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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:408 |
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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: What sets TechChefz apart? |
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sentences: |
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- 'Sharing Stories from Our Team |
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Discover firsthand experiences, growth journeys, and the vibrant culture that |
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fuels our success. |
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I have been a part of Techchefz for 3 years, and I can confidently say it''s been |
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a remarkable journey. From day one, I was welcomed into a vibrant community that |
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values collaboration, creativity, and personal growth. The company culture here |
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isn''t just a buzzword, it''s tangible in every interaction and initiative. |
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profileImg |
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Aashish Massand |
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Sr. Manager Delivery |
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TechChefz has been a transformative journey, equipping me with invaluable skills |
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and fostering a supportive community. From coding fundamentals to advanced techniques, |
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I''ve gained confidence and expertise. Grateful for this experience and opportunity. |
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profileImg |
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Pankaj Datt |
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Associate Technology' |
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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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- 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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- source_sentence: How does this solution comply with data regulations? |
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sentences: |
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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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- 'We assist businesses by transforming their goals, teams, and cultures with digital |
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technology to make them colinear with the digital age. Through digitalization, |
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organizations can facilitate advanced decision-making and management. |
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' |
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- 'Microservices Transformation Process |
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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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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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Testing |
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After each sprint we conduct thorough testing of the website to identify and fix |
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any bugs or issues. Perform usability testing to ensure a positive user experience. |
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Deployment |
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After testing we deploy the website sprint by sprint, to a hosting environment, |
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ensuring proper configuration for security and performance. Our expert DevOps |
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team sets up any necessary domain and server configurations and ensure smooth |
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running of website.' |
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- source_sentence: What tasks can we automate using machine learning? |
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sentences: |
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- 'Check out our latest news, announcements, and featured insights. |
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Explore our latest insights and stay informed with our thought-provoking content. |
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Dive in now for valuable perspectives. |
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Our Featured Insights |
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How UX and UI Work Together in Web Design |
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Navigating the Post-Cookie Era: Strategies for Effective Targeting and Personalization |
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Data-Driven Decision Making in Digital Advertising: Leveraging Analytics for Success |
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SEO Unleashed: Navigating the Digital Landscape with Advanced Search Engine Optimization |
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Tools |
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Is manual testing replaced by automation Testing?' |
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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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- Mayank Maggon is CEO of Techchefz Digital |
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- source_sentence: How can you help us grow our partnerships? |
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sentences: |
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- "Partner Experience (PX)\n From optimized collaboration tools to data-driven insights,\ |
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\ our solutions are designed to drive efficiency, transparency, and growth in\ |
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\ partner relationships. With a keen understanding of complexities of partner\ |
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\ ecosystems, we help enterprise brands unlock new opportunities, strengthen alliances,\ |
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\ and achieve shared success in today’s dynamic business environment." |
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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 and |
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decision-making capabilities across all sectors. With a global network of AI/ML |
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experts and a commitment to excellence, we are your partners in transforming innovative |
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possibilities into real-world achievements. |
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- 'COMMERCE PLATFORMS |
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Discover the strength of our partnership. |
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Adobe Commerce Cloud |
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A comprehensive e-commerce platform that allows businesses to create, manage, |
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and optimize their online stores. Formerly known as Magento Commerce, Adobe Commerce |
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Cloud provides a range of features and capabilities to help businesses create |
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engaging online shopping experiences, manage their products and catalogs, process |
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orders, and drive online sales. |
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Magento |
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An open-source e-commerce platform that allows businesses to create online stores |
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and manage their digital operations. It was first released in 2008 and has since |
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become one of the most popular e-commerce platforms in the world. |
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Shopify |
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Salesforce Commerce Cloud (SFCC)' |
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- source_sentence: How is an Enterprise CMS different from a headless CMS? |
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sentences: |
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- 'How do I figure out how much your services will cost? |
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Determining the cost of our services is best achieved through a 15-30 minute discovery |
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call, where we can understand your unique requirements. Following that, we will |
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provide a transparent and detailed price within 24-48 hours tailored specifically |
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to you' |
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- 'Discover the right CMS for your Business Requirements |
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Headless CMS |
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They separate the backend content repository from the frontend presentation layer, |
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allowing content to be delivered to any device or platform via APIs offering flexibility |
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and scalability. |
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Enterprise CMS |
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ECMSs are more comprehensive systems designed to manage all types of content within |
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an organization, including documents, images, videos, and other digital assets.' |
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- We offer custom software development, digital marketing strategies, and tailored |
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solutions to drive tangible results for your business. Our expert team combines |
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technical prowess with industry insights to propel your business forward in the |
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digital landscape. |
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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: 0.0 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.0784313725490196 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.4019607843137255 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.5196078431372549 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.0 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.0261437908496732 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.08039215686274509 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.05196078431372548 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.0 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.0784313725490196 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.4019607843137255 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.5196078431372549 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.20681828171013134 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.11193977591036408 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.12704742492729623 |
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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: 0.0 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.0784313725490196 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.4019607843137255 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.5196078431372549 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.0 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.0261437908496732 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.08039215686274509 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.05196078431372548 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.0 |
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|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.0784313725490196 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.4019607843137255 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.5196078431372549 |
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|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.20587690425273067 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.11086601307189538 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.12502250584870636 |
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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: 0.0 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.06862745098039216 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.39215686274509803 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.5098039215686274 |
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|
name: Cosine Accuracy@10 |
|
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- type: cosine_precision@1 |
|
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value: 0.0 |
|
|
name: Cosine Precision@1 |
|
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- type: cosine_precision@3 |
|
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value: 0.02287581699346405 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.0784313725490196 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.05098039215686274 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
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value: 0.0 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.06862745098039216 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.39215686274509803 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.5098039215686274 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.20200410483390918 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.10891690009337061 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.12124652633795324 |
|
|
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 128 |
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type: dim_128 |
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metrics: |
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|
- type: cosine_accuracy@1 |
|
|
value: 0.0 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.058823529411764705 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.3137254901960784 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.49019607843137253 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.0 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.0196078431372549 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.06274509803921569 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.04901960784313725 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.0 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.058823529411764705 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.3137254901960784 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.49019607843137253 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.18661585783989612 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.09673202614379077 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.11007694082793783 |
|
|
name: Cosine Map@100 |
|
|
- task: |
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type: information-retrieval |
|
|
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: 0.0 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.029411764705882353 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.28431372549019607 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.4117647058823529 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.0 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.00980392156862745 |
|
|
name: Cosine Precision@3 |
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|
- type: cosine_precision@5 |
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|
value: 0.05686274509803922 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.04117647058823529 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.0 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.029411764705882353 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.28431372549019607 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.4117647058823529 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.15696823886592676 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.08097572362278241 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.09297982754610348 |
|
|
name: Cosine Map@100 |
|
|
--- |
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|
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# BGE base Financial Matryoshka |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **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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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **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 |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(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("akashmaggon/bge-base-financial-matryoshka-finetuning-tcz-1") |
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# Run inference |
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sentences = [ |
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'How is an Enterprise CMS different from a headless CMS?', |
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|
'Discover the right CMS for your Business Requirements\nHeadless CMS\nThey separate the backend content repository from the frontend presentation layer, allowing content to be delivered to any device or platform via APIs offering flexibility and scalability.\n\n\nEnterprise CMS\nECMSs are more comprehensive systems designed to manage all types of content within an organization, including documents, images, videos, and other digital assets.', |
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'How do I figure out how much your services will cost?\nDetermining the cost of our services is best achieved through a 15-30 minute discovery call, where we can understand your unique requirements. Following that, we will provide a transparent and detailed price within 24-48 hours tailored specifically to you', |
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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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|
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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 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| cosine_accuracy@3 | 0.0784 | 0.0784 | 0.0686 | 0.0588 | 0.0294 | |
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| cosine_accuracy@5 | 0.402 | 0.402 | 0.3922 | 0.3137 | 0.2843 | |
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| cosine_accuracy@10 | 0.5196 | 0.5196 | 0.5098 | 0.4902 | 0.4118 | |
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| cosine_precision@1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| cosine_precision@3 | 0.0261 | 0.0261 | 0.0229 | 0.0196 | 0.0098 | |
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| cosine_precision@5 | 0.0804 | 0.0804 | 0.0784 | 0.0627 | 0.0569 | |
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| cosine_precision@10 | 0.052 | 0.052 | 0.051 | 0.049 | 0.0412 | |
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| cosine_recall@1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| cosine_recall@3 | 0.0784 | 0.0784 | 0.0686 | 0.0588 | 0.0294 | |
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| cosine_recall@5 | 0.402 | 0.402 | 0.3922 | 0.3137 | 0.2843 | |
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| cosine_recall@10 | 0.5196 | 0.5196 | 0.5098 | 0.4902 | 0.4118 | |
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| **cosine_ndcg@10** | **0.2068** | **0.2059** | **0.202** | **0.1866** | **0.157** | |
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| cosine_mrr@10 | 0.1119 | 0.1109 | 0.1089 | 0.0967 | 0.081 | |
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| cosine_map@100 | 0.127 | 0.125 | 0.1212 | 0.1101 | 0.093 | |
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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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<!-- |
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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: 408 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 408 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: 8 tokens</li><li>mean: 12.63 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 94.18 tokens</li><li>max: 270 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:-----------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>What's it like working at Techchefz?</code> | <code>Join one of the most resourceful tech teams<br><br>Discover your future with us. Explore opportunities, values, and culture. Join a dynamic and innovative team at Techchefz.<br><br>LIFE AT TECHCHEFZ<br>Make an Impact from Day One.<br><br>We believe in the power of collaboration to create, innovate, and develop groundbreaking solutions. Our teams work closely with clients and partners to co-create solutions that drive innovation and business growth.<br>Your new journey awaits!</code> | |
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| <code>How can I contact TechChefz if I'm in the US?</code> | <code>TechChefz Digital has established its presence in two countries, showcasing its global reach and influence. The company’s headquarters is strategically located in Noida, India, serving as the central hub for its operations and leadership. In addition to the headquarters, TechChefz Digital has expanded its footprint with offices in Delaware, United States, allowing the company to cater to the North American market with ease and efficiency.</code> | |
|
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| <code>What results can I expect from your services?</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.</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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|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
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|
|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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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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- `batch_sampler`: no_duplicates |
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|
|
|
#### All Hyperparameters |
|
|
<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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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`: 16 |
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|
- `eval_accumulation_steps`: None |
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|
- `torch_empty_cache_steps`: None |
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|
- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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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 |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `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 |
|
|
- `use_cpu`: False |
|
|
- `use_mps_device`: False |
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|
- `seed`: 42 |
|
|
- `data_seed`: None |
|
|
- `jit_mode_eval`: False |
|
|
- `use_ipex`: False |
|
|
- `bf16`: False |
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|
- `fp16`: True |
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|
- `fp16_opt_level`: O1 |
|
|
- `half_precision_backend`: auto |
|
|
- `bf16_full_eval`: False |
|
|
- `fp16_full_eval`: False |
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|
- `tf32`: None |
|
|
- `local_rank`: 0 |
|
|
- `ddp_backend`: None |
|
|
- `tpu_num_cores`: None |
|
|
- `tpu_metrics_debug`: False |
|
|
- `debug`: [] |
|
|
- `dataloader_drop_last`: False |
|
|
- `dataloader_num_workers`: 0 |
|
|
- `dataloader_prefetch_factor`: None |
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|
- `past_index`: -1 |
|
|
- `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 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `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} |
|
|
- `deepspeed`: None |
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|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch_fused |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: None |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `dispatch_batches`: None |
|
|
- `split_batches`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | 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 | |
|
|
|:----------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
|
| 0.6154 | 1 | 0.2038 | 0.1993 | 0.1953 | 0.1764 | 0.1595 | |
|
|
| 1.6154 | 2 | 0.2038 | 0.1993 | 0.1953 | 0.1764 | 0.1595 | |
|
|
| **2.6154** | **3** | **0.2068** | **0.2059** | **0.202** | **0.1866** | **0.157** | |
|
|
| 3.6154 | 4 | 0.2068 | 0.2059 | 0.2020 | 0.1866 | 0.1570 | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 3.3.1 |
|
|
- Transformers: 4.47.1 |
|
|
- PyTorch: 2.5.1+cu121 |
|
|
- Accelerate: 1.2.1 |
|
|
- Datasets: 3.2.0 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MatryoshkaLoss |
|
|
```bibtex |
|
|
@misc{kusupati2024matryoshka, |
|
|
title={Matryoshka Representation Learning}, |
|
|
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}, |
|
|
year={2024}, |
|
|
eprint={2205.13147}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.LG} |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
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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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## 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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