--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:999 - loss:CosineSimilarityLoss base_model: manu/bge-m3-custom-fr widget: - source_sentence: Marketing with TikTok sentences: - "In this Specialization, we show you techniques to increase price realization\ \ and maximize profits. Learn from Boston Consulting Group's global pricing experts\ \ and University of Virginia Darden School of Business faculty, who share the\ \ frameworks, tips and tools we use in our business and research environments.\ \ We will look at pricing through BCG's proprietary and time-tested three “lenses”—cost\ \ and economics, customer value, and competition—to build your understanding of\ \ the strategic power of pricing. You’ll leave the Specialization with a portfolio-building\ \ presentation that demonstrates your ability to price strategically.\nApplied\ \ Learning Project\nUtilizing the concepts, tools and techniques taught in each\ \ course—from basic techniques of economics to knowledge of customer segments,\ \ willingness to pay, and customer decision making to analysis of market prices,\ \ share, and industry dynamics—you will practice setting profit maximizing prices\ \ to improve price realization in a variety of real-world scenarios. \n Beginner" - "This program is for anyone asking, “Is TikTok right for my business?” In the\ \ first course, Understanding TikTok and Its Users, you’ll be introduced to the\ \ TikTok platform, including who’s on it and what they’re creating. Then in the\ \ second course, Marketing on TikTok, you'll learn how businesses use TikTok,\ \ how they tailor content to the platform, and how they find an audience. In the\ \ final course, Advertising and E-commerce on TikTok, you’ll learn how to create\ \ a marketing strategy for TikTok, how to create advertising campaigns, how to\ \ promote and sell products via organic and e-commerce channels like Shopify,\ \ and how to use analytics to inform future marketing strategies. Throughout the\ \ program, you will have the chance to create your own videos, as well as engage\ \ with what other businesses are doing by studying various TikTok accounts in\ \ order to get inspiration and insight for your own future interactions with TikTok.\n\ By the end of this program, you will be able to: \nCreate fun and engaging TikTok\ \ videos\nUnderstand how TikTok fits into your business’s marketing strategy\n\ Articulate how a number of different businesses use TikTok\nCreate original content\ \ using hashtags, trends, filters, and more\nBuild and maintain a brand presence\ \ on TikTok\nCreate a content calendar\nUtilize TikTok Ads Manager to create and\ \ execute a campaign\nSet up e-commerce integration into your TikTok account\n\ Analyze data from your efforts to inform your future content strategy\n\nApplied\ \ Learning Project\nLearners will practice creating advertisements on TikTok through\ \ guided simulations. These simulations recreate the TikTok advertising platform\ \ and allow learners to engage with the platform in a practice environment before\ \ creating ads with their own accounts.\n Beginner" - "In this course, you will explore the foundations upon which modern-day ESG was\ \ built, how market forces react to ESG, and ways to create and maintain value\ \ using ESG investment strategies. You will also learn about the five pathways\ \ of materiality, and how those interplay with or against ESG performance.\nYou\ \ will examine the many challenges that corporations face when it comes to leveraging\ \ ESG investing into their portfolios, and how the changing landscape of ESG is\ \ making this an area of untapped potential when it comes to the financial workings\ \ of businesses today. You'll also learn from real-life case studies how you can\ \ assess risk, create better risk management policy, and build a map to identify\ \ valuable areas of opportunity and create better decision-making approaches.\ \ Lastly, you will look at portfolio optimization and the utilization of ESG factors\ \ to maximize returns in addition to examining different funds, their fee structures,\ \ and how investors can blend ESG into their investment portfolio.\n\nBy the end\ \ of this course, you will know the best practices for creating a solid risk management\ \ plan and how to create a culture that is sensitive to ESG. You will better understand\ \ the history and framework behind ESG, and how to create a path forward using\ \ smarter methods to identify risk, navigate ESG issues, and reach ESG investing\ \ goals.\n Intermediate" - source_sentence: AWS Cloud Technical Essentials sentences: - "Are you in a technical role and want to learn the fundamentals of AWS? Do you\ \ aspire to have a job or career as a cloud developer, architect, or in an operations\ \ role? If so, AWS Cloud Technical Essentials is an ideal way to start. This course\ \ was designed for those at the beginning of their cloud-learning journey - no\ \ prior knowledge of cloud computing or AWS products and services required!\n\ Throughout the course, students will build highly available, scalable, and cost\ \ effective application step-by-step. Upon course completion, you will be able\ \ to make an informed decision about when and how to apply core AWS services for\ \ compute, storage, and database to different use cases. You’ll also learn about\ \ cloud security with a review of AWS' shared responsibility model and an introduction\ \ to AWS Identity and Access Management (IAM). And, you’ll know how AWS services\ \ can be used to monitor and optimize infrastructure in the cloud.\n\nAWS Cloud\ \ Technical Essentials is a fundamental-level course and will build your competence,\ \ confidence, and credibility with practical cloud skills that help you innovate\ \ and advance your professional future. Enroll in AWS Cloud Technical Essentials\ \ and start learning the technical fundamentals of AWS today!\n\nNote: This course\ \ was designed for learners with a technical background. If you are new to the\ \ cloud or come from a business background, we recommend completing AWS Cloud\ \ Practitioner Essentials (https://www.coursera.org/learn/aws-cloud-practitioner-essentials)\ \ before enrolling in this course.\n Beginner" - "In a 2018 survey of businesses, Buffer found that only 29% had effective social\ \ media marketing programs. A recent survey of consumers by Tomoson found 92%\ \ of consumers trust recommendations from other people over brand content, 70%\ \ found consumer reviews to be their second most trusted source, 47% read blogs\ \ developed by influencers and experts to discover new trends and new ideas and\ \ 35% used blogs to discover new products and services. Also, 20% of women who\ \ used social considered products promoted by bloggers they knew. Today, businesses\ \ and consumers use social media to make their purchase decisions.\nCreated in\ \ 2014, this Specialization is updated every quarter to ensure you are receiving\ \ the most up-to-date training. The Social Media Marketing Specialization is\ \ designed to achieve two objectives. It gives you the social analytics tools,\ \ and training to help you become an influencer on social media. The course also\ \ gives you the knowledge and resources to build a complete social media marketing\ \ strategy – from consumer insights to final justification metrics. In each course,\ \ you will also receive special toolkits with timely information & when you pay\ \ for the Capstone, you receive a market planning toolkit.\nEach of the individual\ \ courses can be audited for free. To see more, visit each course: 1-What is Social?Opens\ \ in a new tab, 2-The Importance of ListeningOpens in a new tab, 3-Engagement\ \ & Nurture Marketing StrategiesOpens in a new tab, 4-Content, Advertising & Social\ \ IMCOpens in a new tab, 5-The Business of SocialOpens in a new tab.\n Beginner" - "This specialization will provide learners with the fundamentals and history of\ \ ESG investing, and a close examination of the set of investment approaches that\ \ are informed by environmental, social and, governance factors. You'll about\ \ the five pathways of materiality, and how those interplay with or against ESG\ \ performance. You'll review the concepts of positive and negative screening and\ \ identify the ESG factors that cause investors to divest from or negatively screen\ \ certain assets. \nNext, you'll review private environmental governance, the\ \ active role that private companies are playing in combating climate change,\ \ and the parallels between the public and private sectors. Lastly, you'll evaluate\ \ the effectiveness of corporate authenticity and the impacts of politics when\ \ building corporate ESG policy, and the importance of creating independent Directors\ \ to maintain neutrality and protect stakeholder interests. \nBy the end of this\ \ specialization, you will know the best practices for creating a solid risk management\ \ plan, analyzed the complex indexing and measurement techniques employed in the\ \ ESG space, examined climate disclosures and implementation of climate solutions,\ \ and how social activism affects the corporate world in the 21st century. \n\ Applied Learning Project\nEach course module in this Specialization culminates\ \ in an assessment. These assessments are designed to check learners' knowledge\ \ and to provide an opportunity for learners to apply course concepts such as\ \ investment analytics, indexing tools, and critical analysis of ESG policies,\ \ activism, and greenwashing.\nThe assessments will be cumulative and cover the\ \ fundamentals of ESG investing, public and private governance, and sustainability\ \ factors that will allow you to build a diverse and risk-resilient ESG portfolio.\n\ \ Beginner" - source_sentence: Six Sigma Yellow Belt sentences: - "This specialization gives current or aspiring IT professionals an overview of\ \ the features, benefits, and capabilities of Amazon Web Services (AWS). As you\ \ proceed through these four interconnected courses, you will gain a more vivid\ \ understanding of core AWS services, key AWS security concepts, strategies for\ \ migrating from on-premises to AWS, and basics of building serverless applications\ \ with AWS. Additionally, you will have opportunities to practice what you have\ \ learned by completing labs and exercises developed by AWS technical instructors.\n\ Applied Learning Project\nThis specialization gives current or aspiring IT professionals\ \ an overview of the features, benefits, and capabilities of Amazon Web Services\ \ (AWS). As you proceed through these four interconnected courses, you will gain\ \ a more vivid understanding of core AWS services, key AWS security concepts,\ \ strategies for migrating from on-premises to AWS, and basics of building serverless\ \ applications with AWS. Additionally, you will have opportunities to practice\ \ what you have learned by completing labs and exercises developed by AWS technical\ \ instructors.\n Beginner" - "This specialization is for you if you are looking to learn more about Six Sigma\ \ or refresh your knowledge of the basic components of Six Sigma and Lean. Six\ \ Sigma skills are widely sought by employers both nationally and internationally.\ \ These skills have been proven to help improve business processes, performance,\ \ and quality assurance. \nIn this specialization, you will learn proven principles\ \ and tools specific to six sigma and lean.\nThis is a sequential, linear designed\ \ specialization that covers the introductory level content (at the \"yellow belt\"\ \ level) of Six Sigma and Lean. Yellow Belt knowledge is needed before advancing\ \ to Green Belt (which is a second specialization offered here on Coursera by\ \ the USG). Green Belt knowledge is needed before moving to a Black Belt.\nThe\ \ proper sequence of this specialization is:\nCourse #1 - Six Sigma Fundamentals\n\ Course #2 - Six Sigma Tools for Define and Measure\nCourse #3 - Six Sigma Tools\ \ for Analyze\nCourse #4 - Six Sigma Tools for Improve and Control\nAt the end\ \ of Course #4 (Six Sigma Tools for Improve and Control), there is a peer-reviewed,\ \ capstone project. Successful completion of this project is necessary for full\ \ completion of this specialization.\nIt should be noted that completing either\ \ the Yellow Belt or Green Belt Specializations does not give the learner \"professional\ \ accreditation\" in Six Sigma. However, successful completion will assist in\ \ better preparation for such professional accreditation testing.\nApplied Learning\ \ Project\nUpon completion of this specialization, learners will have created\ \ a project charter, project team charter, data collection plan, process map,\ \ along with null and alternative hypotheses, problem statement, business case,\ \ goal statement, process and scope description, and timeline. All these will\ \ be created using the six sigma principles and tools they learned.\n Beginner" - "Around the world, we find ourselves facing global epidemics of obesity, Type\ \ 2 Diabetes and other predominantly diet-related diseases. To address these public\ \ health crises, we urgently need to explore innovative strategies for promoting\ \ healthful eating. There is strong evidence that global increases in the consumption\ \ of heavily processed foods, coupled with cultural shifts away from the preparation\ \ of food in the home, have contributed to high rates of preventable, chronic\ \ disease. In this course, learners will be given the information and practical\ \ skills they need to begin optimizing the way they eat. This course will shift\ \ the focus away from reductionist discussions about nutrients and move, instead,\ \ towards practical discussions about real food and the environment in which we\ \ consume it. By the end of this course, learners should have the tools they need\ \ to distinguish between foods that will support their health and those that threaten\ \ it. In addition, we will present a compelling rationale for a return to simple\ \ home cooking, an integral part of our efforts to live longer, healthier lives.\n\ View the trailer for the course here: https://www.youtube.com/watch?v=z7x1aaZ03xU\n\ \ Beginner" - source_sentence: Goodwill® Career Coach and Navigator sentences: - "Perhaps the most important thing students and professionals of all kinds can\ \ do to improve their effectiveness is embrace the following advice: become good\ \ with words.\nThis series of courses targets the writing side of that recommendation.\ \ The skills it focuses on include everything from how to arrange a complex set\ \ of information in a reader-friendly way, to how to give and receive high-quality\ \ feedback, to how to consistently hit deadlines. \nPart of Professor Barry’s\ \ proceeds from this course will be donated to the COVID-19 relief efforts of\ \ Ozone HouseOpens in a new tab, a shelter for homeless youth in Southeastern\ \ Michigan where he regularly conducts job-training workshops. These proceeds\ \ come from purchases of the version of the course that earns you a certificate.\ \ The course remains free for anyone who is simply auditing. \n Beginner" - "Kickstart your learning of Python with this beginner-friendly self-paced course\ \ taught by an expert. Python is one of the most popular languages in the programming\ \ and data science world and demand for individuals who have the ability to apply\ \ Python has never been higher. \nThis introduction to Python course will take\ \ you from zero to programming in Python in a matter of hours—no prior programming\ \ experience necessary! You will learn about Python basics and the different data\ \ types. You will familiarize yourself with Python Data structures like List and\ \ Tuples, as well as logic concepts like conditions and branching. You will use\ \ Python libraries such as Pandas, Numpy & Beautiful Soup. You’ll also use Python\ \ to perform tasks such as data collection and web scraping with APIs. \n\nYou\ \ will practice and apply what you learn through hands-on labs using Jupyter Notebooks.\ \ By the end of this course, you’ll feel comfortable creating basic programs,\ \ working with data, and automating real-world tasks using Python. \n\nThis course\ \ is suitable for anyone who wants to learn Data Science, Data Analytics, Software\ \ Development, Data Engineering, AI, and DevOps as well as a number of other job\ \ roles.\n Beginner" - "This is the sixth course in the Google Data Analytics Certificate. These courses\ \ will equip you with the skills needed to apply to introductory-level data analyst\ \ jobs. You’ll learn how to visualize and present your data findings as you complete\ \ the data analysis process. This course will show you how data visualizations,\ \ such as visual dashboards, can help bring your data to life. You’ll also explore\ \ Tableau, a data visualization platform that will help you create effective visualizations\ \ for your presentations. Current Google data analysts will continue to instruct\ \ and provide you with hands-on ways to accomplish common data analyst tasks with\ \ the best tools and resources.\nLearners who complete this certificate program\ \ will be equipped to apply for introductory-level jobs as data analysts. No previous\ \ experience is necessary.\n\nBy the end of this course, you will:\n - Examine\ \ the importance of data visualization.\n - Learn how to form a compelling narrative\ \ through data stories.\n - Gain an understanding of how to use Tableau to create\ \ dashboards and dashboard filters.\n - Discover how to use Tableau to create\ \ effective visualizations. \n - Explore the principles and practices involved\ \ with effective presentations.\n - Learn how to consider potential limitations\ \ associated with the data in your presentations.\n - Understand how to apply\ \ best practices to a Q&A with your audience.\n Beginner" - source_sentence: Machine Learning Engineering for Production (MLOps) sentences: - "The future of making is here, bringing with it radical changes in the way things\ \ are designed, made, and used. And it’s disrupting every industry. With the right\ \ knowledge and tools, this disruption is your opportunity—whether you're an entrepreneur,\ \ designer, or engineer. \nToday’s dominant technology trends—cloud computing,\ \ mobile technology, social connection, and collaboration—are driving businesses\ \ and consumers alike to explore profoundly different ways to design, make, and\ \ use things. This kind of industry transformation has happened before, but the\ \ pace of change is now much faster. In today’s competitive landscape, anyone\ \ can be an innovator—and it’s all about who innovates first. \nThrough this specialization,\ \ you will learn the foundations of product innovation and digital manufacturing\ \ while developing your technical skills within Autodesk® Fusion 360™.\nPlus,\ \ by completing this Specialization, you’ll unlock an Autodesk Credential as further\ \ recognition of your success! The Autodesk Credential comes with a digital badge\ \ and certificate, which you can add to your resume and share on social media\ \ platforms like LinkedIn, Facebook, and Twitter. Sharing your Autodesk Credential\ \ can signal to hiring managers that you’ve got the right skills for the job and\ \ you’re up on the latest industry trends like digital manufacturing.\nLooking\ \ for Autodesk Fusion 360 certification prep courses? Check out additional learning\ \ resourcesOpens in a new tab to help you uplevel your skills.\nApplied Learning\ \ Project\nLearners will create a search and rescue drone inspired by the XVEIN\ \ droneOpens in a new tab designed by student makers Yuki Ogasawara and Ryo Kumeda.\ \ These project-based courses offer learners the opportunity to apply their Autodesk®\ \ Fusion 360™ skills, and encourages them to explore new approaches to customizing\ \ their own drone design for manufacture.\n Beginner" - "Este certificado de cinco cursos, desenvolvido pelo Google, inclui um currículo\ \ inovador projetado para prepará-lo para uma função de nível básico em suporte\ \ de TI. Uma posição na área de TI pode ser um serviço de apoio pessoalmente ou\ \ remoto em uma pequena empresa ou em uma empresa global como o Google. Se você\ \ já lida com TI por algum tempo, ou é novo no campo, você veio ao lugar certo.\ \ O programa faz parte do Cresça com o Google, uma iniciativa do Google para ajudar\ \ a criar oportunidades econômicas.\nAtravés de uma mistura de palestras em vídeo,\ \ questionários e laboratórios e widgets práticos, o programa apresentará soluções\ \ de problemas e atendimento ao cliente, redes, sistemas operacionais, administração\ \ de sistemas e segurança. Ao longo do caminho, você aprenderá de Googlers com\ \ históricos exclusivos cuja base no suporte de TI serviu como um ponto de partida\ \ para suas carreiras.\nAo dedicar 5 horas por semana, você pode concluir o certificado\ \ em cerca de seis meses. Você pode pular o conteúdo que já sabe e fazer mais\ \ cedo os exames de avaliação.\nO conteúdo do Certificado Profissional de Suporte\ \ em TI do Google está sob a Licença Internacional de Atribuição 4.0 da Creative\ \ Commons.\n75% dos alunos que obtêm os Certificados do Google nos Estados Unidos\ \ relatam uma melhora em suas carreiras dentro de um intervalo de 6 meses após\ \ a obtenção da certificação.\nFonte: *baseado nas respostas de pesquisa com os\ \ graduados pelo programa, Estados Unidos, 2021\nApplied Learning Project\nEste\ \ certificado de cinco cursos, desenvolvido pelo Google, inclui um currículo inovador\ \ projetado para prepará-lo para uma função de nível básico em suporte de TI.\ \ Uma posição na área de TI pode ser um trabalho de serviço de apoio em pessoa\ \ ou remoto em uma pequena empresa ou em uma empresa global como o Google. Se\ \ você já lida com TI por algum tempo, ou é novo no campo, você veio ao lugar\ \ certo. O programa faz parte do Grow with Google, uma iniciativa do Google para\ \ ajudar a criar oportunidades econômicas.\n Beginner" - "Understanding machine learning and deep learning concepts is essential, but if\ \ you’re looking to build an effective AI career, you need production engineering\ \ capabilities as well. \nEffectively deploying machine learning models requires\ \ competencies more commonly found in technical fields such as software engineering\ \ and DevOps. Machine learning engineering for production combines the foundational\ \ concepts of machine learning with the functional expertise of modern software\ \ development and engineering roles. \nThe Machine Learning Engineering for Production\ \ (MLOps) Specialization covers how to conceptualize, build, and maintain integrated\ \ systems that continuously operate in production. In striking contrast with standard\ \ machine learning modeling, production systems need to handle relentless evolving\ \ data. Moreover, the production system must run non-stop at the minimum cost\ \ while producing the maximum performance. In this Specialization, you will learn\ \ how to use well-established tools and methodologies for doing all of this effectively\ \ and efficiently.\nIn this Specialization, you will become familiar with the\ \ capabilities, challenges, and consequences of machine learning engineering in\ \ production. By the end, you will be ready to employ your new production-ready\ \ skills to participate in the development of leading-edge AI technology to solve\ \ real-world problems.\nApplied Learning Project\nBy the end, you'll be ready\ \ to\n• Design an ML production system end-to-end: project scoping, data needs,\ \ modeling strategies, and deployment requirements\n• Establish a model baseline,\ \ address concept drift, and prototype how to develop, deploy, and continuously\ \ improve a productionized ML application\n• Build data pipelines by gathering,\ \ cleaning, and validating datasets\n• Implement feature engineering, transformation,\ \ and selection with TensorFlow Extended\n• Establish data lifecycle by leveraging\ \ data lineage and provenance metadata tools and follow data evolution with enterprise\ \ data schemas\n• Apply techniques to manage modeling resources and best serve\ \ offline/online inference requests\n• Use analytics to address model fairness,\ \ explainability issues, and mitigate bottlenecks\n• Deliver deployment pipelines\ \ for model serving that require different infrastructures\n• Apply best practices\ \ and progressive delivery techniques to maintain a continuously operating production\ \ system\n Advanced" pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on manu/bge-m3-custom-fr This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [manu/bge-m3-custom-fr](https://huggingface.co/manu/bge-m3-custom-fr). It maps sentences & paragraphs to a 1024-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:** [manu/bge-m3-custom-fr](https://huggingface.co/manu/bge-m3-custom-fr) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id") # Run inference sentences = [ 'Machine Learning Engineering for Production (MLOps)', "Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. \nEffectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. \nThe Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.\nIn this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.\nApplied Learning Project\nBy the end, you'll be ready to\n• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements\n• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application\n• Build data pipelines by gathering, cleaning, and validating datasets\n• Implement feature engineering, transformation, and selection with TensorFlow Extended\n• Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas\n• Apply techniques to manage modeling resources and best serve offline/online inference requests\n• Use analytics to address model fairness, explainability issues, and mitigate bottlenecks\n• Deliver deployment pipelines for model serving that require different infrastructures\n• Apply best practices and progressive delivery techniques to maintain a continuously operating production system\n Advanced", 'Este certificado de cinco cursos, desenvolvido pelo Google, inclui um currículo inovador projetado para prepará-lo para uma função de nível básico em suporte de TI. Uma posição na área de TI pode ser um serviço de apoio pessoalmente ou remoto em uma pequena empresa ou em uma empresa global como o Google. Se você já lida com TI por algum tempo, ou é novo no campo, você veio ao lugar certo. O programa faz parte do Cresça com o Google, uma iniciativa do Google para ajudar a criar oportunidades econômicas.\nAtravés de uma mistura de palestras em vídeo, questionários e laboratórios e widgets práticos, o programa apresentará soluções de problemas e atendimento ao cliente, redes, sistemas operacionais, administração de sistemas e segurança. Ao longo do caminho, você aprenderá de Googlers com históricos exclusivos cuja base no suporte de TI serviu como um ponto de partida para suas carreiras.\nAo dedicar 5 horas por semana, você pode concluir o certificado em cerca de seis meses. Você pode pular o conteúdo que já sabe e fazer mais cedo os exames de avaliação.\nO conteúdo do Certificado Profissional de Suporte em TI do Google está sob a Licença Internacional de Atribuição 4.0 da Creative Commons.\n75% dos alunos que obtêm os Certificados do Google nos Estados Unidos relatam uma melhora em suas carreiras dentro de um intervalo de 6 meses após a obtenção da certificação.\nFonte: *baseado nas respostas de pesquisa com os graduados pelo programa, Estados Unidos, 2021\nApplied Learning Project\nEste certificado de cinco cursos, desenvolvido pelo Google, inclui um currículo inovador projetado para prepará-lo para uma função de nível básico em suporte de TI. Uma posição na área de TI pode ser um trabalho de serviço de apoio em pessoa ou remoto em uma pequena empresa ou em uma empresa global como o Google. Se você já lida com TI por algum tempo, ou é novo no campo, você veio ao lugar certo. O programa faz parte do Grow with Google, uma iniciativa do Google para ajudar a criar oportunidades econômicas.\n Beginner', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 999 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 999 samples: | | sentence_0 | sentence_1 | label | |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:---------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | AI Applications in Marketing and Finance | In this course, you will learn about AI-powered applications that can enhance the customer journey and extend the customer lifecycle. You will learn how this AI-powered data can enable you to analyze consumer habits and maximize their potential to target your marketing to the right people. You will also learn about fraud, credit risks, and how AI applications can also help you combat the ever-challenging landscape of protecting consumer data. You will also learn methods to utilize supervised and unsupervised machine learning to enhance your fraud detection methods. You will also hear from leading industry experts in the world of data analytics, marketing, and fraud prevention. By the end of this course, you will have a substantial understanding of the role AI and Machine Learning play when it comes to consumer habits, and how we are able to interact and analyze information to increase deep learning potential for your business.
Mixed | 1.0 | | Business Strategies for A Better World | In this Specialization, you’ll develop basic literacy in the language of business, which you can use to transition to a new career, start or improve your own small business, or apply to business school to continue your education. In five courses, you’ll learn the fundamentals of marketing, accounting, operations, and finance. In the final Capstone Project, you’ll apply the skills learned by developing a go-to-market strategy to address a real business challenge.
Beginner | 1.0 | | Financial Acumen for Non-Financial Managers | In Finance for Technical Managers, you will explore the fundamental principles of financial management. Topics include understanding and interpreting a company’s financial statements, the time value of money and its role in evaluating the economic viability of different projects, and the annual capital budgeting process every company performs when selecting which projects to fund. In addition, you will cover some highly practical topics, such as how to determine product costs, establishing a department’s annual budget, and ways of forecasting future sales. As a side benefit, the quantitative skills you will learn for business are identical to the skills necessary to manage your own personal finances. Therefore, you will extend your analyses to cover investing of mutual funds composed of stocks and bonds, and you will explore the fascinating area of asset allocation.
This specialization can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Managem... | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `num_train_epochs`: 2 - `fp16`: True - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `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`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: True - `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`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | |:-----:|:----:|:-------------:| | 2.0 | 500 | 0.013 | ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 3.4.1 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## 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", } ```