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DistilBERT User Profiling and matching Model (developer not writer)

€8-30 EUR

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Disiarkan 28 hari yang lalu

€8-30 EUR

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To fully implement the architecture for the three models—profile creation, event creation, and matching events with user profiles—on devices using DistilBERT, let's detail each component, including the practical data flow and model interactions. This structure will ensure privacy, efficiency, and adaptability of the system in real-world scenarios. 1. Profile Creation Model Architecture: Input Layer: Takes raw user data including textual descriptions and user activity logs. Processing Layer: Utilizes DistilBERT to extract features such as interests, professional background, and preferences from textual data. Output Layer: Produces a structured user profile with categorized data such as interests (e.g., AI, outdoor activities), professional background (e.g., software developer), and preferred event types (e.g., workshops). Example: Input: "I am a web developer with a keen interest in blockchain technologies. I enjoy outdoor sports and networking events." Processing: DistilBERT Analysis: Extracts "web developer" as a profession, "blockchain" as an interest under technology, and "outdoor sports" and "networking events" as social preferences. Output: Profile: { Profession: "Web Developer", Interests: ["Blockchain", "Outdoor Sports"], Event Preferences: ["Networking Events"], Social Preferences: ["Outdoor Activities"] } 2. Event Creation Model Architecture: Input Layer: Accepts raw event descriptions and other metadata provided by the event organizers. Processing Layer: DistilBERT processes the description to categorize the event and tag it with relevant attributes like location, event type, and key topics. i have the datasets as well Output Layer: Generates a structured event profile that is stored locally and used for matching with user profiles. Example: Input: "Explore the future of AI at our annual conference with interactive sessions in Silicon Valley this August." Processing: DistilBERT Analysis: Identifies "AI" as the key topic, "annual conference" as the event type, and "Silicon Valley" as the location. Output: Event Profile: { Category: "Technology", Type: "Conference", Location: "Silicon Valley", Keywords: ["AI", "Interactive Sessions"], Time: "August" } 3. Matching Events with User Profiles Architecture: Input Layer: Receives structured profiles of both users and events from their respective local databases. Processing Layer: A similarity calculation algorithm (using techniques such as cosine similarity or a machine-learned scoring model) evaluates the fit between user preferences and event attributes. Output Layer: Provides a list of event recommendations ranked by relevance to the user’s profile. Example: Input: User Profile { Profession: "Web Developer", Interests: ["Blockchain", "Outdoor Sports"], Event Preferences: ["Networking Events"], Location: "San Francisco" } Event Profile { Category: "Technology", Type: "Conference", Location: "Silicon Valley", Keywords: ["AI", "Interactive Sessions"], Time: "August" } Processing: Similarity Score: Calculate how well the event's features match the user’s interests and preferences. Output: Recommendation: High relevance due to the match in professional interest (technology) and geographical proximity. Implementation Considerations Local Processing and Storage: All three components operate entirely on-device, ensuring data privacy and reducing latency. Optimized DistilBERT: Employ techniques such as quantization and pruning to ensure the model runs efficiently on mobile devices. Incremental Learning and Updates: Models can be incrementally updated with new data inputs to refine their outputs continuously, using techniques like online learning. Feedback System: Incorporate user feedback to adapt and improve model predictions and relevance over time. This architecture ensures a cohesive and interactive user experience, allowing for dynamic event discovery and networking opportunities tailored to individual preferences, all while maintaining a high standard of privacy and data security.
ID Projek: 38024473

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As a skilled AI developer with a wide experience in ML and LLM , I specialize in DistilBert. I have fully understanding of your requirements and My skills align well with your demands. I can implement your three architectures on time . I am eager to discuss with your project more detail. Thanks.
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Hello there Arpita S., Good evening! My name is Jane a skilled Statistician with skills including Python, Large Language Models (LLMs), Software Architecture, Machine Learning (ML) and Algorithm. I have over 5 years in tutoring data analysis and statistics. Having completed similar project, I am confident in my ability to deliver high-quality results for this project. I am eager to discuss further details and see how I can contribute to your team. I am happy to offer a free consultation and a 10% discount for first-time clients. Please send a message to discuss more about this project. Best Jane
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Dear Client. After read your requirement, I realized your project is matched well with my ability-Algorithm, Machine Learning (ML), Software Architecture, Large Language Models (LLMs) and Python. I have successfully developed several project as same as you. I prioritize clear transparency, and collaboration throughout the development process to ensure that the final product aligns with your vision and objectives. I am excited about the opportunity to work with you on this project and confident in my ability to deliver exceptional results. If you have any questions or would like to discuss the details further, please feel free to reach out at your convenience. Thank you for considering my proposal. I am looking forward to the possibility of collaborating with you. Patricio
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Hi I can knock this out of the park for you. I absolutely guarantee I can accomplish the development of your project on-time, and on-budget. With over 10 years of experience as a full-stack developer, I am confident in my ability to deliver a professional and user-friendly results that meet your requirements. My expertise includes Algorithm, Python, Machine Learning (ML), Large Language Models (LLMs) and Software Architecture. Specially I did complete very similar projects as same as you want.... I'm ready to start from now on. Sincerely, Lukas
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Having a wealth of experience spanning over 8 years as a Full Stack Developer, My skillset makes me ideal for implementing the DistilBERT User Profiling and Matching Model in your project. I have an intricate understanding of Python and Software Architecture, which will enable me to seamlessly execute the different components, including profile creation, event creation, and matching events with user profiles. Throughout my career, I have developed a wide variety of Web & Mobile Apps using languages and frameworks such as HTML5, CSS3, Bootstrap, JavaScript, JQuery&AJAX, MERN & MEVN stack, PHP, NodeJS, Python along with associated databases like MySQL, MongoDB, Postgresql. Given that your project relies heavily on designing an efficient architecture that utilizes DistilBERT while maintaining privacy and adaptability in real-world scenarios, my background in Software Architecture coupled with my proficiency in Python perfectly suit the job. If you’re looking for not just a developer but rather a partner who's committed to providing top quality work through an optimized solution - one that leverages mobile compatibility, ensures data privacy through local processing and storage while also incorporating users' feedback for ongoing improvement - your search ends here! Choose me today for unparalleled expertise and dedication.
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Hello, What I propose is, to solve this, I'll implement three models: profile creation, event creation, and matching events with user profiles, utilizing DistilBERT on devices. The profile creation model extracts user features from textual data, the event creation model categorizes events based on descriptions, and the matching model evaluates the fit between user preferences and event attributes. All processing occurs locally, ensuring privacy and reducing latency. Optimized DistilBERT techniques enable efficient model execution on mobile devices, while incremental learning and user feedback refine model predictions over time. This approach delivers dynamic event discovery and networking opportunities while maintaining stringent privacy and data security standards. My skills: Proficiency in machine learning techniques, particularly in NLP for text analysis and feature extraction using models like DistilBERT. Experience in embedded systems programming for implementing the solution on devices, ensuring efficient utilization of hardware resources. Skills in data preprocessing and structuring to prepare raw user and event data for input into the models. If you like my idea let's chat, Looking forward to work with you, Regards, Vipul
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Tentang klien

Bendera GERMANY
Heilbronn, Germany
5.0
25
Kaedah pembayaran disahkan
Ahli sejak Feb 4, 2022

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