    craft product roadmaps and requirementsProviding input into the prioritization
+    of features to maximize value delivered to clientsAnalyzing complex business problems
+    and identifying solutions and own the implementationIdentifying new technologies
+    and tools which could improve the efficiency and productivity of your teamWorking
+    with in the Agile framework to manage the team’s day-to-day activitiesUnderstanding
+    Curinos’ Application, API and Data Engineering platforms and effectively using
+    them to build product featuresUnderstanding Curinos’ SDLC and compliance processes
+    and ensuring the team’s adherence to them
+
+    Base Salary Range: $160,000 to $185,000 (plus bonus)
+
+    Desired Skills & Expertise6+ years professional full stack experience developing
+    cloud based SaaS products using Java, SPA and related technologies with a complex
+    backend data processing system[SW1][NS2]3+ years of experience with SQL Server
+    or Databricks ETL, including hands-on experience developing SQL stored procedures
+    and SQL-based ETL pipelines2+ Years of management experience of engineers/ICsProven
+    ability to grow and lead geographically dispersed and cross-functional teamsA
+    passion for proactively identifying opportunities to eliminate manual work within
+    the SDLC process and as part of product operationA commitment to building a quality
+    and error-free product, via implementation of unit testing, integration testing,
+    and data validation strategiesA desire to design and develop for scale and in
+    anticipation of future use casesDemonstrated intellectual curiosity and innovative
+    thinking with a passion for problem-solvingSelf–discipline and willingness to
+    learn new skills, tools and technologiesExcellent verbal and written communication
+    skillsAdvanced proficiency in Java (including testing frameworks like Junit) and
+    T-SQL (including dynamic sql and the use of control structures) is an assetExperience
+    using Scala is a plusExperience using a templating language like Apache Freemarker
+    is a plusBachelors or advanced degrees (Masters or PhD) degree, preferably in
+    computer science, or a related engineering field
+
+    Why work at Curinos?Competitive benefits, including a range of Financial, Health
+    and Lifestyle benefits to choose fromFlexible working options, including home
+    working, flexible hours and part time options, depending on the role requirements
+    – please ask!Competitive annual leave, floating holidays, volunteering days and
+    a day off for your birthday!Learning and development tools to assist with your
+    career developmentWork with industry leading Subject Matter Experts and specialist
+    productsRegular social events and networking opportunitiesCollaborative, supportive
+    culture, including an active DE&I programEmployee Assistance Program which provides
+    expert third-party advice on wellbeing, relationships, legal and financial matters,
+    as well as access to counselling services
+
+    Applying:We know that sometimes the ''perfect candidate'' doesn''t exist, and
+    that people can be put off applying for a job if they don''t meet all the requirements.
+    If you''re excited about working for us and have relevant skills or experience,
+    please go ahead and apply. You could be just what we need!If you need any adjustments
+    to support your application, such as information in alternative formats, special
+    requirements to access our buildings or adjusted interview formats please contact
+    us at [email protected] and we’ll do everything we can to help.
+
+    Inclusivity at Curinos:We believe strongly in the value of diversity and creating
+    supportive, inclusive environments where our colleagues can succeed. As such,
+    Curinosis proud to be'
+  - "Qualifications\n Data Science, Statistics, and Data Analytics skillsData Visualization\
+    \ and Data Analysis skillsExperience with machine learning algorithms and predictive\
+    \ modelingProficiency in programming languages such as Python or RStrong problem-solving\
+    \ and critical thinking abilitiesExcellent communication and presentation skillsAbility\
+    \ to work independently and remotelyExperience in the field of data science or\
+    \ related rolesBachelor's degree in Data Science, Statistics, Computer Science,\
+    \ or a related field"
+- source_sentence: NLP algorithm development, statistical modeling, biomedical informatics
+  sentences:
+  - 'skills for this position are:Natural Language Processing (NLP)Python (Programming
+    Language)Statistical ModelingHigh-Performance Liquid Chromatography (HPLC)Java
+    Job Description:We are seeking a highly skilled NLP Scientist to develop our innovative
+    and cutting-edge NLP/AI solutions to empower life science. This involves working
+    directly with our clients, as well as cross-functional Biomedical Science, Engineering,
+    and Business leaders, to identify, prioritize, and develop NLP/AI and Advanced
+    analytics products from inception to delivery.Key requirements and design innovative
+    NLP/AI solutions.Develop and validate cutting-edge NLP algorithms, including large
+    language models tailored for healthcare and biopharma use cases.Translate complex
+    technical insights into accessible language for non-technical stakeholders.Mentor
+    junior team members, fostering a culture of continuous learning and growth.Publish
+    findings in peer-reviewed journals and conferences.Engage with the broader scientific
+    community by attending conferences, workshops, and collaborating on research projects.
+    Qualifications:Ph.D. or master''s degree in biomedical NLP, Computer Science,
+    Biomedical Informatics, Computational Linguistics, Mathematics, or other related
+    fieldsPublication records in leading computer science or biomedical informatics
+    journals and conferences are highly desirable
+
+
+    Regards,Guru Prasath M US IT RecruiterPSRTEK Inc.Princeton, NJ [email protected]:
+    609-917-9967 Ext:114'
+  - 'Qualifications and Experience:
+
+
+    Bachelor’s degree in data science, Statistics, or related field, or an equivalent
+    combination of education and experience.Working knowledge of Salesforce.Ability
+    to leverage enterprise data for advanced reporting.Proficiency in combining various
+    data sources for robust output.Strong knowledge of Annuity products and distribution
+    structure.Influencing skills and change management abilities.4-6 years of experience
+    in financial services.Strong organizational skills.Proven success in influencing
+    across business units and management levels.Confidence and ability to make effective
+    business decisions.Willingness to travel (less. than 10%)
+
+
+    Drive. Discipline. Confidence. Focus. Commitment. Learn more about working at
+    Athene.
+
+
+    Athene is a Military Friendly Employer! Learn more about how we support our Veterans.
+
+
+    Athene celebrates diversity, is committed to inclusion and is proud to be'
+  - 'Skills :
+
+    a) Azure Data Factory – Min 3 years of project experiencea. Design of pipelinesb.
+    Use of project with On-prem to Cloud Data Migrationc. Understanding of ETLd. Change
+    Data Capture from Multiple Sourcese. Job Schedulingb) Azure Data Lake – Min 3
+    years of project experiencea. All steps from design to deliverb. Understanding
+    of different Zones and design principalc) Data Modeling experience Min 5 Yearsa.
+    Data Mart/Warehouseb. Columnar Data design and modelingd) Reporting using PowerBI
+    Min 3 yearsa. Analytical Reportingb. Business Domain Modeling and data dictionary
+
+    Interested please apply to the job, looking only for W2 candidates.'
+datasets:
+- Mubin/ai-job-embedding-finetuning
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: ai job validation
+      type: ai-job-validation
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9702970297029703
+      name: Cosine Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: ai job test
+      type: ai-job-test
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9803921568627451
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [ai-job-embedding-finetuning](https://huggingface.co/datasets/Mubin/ai-job-embedding-finetuning) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [ai-job-embedding-finetuning](https://huggingface.co/datasets/Mubin/ai-job-embedding-finetuning)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Mubin/allmini-ai-embedding-similarity")
+# Run inference
+sentences = [
+    'NLP algorithm development, statistical modeling, biomedical informatics',
+    "skills for this position are:Natural Language Processing (NLP)Python (Programming Language)Statistical ModelingHigh-Performance Liquid Chromatography (HPLC)Java Job Description:We are seeking a highly skilled NLP Scientist to develop our innovative and cutting-edge NLP/AI solutions to empower life science. This involves working directly with our clients, as well as cross-functional Biomedical Science, Engineering, and Business leaders, to identify, prioritize, and develop NLP/AI and Advanced analytics products from inception to delivery.Key requirements and design innovative NLP/AI solutions.Develop and validate cutting-edge NLP algorithms, including large language models tailored for healthcare and biopharma use cases.Translate complex technical insights into accessible language for non-technical stakeholders.Mentor junior team members, fostering a culture of continuous learning and growth.Publish findings in peer-reviewed journals and conferences.Engage with the broader scientific community by attending conferences, workshops, and collaborating on research projects. Qualifications:Ph.D. or master's degree in biomedical NLP, Computer Science, Biomedical Informatics, Computational Linguistics, Mathematics, or other related fieldsPublication records in leading computer science or biomedical informatics journals and conferences are highly desirable\n\nRegards,Guru Prasath M US IT RecruiterPSRTEK Inc.Princeton, NJ [email protected]: 609-917-9967 Ext:114",
+    'Skills :\na) Azure Data Factory – Min 3 years of project experiencea. Design of pipelinesb. Use of project with On-prem to Cloud Data Migrationc. Understanding of ETLd. Change Data Capture from Multiple Sourcese. Job Schedulingb) Azure Data Lake – Min 3 years of project experiencea. All steps from design to deliverb. Understanding of different Zones and design principalc) Data Modeling experience Min 5 Yearsa. Data Mart/Warehouseb. Columnar Data design and modelingd) Reporting using PowerBI Min 3 yearsa. Analytical Reportingb. Business Domain Modeling and data dictionary\nInterested please apply to the job, looking only for W2 candidates.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Datasets: `ai-job-validation` and `ai-job-test`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | ai-job-validation | ai-job-test |
+|:--------------------|:------------------|:------------|
+| **cosine_accuracy** | **0.9703**        | **0.9804**  |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### ai-job-embedding-finetuning
+
+* Dataset: [ai-job-embedding-finetuning](https://huggingface.co/datasets/Mubin/ai-job-embedding-finetuning) at [b18b3c2](https://huggingface.co/datasets/Mubin/ai-job-embedding-finetuning/tree/b18b3c20bc31354d97bad62866da97618b6c13b7)
+* Size: 812 training samples
+* Columns: <code>query</code>, <code>job_description_pos</code>, and <code>job_description_neg</code>
+* Approximate statistics based on the first 812 samples:
+  |         | query                                                                             | job_description_pos                                                                 | job_description_neg                                                                 |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              | string                                                                              |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 15.03 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 216.92 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 217.63 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | query                                                                                         | job_description_pos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | job_description_neg                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Data Engineering Lead, Databricks administration, Neo4j expertise, ETL processes</code> | <code>Requirements<br><br>Experience: At least 6 years of hands-on experience in deploying production-quality code, with a strong preference for experience in Python, Java, or Scala for data processing (Python preferred).Technical Proficiency: Advanced knowledge of data-related Python packages and a profound understanding of SQL and Databricks.Graph Database Expertise: Solid grasp of Cypher and experience with graph databases like Neo4j.ETL/ELT Knowledge: Proven track record in implementing ETL (or ELT) best practices at scale and familiarity with data pipeline tools.<br><br>Preferred Qualifications<br><br>Professional experience using Python, Java, or Scala for data processing (Python preferred)<br><br>Working Conditions And Physical Requirements<br><br>Ability to work for long periods at a computer/deskStandard office environment<br><br>About The Organization<br><br>Fullsight is an integrated brand of our three primary affiliate companies – SAE Industry Technologies Consortia, SAE International and Performance Review Institute – a...</code> | <code>skills through a combination of education, work experience, and hobbies. You are excited about the complexity and challenges of creating intelligent, high-performance systems while working with a highly experienced and driven data science team.<br><br>If this described you, we are interested. You can be an integral part of a cross-disciplinary team working on highly visible projects that improve performance and grow the intelligence in our Financial Services marketing product suite. Our day-to-day work is performed in a progressive, high-tech workspace where we focus on a friendly, collaborative, and fulfilling environment.<br><br>Key Duties/Responsibilities<br><br>Leverage a richly populated feature stores to understand consumer and market behavior. 20%Implement a predictive model to determine whether a person or household is likely to open a lending or deposit account based on the advertising signals they've received. 20%Derive a set of new features that will help better understand the interplay betwe...</code> |
+  | <code>Snowflake data warehousing, Python design patterns, AWS tools expertise</code>          | <code>Requirements:<br>- Good communication; and problem-solving abilities- Ability to work as an individual contributor; collaborating with Global team- Strong experience with Data Warehousing- OLTP, OLAP, Dimension, Facts, Data Modeling- Expertise implementing Python design patterns (Creational, Structural and Behavioral Patterns)- Expertise in Python building data application including reading, transforming; writing data sets- Strong experience in using boto3, pandas, numpy, pyarrow, Requests, Fast API, Asyncio, Aiohttp, PyTest, OAuth 2.0, multithreading, multiprocessing, snowflake python connector; Snowpark- Experience in Python building data APIs (Web/REST APIs)- Experience with Snowflake including SQL, Pipes, Stream, Tasks, Time Travel, Data Sharing, Query Optimization- Experience with Scripting language in Snowflake including SQL Stored Procs, Java Script Stored Procedures; Python UDFs- Understanding of Snowflake Internals; experience in integration with Reporting; UI applications- Stron...</code>                                        | <code>skills and ability to lead detailed data analysis meetings/discussions.<br><br>Ability to work collaboratively with multi-functional and cross-border teams.<br><br>Good English communication written and spoken.<br><br>Nice to have;<br><br>Material master create experience in any of the following areas;<br><br>SAP<br><br>GGSM<br><br>SAP Data Analyst, MN/Remote - Direct Client</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+  | <code>Cloud Data Engineering, Databricks Pyspark, Data Warehousing Design</code>              | <code>Experience of Delta Lake, DWH, Data Integration, Cloud, Design and Data Modelling. Proficient in developing programs in Python and SQLExperience with Data warehouse Dimensional data modeling. Working with event based/streaming technologies to ingest and process data. Working with structured, semi structured and unstructured data. Optimize Databricks jobs for performance and scalability to handle big data workloads. Monitor and troubleshoot Databricks jobs, identify and resolve issues or bottlenecks. Implement best practices for data management, security, and governance within the Databricks environment. Experience designing and developing Enterprise Data Warehouse solutions. Proficient writing SQL queries and programming including stored procedures and reverse engineering existing process. Perform code reviews to ensure fit to requirements, optimal execution patterns and adherence to established standards. <br><br>Requirements: <br><br>You are:<br><br>Minimum 9+ years of experience is required. 5+ years...</code>                         | <code>QualificationsExpert knowledge of using and configuring GCP (Vertex), AWS, Azure Python: 5+ years of experienceMachine Learning libraries: Pytorch, JaxDevelopment tools: Bash, GitData Science frameworks: DatabricksAgile Software developmentCloud Management: Slurm, KubernetesData Logging: Weights and BiasesOrchestration, Autoscaling: Ray, ClearnML, WandB etc.<br>Optional QualificationsExperience training LLMs and VLMsML for Robotics, Computer Vision etc.Developing Browser Apps/Dashboards, both frontend and backend Javascript, React, etc. Emancro is committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.</code>                                                                                                                                                                                                                                                             |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### ai-job-embedding-finetuning
+
+* Dataset: [ai-job-embedding-finetuning](https://huggingface.co/datasets/Mubin/ai-job-embedding-finetuning) at [b18b3c2](https://huggingface.co/datasets/Mubin/ai-job-embedding-finetuning/tree/b18b3c20bc31354d97bad62866da97618b6c13b7)
+* Size: 101 evaluation samples
+* Columns: <code>query</code>, <code>job_description_pos</code>, and <code>job_description_neg</code>
+* Approximate statistics based on the first 101 samples:
+  |         | query                                                                              | job_description_pos                                                                 | job_description_neg                                                                  |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              | string                                                                               |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 15.78 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 220.13 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 213.07 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | query                                                                                              | job_description_pos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | job_description_neg                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  |:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Big Data Engineer, Spark, Hadoop, AWS/GCP</code>                                             | <code>Skills • Expertise and hands-on experience on Spark, and Hadoop echo system components – Must Have • Good and hand-on experience* of any of the Cloud (AWS/GCP) – Must Have • Good knowledge of HiveQL & SparkQL – Must Have Good knowledge of Shell script & Java/Scala/python – Good to Have • Good knowledge of SQL – Good to Have • Good knowledge of migration projects on Hadoop – Good to Have • Good Knowledge of one of the Workflow engines like Oozie, Autosys – Good to Have Good knowledge of Agile Development– Good to Have • Passionate about exploring new technologies – Good to Have • Automation approach – Good to Have <br>Thanks & RegardsShahrukh KhanEmail: [email protected]</code>                                                                                                                                                                                                                                                                                                                                                          | <code>experience:<br><br>GS-14:<br><br>Supervisory/Managerial Organization Leadership<br><br>Supervises an assigned branch and its employees. The work directed involves high profile data science projects, programs, and/or initiatives within other federal agencies.Provides expert advice in the highly technical and specialized area of data science and is a key advisor to management on assigned/delegated matters related to the application of mathematics, statistical analysis, modeling/simulation, machine learning, natural language processing, and computer science from a data science perspective.Manages workforce operations, including recruitment, supervision, scheduling, development, and performance evaluations.Keeps up to date with data science developments in the private sector; seeks out best practices; and identifies and seizes opportunities for improvements in assigned data science program and project operations.<br><br><br>Senior Expert in Data Science<br><br>Recognized authority for scientific data analysis using advanc...</code>    |
+  | <code>Time series analysis, production operations, condition-based monitoring</code>               | <code>Experience in Production Operations or Well Engineering Strong scripting/programming skills (Python preferable)<br><br>Desired: <br><br> Strong time series surveillance background (eg. OSI PI, PI AF, Seeq) Strong scripting/programming skills (Python preferable) Strong communication and collaboration skills Working knowledge of machine learning application (eg. scikit-learn) Working knowledge of SQL and process historians Delivers positive results through realistic planning to accomplish goals Must be able to handle multiple concurrent tasks with an ability to prioritize and manage tasks effectively<br><br><br><br>Apex Systems is <br><br>Apex Systems is a world-class IT services company that serves thousands of clients across the globe. When you join Apex, you become part of a team that values innovation, collaboration, and continuous learning. We offer quality career resources, training, certifications, development opportunities, and a comprehensive benefits package. Our commitment to excellence is reflected in man...</code> | <code>Qualifications:· 3-5 years of experience as a hands-on analyst in an enterprise setting, leveraging Salesforce, Marketo, Dynamics, and similar tools.· Excellent written and verbal communication skills.· Experience with data enrichment processes and best practices.· Strong understanding of B2B sales & marketing for large, complex organizations.· Expertise in querying, manipulating, and analyzing data using SQL and/or similar languages.· Advanced Excel skills and experience with data platforms like Hadoop and Databricks.· Proven proficiency with a data visualization tool like Tableau or Power BI.· Strong attention to detail with data quality control and integration expertise.· Results-oriented, self-directed individual with multi-tasking, problem-solving, and independent learning abilities.· Understanding of CRM systems like Salesforce and Microsoft Dynamics.· Solid grasp of marketing practices, principles, KPIs, and data types.· Familiarity with logical data architecture and cloud data ...</code>                                     |
+  | <code>Senior Data Analyst jobs with expertise in Power BI, NextGen EHR, and enterprise ETL.</code> | <code>requirements.Reporting and Dashboard Development: Design, develop, and maintain reports for the HRSA HCCN Grant and other assignments. Create and maintain complex dashboards using Microsoft Power BI.Infrastructure Oversight: Monitor and enhance the data warehouse, ensuring efficient data pipelines and timely completion of tasks.Process Improvements: Identify and implement internal process improvements, including automating manual processes and optimizing data delivery.Troubleshooting and Maintenance: Address data inconsistencies using knowledge of various database structures and workflow best practices, including NextGen EHR system.Collaboration and Mentorship: Collaborate with grant PHCs and analytic teams, mentor less senior analysts, and act as a project lead for specific deliverables.<br>Experience:Highly proficient in SQL and experienced with reporting packages.Enterprise ETL experience is a major plus!data visualization tools (e.g., Tableau, Power BI, Qualtrics).Azure, Azure Data Fa...</code>                            | <code>Qualifications<br><br>3 to 5 years of experience in exploratory data analysisStatistics Programming, data modeling, simulation, and mathematics Hands on working experience with Python, SQL, R, Hadoop, SAS, SPSS, Scala, AWSModel lifecycle executionTechnical writingData storytelling and technical presentation skillsResearch SkillsInterpersonal SkillsModel DevelopmentCommunicationCritical ThinkingCollaborate and Build RelationshipsInitiative with sound judgementTechnical (Big Data Analysis, Coding, Project Management, Technical Writing, etc.)Problem Solving (Responds as problems and issues are identified)Bachelor's Degree in Data Science, Statistics, Mathematics, Computers Science, Engineering, or degrees in similar quantitative fields<br><br><br>Desired Qualification(s)<br><br>Master's Degree in Data Science, Statistics, Mathematics, Computer Science, or Engineering<br><br><br>Hours: Monday - Friday, 8:00AM - 4:30PM<br><br>Locations: 820 Follin Lane, Vienna, VA 22180 | 5510 Heritage Oaks Drive, Pensacola, FL 32526 | 141 Se...</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch | Step | ai-job-validation_cosine_accuracy | ai-job-test_cosine_accuracy |
+|:-----:|:----:|:---------------------------------:|:---------------------------:|
+| 0     | 0    | 0.9307                            | -                           |
+| 1.0   | 51   | 0.9703                            | 0.9804                      |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.3.1
+- Transformers: 4.47.1
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.2.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.47.1",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:daff92a6b8591c145e5c7081286dc06a035f5989ad8278ef5c97036caf20d53d
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}