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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:154 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/msmarco-distilbert-base-v4 |
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widget: |
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- source_sentence: Hey, what career oppotunities do you provide? |
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sentences: |
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- TechChefz Digital is present in two countries. Its headquarters is in Noida, India, |
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with additional offices in Delaware, United States, and Gauram Nagar, Delhi, India. |
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- 'Customer Experience & Marketing Technology |
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Covering journey science, content architecture, personalization, campaign management, |
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and conversion rate optimization, driving customer experiences and engagements |
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Enterprise Platforms & Systems Integration |
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Platform selection services in CMS, e-commerce, and learning management systems, |
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with a focus on marketplace commerce |
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Analytics, Data Science & Business Intelligence |
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Engage in analytics, data science, and machine learning to derive insights. Implement |
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intelligent search, recommendation engines, and predictive models for optimization |
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and enhanced decision-making. TechChefz Digital seeks passionate individuals to |
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join our innovative team. We offer dynamic work environments fostering creativity |
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and expertise. Whether you''re seasoned or fresh, exciting career opportunities |
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await in technology, consulting, design, and more. Join us in shaping digital |
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transformation and unlocking possibilities for clients and the industry. |
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7+ Years Industry Experience |
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300+ Enthusiasts |
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80% Employee Retention Rate |
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' |
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- 'How long does it take to develop an e-commerce website? |
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The development time for an e-commerce website can vary widely depending on its |
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complexity, features, and the platform chosen. A basic online store might take |
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a few weeks to set up, while a custom, feature-rich site could take several months |
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to develop. Clear communication of your requirements and timely decision-making |
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can help streamline the process.' |
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- source_sentence: What technologies are used for web development? |
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sentences: |
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- 'Our Featured Insights |
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Simplifying Image Loading in React with Lazy Loading and Intersection Observer |
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API |
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What Is React Js? |
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The Role of Artificial Intelligence (AI) in Personalizing Digital Marketing Campaigns |
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Mastering Personalization in Digital Marketing: Tailoring Campaigns for Success |
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How Customer Experience Drives Your Business Growth |
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Which is the best CMS for your Digital Transformation Journey? |
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The Art of Test Case Creation Templates' |
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- 'DISCOVER TECHSTACK |
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Empowering solutions |
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with cutting-edge technology stacks |
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Web & Mobile Development |
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Crafting dynamic and engaging online experiences tailored to your brand''s vision |
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and objectives. |
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Content Management Systems |
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3D, AR & VR |
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Learning Management System |
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Commerce |
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Analytics |
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Personalization & Marketing Cloud |
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Cloud & DevSecOps |
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Tech Stack |
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HTML, JS, CSS |
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React JS |
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Angular JS |
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Vue JS |
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Next JS |
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React Native |
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Flutter |
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Node JS |
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Python |
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Frappe |
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Java |
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Spring Boot |
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Go Lang |
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Mongo DB |
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PostgreSQL |
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MySQL' |
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- 'Can you help migrate our existing infrastructure to a DevOps model? |
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Yes, we specialize in transitioning traditional IT infrastructure to a DevOps |
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model. Our process includes assessing your current setup, planning the migration, |
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implementing the necessary tools and practices, and providing ongoing support |
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to ensure a smooth transition.' |
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- source_sentence: Where is TechChefz based? |
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sentences: |
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- 'CLIENT TESTIMONIALS |
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Worked with TCZ on two business critical website development projects. The TCZ |
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team is a group of experts in their respective domains and have helped us with |
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excellent end-to-end development of a website right from the conceptualization |
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to implementation and maintenance. By Dr. Kunal Joshi - Healthcare Marketing & |
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Strategy Professional |
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TCZ helped us with our new website launch in a seamless manner. Through all our |
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discussions, they made sure to have the website designed as we had envisioned |
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it to be. Thank you team TCZ. |
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By Dr. Sarita Ahlawat - Managing Director and Co-Founder, Botlab Dynamics ' |
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- TechChefz Digital is present in two countries. Its headquarters is in Noida, India, |
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with additional offices in Delaware, United States, and Gauram Nagar, Delhi, India. |
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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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- source_sentence: Will you assess our current infrastructure before migrating? |
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sentences: |
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- 'Introducing the world of Global EdTech Firm. |
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In this project, We implemented a comprehensive digital platform strategy to unify |
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user experience across platforms, integrating diverse tech stacks and specialized |
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platforms to enhance customer engagement and streamline operations. |
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Develop tailored online tutoring and learning hub platforms, leveraging AI/ML |
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for personalized learning experiences, thus accelerating user journeys and improving |
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conversion rates. |
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Provide managed services for seamless application support and platform stabilization, |
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optimizing operational efficiency and enabling scalable B2B subscriptions for |
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schools and districts, facilitating easy onboarding and growth across the US States. |
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We also achieved 200% Improvement in Courses & Content being delivered to Students. |
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50% Increase in Student’s Retention 150%, Increase in Teacher & Tutor Retention.' |
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- TechChefz Digital has established its presence in two countries, showcasing its |
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global reach and influence. The company’s headquarters is strategically located |
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in Noida, India, serving as the central hub for its operations and leadership. |
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In addition to the headquarters, TechChefz Digital has expanded its footprint |
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with offices in Delaware, United States, allowing the company to cater to the |
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North American market with ease and efficiency. |
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- 'Can you help migrate our existing infrastructure to a DevOps model? |
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Yes, we specialize in transitioning traditional IT infrastructure to a DevOps |
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model. Our process includes assessing your current setup, planning the migration, |
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implementing the necessary tools and practices, and providing ongoing support |
|
to ensure a smooth transition.' |
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- source_sentence: What steps do you take to understand a business's needs? |
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sentences: |
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- 'How do you customize your DevOps solutions for different industries? |
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We understand that each industry has unique challenges and requirements. Our approach |
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involves a thorough analysis of your business needs, industry standards, and regulatory |
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requirements to tailor a DevOps solution that meets your specific objectives' |
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- "Inception: Pioneering the Digital Frontier In our foundational year, TechChefz\ |
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\ embarked on a journey of digital transformation, laying the groundwork for our\ |
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\ future endeavors. We began working on Cab Accelerator Apps akin to Uber and\ |
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\ Ola, deploying them across Europe, Africa, and Australia, marking our initial\ |
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\ foray into global markets. Alongside, we successfully delivered technology trainings\ |
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\ across USA & India. \nqueries-techchefz-website\nqueries-techchefz-website\n\ |
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100%\n10\nA4\n\nAccelerating Momentum: A year of strategic partnerships & Transformative\ |
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\ Projects. In 2018, TechChefz continued to build on its strong foundation, expanding\ |
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\ its global footprint and forging strategic partnerships. Our collaboration with\ |
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\ digital agencies and system integrators propelled us into enterprise accounts,\ |
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\ focusing on digital experience development. This year marked significant collaborations\ |
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\ with leading automotive brands and financial institutions, enhancing our portfolio\ |
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\ and establishing TechChefz as a trusted partner in the industry. \n " |
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- 'Our Vision Be a partner for industry verticals on the inevitable journey towards |
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enterprise transformation and future readiness, by harnessing the growing power |
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of Artificial Intelligence, Machine Learning, Data Science and emerging methodologies, |
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with immediacy of impact and swiftness of outcome.Our Mission |
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To decode data, and code new intelligence into products and automation, engineer, |
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develop and deploy systems and applications that redefine experiences and realign |
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business growth.' |
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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: 0.03896103896103896 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.4805194805194805 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.5714285714285714 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.6493506493506493 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.03896103896103896 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.1601731601731602 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.11428571428571425 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.06493506493506492 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.03896103896103896 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.4805194805194805 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.5714285714285714 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.6493506493506493 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.3349468392248154 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.23376623376623376 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.24652168791713625 |
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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: 0.025974025974025976 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.4935064935064935 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.5844155844155844 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.6493506493506493 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.025974025974025976 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.1645021645021645 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.11688311688311684 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.06493506493506492 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.025974025974025976 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.4935064935064935 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.5844155844155844 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.6493506493506493 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.3381817622000061 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.23697691197691195 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.2485755814005223 |
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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 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: 0.05194805194805195 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.4675324675324675 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.5194805194805194 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.6233766233766234 |
|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.05194805194805195 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
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value: 0.15584415584415587 |
|
name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1038961038961039 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
|
value: 0.062337662337662324 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.05194805194805195 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.4675324675324675 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.5194805194805194 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
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value: 0.6233766233766234 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.3379715765084199 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
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value: 0.24577922077922074 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.2597360814073472 |
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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: 0.05194805194805195 |
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name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.44155844155844154 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5584415584415584 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6623376623376623 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05194805194805195 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.14718614718614723 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.11168831168831166 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0662337662337662 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05194805194805195 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.44155844155844154 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.5584415584415584 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6623376623376623 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.34288867015255386 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.24065656565656557 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2507978917088375 |
|
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 64 |
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type: dim_64 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.06493506493506493 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4155844155844156 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5064935064935064 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5974025974025974 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.06493506493506493 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.13852813852813856 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1012987012987013 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05974025974025971 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.06493506493506493 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4155844155844156 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.5064935064935064 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5974025974025974 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.32285221821950844 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.23481240981240978 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.24816289395996594 |
|
name: Cosine Map@100 |
|
--- |
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|
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# BGE base Financial Matryoshka |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/msmarco-distilbert-base-v4](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v4). 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. |
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|
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## Model Details |
|
|
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### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [sentence-transformers/msmarco-distilbert-base-v4](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v4) <!-- at revision 19f0f4c73dc418bad0e0fc600611e808b7448a28 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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|
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### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
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### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
|
|
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## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
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|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
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|
|
# Download from the 🤗 Hub |
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model = SentenceTransformer("Shashwat13333/msmarco-distilbert-base-v4") |
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# Run inference |
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sentences = [ |
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"What steps do you take to understand a business's needs?", |
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'How do you customize your DevOps solutions for different industries?\nWe understand that each industry has unique challenges and requirements. Our approach involves a thorough analysis of your business needs, industry standards, and regulatory requirements to tailor a DevOps solution that meets your specific objectives', |
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'Our Vision Be a partner for industry verticals on the inevitable journey towards enterprise transformation and future readiness, by harnessing the growing power of Artificial Intelligence, Machine Learning, Data Science and emerging methodologies, with immediacy of impact and swiftness of outcome.Our Mission\nTo decode data, and code new intelligence into products and automation, engineer, develop and deploy systems and applications that redefine experiences and realign business growth.', |
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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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### 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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### 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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### 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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## 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 | 0.039 | 0.026 | 0.0519 | 0.0519 | 0.0649 | |
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| cosine_accuracy@3 | 0.4805 | 0.4935 | 0.4675 | 0.4416 | 0.4156 | |
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| cosine_accuracy@5 | 0.5714 | 0.5844 | 0.5195 | 0.5584 | 0.5065 | |
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| cosine_accuracy@10 | 0.6494 | 0.6494 | 0.6234 | 0.6623 | 0.5974 | |
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| cosine_precision@1 | 0.039 | 0.026 | 0.0519 | 0.0519 | 0.0649 | |
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| cosine_precision@3 | 0.1602 | 0.1645 | 0.1558 | 0.1472 | 0.1385 | |
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| cosine_precision@5 | 0.1143 | 0.1169 | 0.1039 | 0.1117 | 0.1013 | |
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| cosine_precision@10 | 0.0649 | 0.0649 | 0.0623 | 0.0662 | 0.0597 | |
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| cosine_recall@1 | 0.039 | 0.026 | 0.0519 | 0.0519 | 0.0649 | |
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| cosine_recall@3 | 0.4805 | 0.4935 | 0.4675 | 0.4416 | 0.4156 | |
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| cosine_recall@5 | 0.5714 | 0.5844 | 0.5195 | 0.5584 | 0.5065 | |
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| cosine_recall@10 | 0.6494 | 0.6494 | 0.6234 | 0.6623 | 0.5974 | |
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| **cosine_ndcg@10** | **0.3349** | **0.3382** | **0.338** | **0.3429** | **0.3229** | |
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| cosine_mrr@10 | 0.2338 | 0.237 | 0.2458 | 0.2407 | 0.2348 | |
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| cosine_map@100 | 0.2465 | 0.2486 | 0.2597 | 0.2508 | 0.2482 | |
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 154 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 154 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: 12.43 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 126.6 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>What kind of websites can you help us with?</code> | <code>CLIENT TESTIMONIALS<br>Worked with TCZ on two business critical website development projects. The TCZ team is a group of experts in their respective domains and have helped us with excellent end-to-end development of a website right from the conceptualization to implementation and maintenance. By Dr. Kunal Joshi - Healthcare Marketing & Strategy Professional<br><br>TCZ helped us with our new website launch in a seamless manner. Through all our discussions, they made sure to have the website designed as we had envisioned it to be. Thank you team TCZ.<br>By Dr. Sarita Ahlawat - Managing Director and Co-Founder, Botlab Dynamics </code> | |
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| <code>What does DevSecOps mean?</code> | <code>How do you ensure the security of our DevOps pipeline?<br>Security is a top priority in our DevOps solutions. We implement DevSecOps practices, integrating security measures into the CI/CD pipeline from the outset. This includes automated security scans, compliance checks, and vulnerability assessments to ensure your infrastructure is secure</code> | |
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| <code>do you work with tech like nlp ?</code> | <code>What AI solutions does Techchefz specialize in?<br>We specialize in a range of AI solutions including recommendation engines, NLP, computer vision, customer segmentation, predictive analytics, operational efficiency through machine learning, risk management, and conversational AI for customer service.</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/msmarco-distilbert-base-v4_1 |
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- `push_to_hub_model_id`: msmarco-distilbert-base-v4_1 |
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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/msmarco-distilbert-base-v4_1 |
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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`: msmarco-distilbert-base-v4_1 |
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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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</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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|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
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| 0.2 | 1 | 4.0076 | - | - | - | - | - | |
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| 1.0 | 5 | 4.8662 | 0.3288 | 0.3390 | 0.3208 | 0.3246 | 0.2749 | |
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| 2.0 | 10 | 4.1825 | 0.3288 | 0.3456 | 0.3306 | 0.3405 | 0.2954 | |
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| 3.0 | 15 | 3.048 | 0.3329 | 0.3313 | 0.3346 | 0.3392 | 0.3227 | |
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| **4.0** | **20** | **2.5029** | **0.3349** | **0.3382** | **0.338** | **0.3429** | **0.3229** | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.11.11 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.47.1 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.2.1 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@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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#### 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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#### 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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