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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:150 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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widget: |
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- source_sentence: Do you provide support 24/7? |
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sentences: |
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- 'How can we get started with your DevOps solutions? |
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Getting started is easy. Contact us through our website. We''ll schedule a consultation |
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to discuss your needs, evaluate your current infrastructure, and propose a customized |
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DevOps solution designed to achieve your goals.' |
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- 'This is our Portfolio |
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Introducing the world of Housing Finance& Banking Firm. |
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Corporate Website with 10 regional languages in India with analytics and user |
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personalization and Dashboard for Regional Managers, Sales Agents, etc. to manage |
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the Builder Requests, approve/deny Properties, manage visits and appointments, |
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manage leads, etc. |
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Introducing the world of Global Automotive Brand.We have implemented a Multi Locale |
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Multilingual Omnichannel platform for Royal Enfield. The platform supports public |
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websites, customer portals, internal portals, business applications for over 35+ |
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different locations all over the world. |
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Developed Digital Platform for Students, Guardians, Teachers, Tutors, with AI/ML |
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in collaboration with Successive Technologies Inc, USA. Cloud, Dev-Sec-Ops & |
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Data Governance |
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Managing cloud provisioning and modernization alongside automated infrastructure, |
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event-driven microservices, containerization, DevOps, cybersecurity, and 24x7 |
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monitoring support ensures efficient, secure, and responsive IT operations.' |
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- 'We are a New breed of innovative digital transformation agency, redefining storytelling |
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for an always-on world. |
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With roots dating back to 2017, we started as a pocket size team of enthusiasts |
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with a goal of helping traditional businesses transform and create dynamic, digital |
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cultures through disruptive strategies and agile deployment of innovative solutions.' |
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- source_sentence: What services do you offer for AI adoption? |
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sentences: |
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- 'In what ways can machine learning optimize our operations? |
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Machine learning algorithms can analyze operational data to identify inefficiencies, |
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predict maintenance needs, optimize supply chains, and automate repetitive tasks, |
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significantly improving operational efficiency and reducing costs.' |
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- "At Techchefz Digital, we specialize in guiding companies through the complexities\ |
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\ of adopting and integrating Artificial Intelligence and Machine Learning technologies.\ |
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\ Our consultancy services are designed to enhance your operational efficiency\ |
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\ and decision-making capabilities across all sectors. With a global network of\ |
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\ AI/ML experts and a commitment to excellence, we are your partners in transforming\ |
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\ innovative possibilities into real-world achievements. \ |
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\ \ |
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\ \n DATA INTELLIGENCE PLATFORMS we\ |
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\ specialize in\nTensorFlow\nDatabricks\nTableau\nPytorch\nOpenAI\nPinecone\"" |
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- "SERVICES WE PROVIDE\nFlexible engagement models tailored to your needs\nWe specialize\ |
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\ in comprehensive website audits that provide valuable insights and recommendations\ |
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\ to enhance your online presence.\nDigital Strategy & Consulting\nCreating digital\ |
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\ roadmap that transform your digital enterprise and produce a return on investment,\ |
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\ basis our discovery framework, brainstorming sessions & current state analysis.\n\ |
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\nPlatform Selection\nHelping you select the optimal digital experience, commerce,\ |
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\ cloud and marketing platform for your enterprise.\n\nPlatform Builds\nDeploying\ |
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\ next-gen scalable and agile enterprise digital platforms, along with multi-platform\ |
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\ integrations. \nProduct Builds\nHelp you ideate, strategize, and engineer\ |
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\ your product with help of our enterprise frameworks\nInfrastructure\nSpecialize\ |
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\ in multi-cloud infrastructure helping you put forward the right cloud infrastructure\ |
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\ and optimization strategy.\n\nManaged Services\nOperate and monitor your business-critical\ |
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\ applications, data, and IT workloads, along with Application maintenance and\ |
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\ operations.\nTeam Augmentation\nHelp you scale up and augment your existing\ |
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\ team to solve your hiring challenges with our easy to deploy staff augmentation\ |
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\ offerings.\"" |
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- source_sentence: What challenges did the company face in its early days? |
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sentences: |
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- 'How do we do Custom Development ? |
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We follow below process to develop custom web or mobile Application on Agile Methodology, |
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breaking requirements in pieces and developing and shipping them with considering |
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utmost quality: |
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Requirements Analysis |
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We begin by understanding the client's needs and objectives for the website. |
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Identify key features, functionality, and any specific design preferences. |
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Project Planning |
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Then create a detailed project plan outlining the scope, timeline, and milestones. |
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Define the technology stack and development tools suitable for the project. |
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User Experience Design |
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Then comes the stage of Developing wireframes or prototypes to visualize the website's |
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structure and layout. We create a custom design that aligns with the brand identity |
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and user experience goals. |
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Development |
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After getting Sign-off on Design from Client, we break the requirements into Sprints |
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on Agile Methodology, and start developing them.' |
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- 'After a transformative scuba dive in the Maldives, Mayank Maggon made a pivotal |
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decision to depart from the corporate ladder in December 2016. Fueled by a clear |
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vision to revolutionize the digital landscape, Mayank set out to leverage the |
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best technology ingredients, crafting custom applications and digital ecosystems |
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tailored to clients'' specific needs, limitations, and budgets. |
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However, this solo journey was not without its challenges. Mayank had to initiate |
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the revenue engine by offering corporate trainings and conducting online batches |
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for tech training across the USA. He also undertook small projects and subcontracted |
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modules of larger projects for clients in the US, UK, and India. It was only after |
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this initial groundwork that Mayank was able to hire a group of interns, whom |
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he meticulously trained and groomed to prepare them for handling Enterprise Level |
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Applications. This journey reflects Mayank''s resilience, determination, and entrepreneurial |
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spirit in building TechChefz Digital from the ground up. |
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With a passion for innovation and a relentless drive for excellence, Mayank has |
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steered TechChefz Digital through strategic partnerships, groundbreaking projects, |
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and exponential growth. His leadership has been instrumental in shaping TechChefz |
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Digital into a leading force in the digital transformation arena, inspiring a |
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culture of innovation and excellence that continues to propel the company forward.' |
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- 'Our Solutions |
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Strategy & Digital Transformation |
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Innovate via digital transformation, modernize tech, craft product strategies, |
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enhance customer experiences, optimize data analytics, transition to cloud for |
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growth and efficiency |
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Product Engineering & Custom Development |
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Providing product development, enterprise web and mobile development, microservices |
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integrations, quality engineering, and application support services to drive innovation |
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and enhance operational efficiency.' |
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- source_sentence: What kind of data do you leverage for AI solutions? |
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sentences: |
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- 'In the Introducing the world of Global Insurance Firm, we crafted Effective Solutions |
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for Complex Problems and delieverd a comprehensive Website Development, Production |
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Support & Managed Services, we optimized customer journeys, integrate analytics, |
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CRM, ERP, and third-party applications, and implement cutting-edge technologies |
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for enhanced performance and efficiency |
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and achievied 200% Reduction in operational time & effort managing content & experience, |
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70% Reduction in Deployment Errors and Downtime, 2.5X Customer Engagement, Conversion |
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& Retention' |
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- 'Why do we need Microservices ? |
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Instead of building a monolithic application where all functionalities are tightly |
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integrated, microservices break down the system into modular and loosely coupled |
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services. |
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Scalability |
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Flexibility and Agility |
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Resilience and Fault Isolation |
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Technology Diversity |
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Continuous Delivery' |
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- Our AI/ML services pave the way for transformative change across industries, embodying |
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a client-focused approach that integrates seamlessly with human-centric innovation. |
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Our collaborative teams are dedicated to fostering growth, leveraging data, and |
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harnessing the predictive power of artificial intelligence to forge the next wave |
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of software excellence. We don't just deliver AI; we deliver the future. |
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- source_sentence: What do you guys do for digital strategy? |
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sentences: |
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- " What we do\n\nDigital Strategy\nCreating digital frameworks that transform\ |
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\ your digital enterprise and produce a return on investment.\n\nPlatform Selection\n\ |
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Helping you select the optimal digital experience, commerce, cloud and marketing\ |
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\ platform for your enterprise.\n\nPlatform Builds\nDeploying next-gen scalable\ |
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\ and agile enterprise digital platforms, along with multi-platform integrations.\n\ |
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\nProduct Builds\nHelp you ideate, strategize, and engineer your product with\ |
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\ help of our enterprise frameworks \n\nTeam Augmentation\nHelp you scale up and\ |
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\ augment your existing team to solve your hiring challenges with our easy to\ |
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\ deploy staff augmentation offerings .\nManaged Services\nOperate and monitor\ |
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\ your business-critical applications, data, and IT workloads, along with Application\ |
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\ maintenance and operations\n" |
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- "Introducing the world of\nGlobal Hospitality Firm\n\nIn this project, We focused\ |
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\ on strategizing CX, diverse platform dev, travel booking, indemnity journeys,\ |
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\ digital community, and managed services enhance travel experience and operational\ |
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\ efficiency. \nStrategizing & defining the Customer Experience across business\ |
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\ units and respective products / services,\nPlatform Development and Integrations\ |
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\ across different tech stacks - Drupal, Magento, MERN, Microservices, Canvas\ |
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\ LMS, OKTA SSO, AWS based Cloud Infrastructure, Build Automation\nTravel Packages\ |
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\ Booking Platform with payments, subscriptions, real time booking, etc\nIndemnity\ |
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\ & Self-Service Journeys\n\nAnd we achieved, 100% Improvement in Marketing Content,\ |
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\ Real Time Prices & Inventories delivery. 80% Increase in Customer Retention,175%\ |
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\ Increase in Partner & Vendor Operational Efficiency" |
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- 'Introducing the world of General Insurance Firm |
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In this project, we implemented Digital Solution and Implementation with Headless |
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Drupal as the CMS, and lightweight React JS (Next JS SSR on Node JS) with the |
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following features: |
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PWA & AMP based Web Pages |
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Page Speed Optimization |
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Reusable and scalable React JS / Next JS Templates and Components |
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Headless Drupal CMS with Content & Experience management, approval workflows, |
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etc for seamless collaboration between the business and marketing teams |
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Minimalistic Buy and Renewal Journeys for various products, with API integrations |
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and adherence to data compliances |
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We achieved 250% Reduction in Operational Time and Effort in managing the Content |
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& Experience for Buy & renew Journeys,220% Reduction in Customer Drops during |
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buy and renewal journeys, 300% Reduction in bounce rate on policy landing and |
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campaign pages' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.18666666666666668 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5866666666666667 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.68 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.18666666666666668 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.19555555555555554 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.13599999999999998 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07999999999999997 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.18666666666666668 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.5866666666666667 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.68 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.48942651032647805 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.38962962962962955 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.398026376123124 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.24 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5733333333333334 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6533333333333333 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
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value: 0.24 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1911111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13066666666666663 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07999999999999997 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.24 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5733333333333334 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6533333333333333 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
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value: 0.4991793077336057 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4047195767195766 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4124023465759078 |
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name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.21333333333333335 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5466666666666666 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6266666666666667 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7466666666666667 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.21333333333333335 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1822222222222222 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.12533333333333332 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07466666666666665 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.21333333333333335 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
|
value: 0.5466666666666666 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6266666666666667 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7466666666666667 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
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value: 0.4717065825983648 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.38359259259259254 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.39417579048787715 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.21333333333333335 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.52 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5733333333333334 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7066666666666667 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.21333333333333335 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1733333333333333 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.11466666666666667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07066666666666666 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.21333333333333335 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.52 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.5733333333333334 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7066666666666667 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.44415760022208445 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.36086772486772484 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.37364447853598953 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.14666666666666667 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5066666666666667 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6133333333333333 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.14666666666666667 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.13333333333333333 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.10133333333333334 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06133333333333333 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.14666666666666667 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.5066666666666667 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6133333333333333 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3595031317594935 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.27981481481481474 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.29776557642203677 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### 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': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, '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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Shashwat13333/bge-base-en-v1.5_v1") |
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# Run inference |
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sentences = [ |
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'What do you guys do for digital strategy?', |
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' What we do\n\nDigital Strategy\nCreating digital frameworks that transform your digital enterprise and produce a return on investment.\n\nPlatform Selection\nHelping you select the optimal digital experience, commerce, cloud and marketing platform for your enterprise.\n\nPlatform Builds\nDeploying next-gen scalable and agile enterprise digital platforms, along with multi-platform integrations.\n\nProduct Builds\nHelp you ideate, strategize, and engineer your product with help of our enterprise frameworks \n\nTeam Augmentation\nHelp you scale up and augment your existing team to solve your hiring challenges with our easy to deploy staff augmentation offerings .\nManaged Services\nOperate and monitor your business-critical applications, data, and IT workloads, along with Application maintenance and operations\n', |
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'Introducing the world of General Insurance Firm\nIn this project, we implemented Digital Solution and Implementation with Headless Drupal as the CMS, and lightweight React JS (Next JS SSR on Node JS) with the following features:\nPWA & AMP based Web Pages\nPage Speed Optimization\nReusable and scalable React JS / Next JS Templates and Components\nHeadless Drupal CMS with Content & Experience management, approval workflows, etc for seamless collaboration between the business and marketing teams\nMinimalistic Buy and Renewal Journeys for various products, with API integrations and adherence to data compliances\n\nWe achieved 250% Reduction in Operational Time and Effort in managing the Content & Experience for Buy & renew Journeys,220% Reduction in Customer Drops during buy and renewal journeys, 300% Reduction in bounce rate on policy landing and campaign pages', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
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|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.1867 | 0.24 | 0.2133 | 0.2133 | 0.1467 | |
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| cosine_accuracy@3 | 0.5867 | 0.5733 | 0.5467 | 0.52 | 0.4 | |
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| cosine_accuracy@5 | 0.68 | 0.6533 | 0.6267 | 0.5733 | 0.5067 | |
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| cosine_accuracy@10 | 0.8 | 0.8 | 0.7467 | 0.7067 | 0.6133 | |
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| cosine_precision@1 | 0.1867 | 0.24 | 0.2133 | 0.2133 | 0.1467 | |
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| cosine_precision@3 | 0.1956 | 0.1911 | 0.1822 | 0.1733 | 0.1333 | |
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| cosine_precision@5 | 0.136 | 0.1307 | 0.1253 | 0.1147 | 0.1013 | |
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| cosine_precision@10 | 0.08 | 0.08 | 0.0747 | 0.0707 | 0.0613 | |
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| cosine_recall@1 | 0.1867 | 0.24 | 0.2133 | 0.2133 | 0.1467 | |
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| cosine_recall@3 | 0.5867 | 0.5733 | 0.5467 | 0.52 | 0.4 | |
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| cosine_recall@5 | 0.68 | 0.6533 | 0.6267 | 0.5733 | 0.5067 | |
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| cosine_recall@10 | 0.8 | 0.8 | 0.7467 | 0.7067 | 0.6133 | |
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| **cosine_ndcg@10** | **0.4894** | **0.4992** | **0.4717** | **0.4442** | **0.3595** | |
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| cosine_mrr@10 | 0.3896 | 0.4047 | 0.3836 | 0.3609 | 0.2798 | |
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| cosine_map@100 | 0.398 | 0.4124 | 0.3942 | 0.3736 | 0.2978 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 150 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 150 samples: |
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| | anchor | positive | |
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|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 12.15 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 126.17 tokens</li><li>max: 378 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Is it hard to move old systems to the cloud?</code> | <code>We offer custom software development, digital marketing strategies, and tailored solutions to drive tangible results for your business. Our expert team combines technical prowess with industry insights to propel your business forward in the digital landscape.<br><br>"Engage, analyze & target your customers<br>Digital transformation enables you to interact with customers across multiple channels, providing personalized experiences. This could include social media engagement, interactive websites, and mobile apps." "Empower your employees & partners<br>The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges." "Optimize & automate your operations<br>The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges." "Transform your products<br>The push for digi...</code> | |
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| <code>What benefits does marketing automation offer for time management?</code> | <code>Our MarTech capabilities<br><br>Personalization<br>Involves tailoring marketing messages and experiences to individual customers. It enhances customer engagement, loyalty, and ultimately, conversion rates.<br><br>Marketing Automation<br>Marketing automation streamlines repetitive tasks such as email marketing, lead nurturing, and social media posting. It improves efficiency, saves time, and ensures timely communication with customers.<br><br>Customer Relationship Management<br>CRM systems help manage interactions with current and potential customers. They store customer data, track interactions, and facilitate communication, improving customer retention.</code> | |
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| <code>do you track customer behavior?</code> | <code>How can your recommendation engines improve our business?<br>Our recommendation engines are designed to analyze customer behavior and preferences to deliver personalized suggestions, enhancing user experience, increasing sales, and boosting customer retention.</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `gradient_accumulation_steps`: 4 |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `push_to_hub`: True |
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- `hub_model_id`: Shashwat13333/bge-base-en-v1.5_v1 |
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- `push_to_hub_model_id`: bge-base-en-v1.5_v1 |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 8 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 4 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: True |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: Shashwat13333/bge-base-en-v1.5_v1 |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: bge-base-en-v1.5_v1 |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
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| 0.2105 | 1 | 22.6183 | - | - | - | - | - | |
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| 0.8421 | 4 | - | 0.4602 | 0.4392 | 0.4498 | 0.4162 | 0.3698 | |
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| 1.2105 | 5 | 20.549 | - | - | - | - | - | |
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| 1.8421 | 8 | - | 0.5047 | 0.4304 | 0.4538 | 0.4202 | 0.3458 | |
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| 2.4211 | 10 | 17.664 | - | - | - | - | - | |
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| **2.8421** | **12** | **-** | **0.482** | **0.4618** | **0.4658** | **0.4537** | **0.3496** | |
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| 3.6316 | 15 | 14.6735 | - | - | - | - | - | |
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| 3.8421 | 16 | - | 0.4894 | 0.4992 | 0.4717 | 0.4442 | 0.3595 | |
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* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.11.11 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.2 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.3.0 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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|
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## Citation |
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|
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### BibTeX |
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|
|
#### Sentence Transformers |
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```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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|
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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<!-- |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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<!-- |
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## Model Card Contact |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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--> |
