Siftree’s Post

Ever heard about #huggingface or it´s potential in AI? In this presentation Nicolas Franco Cerame will let you discover how Hugging Face is revolutionizing AI with powerful models and seamless integration in Claris platform. #ai #engageu #filemaker #squaremoon #conference

🌟 Unlock the Power of Natural Language Processing with Bold BI! 🌟 Join us on Thursday, December 5th, 2024, at 10:00 AM ET for an enlightening session. Discover how NLP can transform your data experience. 🚀 Don't miss out! 👉 https://lnkd.in/ede_XGX4 #NLP #BoldBI #DataScience

🌐 Session Highlight: Nicolas Franco Cerame's Presentation on Hugging Face & FileMaker Integration 🌐 Hugging Face has revolutionized Natural Language Processing (NLP) with its open-source platform and state-of-the-art pre-trained models. Join Nicolas Franco to discover how to integrate these advanced AI capabilities with FileMaker's robust database solutions. Learn how to: 🔵 Use FileMaker's Insert From URL function to connect to the Hugging Face API 🔵 Implement real-time sentiment analysis for customer comments 🔵 Handle authentication, manage errors, and optimize performance Don't miss this opportunity to see live code examples and practical use cases! #EngageU2024 #HuggingFace #FileMaker #NLP #AIIntegration #TechInnovation

Grateful to مبادرة العطاء الرقمي and Engineer Fatima Alderazi, MSc, PMP, ITIL® for an enlightening initiative on "AI in Natural Language Processing (NLP)" today! The session covered the foundations of NLP, steps of application, challenges, and future trends, highlighting the potential of Large Language Models (LLMs) like GPT-3. The initiative also touched on LLMs utility in diverse sectors like health and education. #NLP #AI #LLM #GPT3 #Workshop #TechTalks

🚀 Excited to share my latest blog post on the revolutionary T5 (Text-To-Text Transfer Transformer) model! 🌟 In this post, I delve into the intricacies of the T5 model, developed by Google Research, and how it is transforming the field of natural language processing (NLP). T5 treats every NLP task as a "text-to-text" problem, offering a unified and versatile approach to numerous applications. 🔍 Key Highlights from the Blog: Unified Framework: Learn how T5 simplifies the model architecture and training process by framing all tasks as text generation tasks. Scalability: Discover the model's ability to scale from small to extremely large, accommodating various computational capacities. Pre-training & Fine-tuning: Explore how T5 achieves state-of-the-art results through extensive pre-training and task-specific fine-tuning. 💡 Applications Covered: Translation: High-quality language translation capabilities. Summarization: Efficiently generating concise summaries from lengthy documents. Question Answering: Accurately answering queries based on contextual information. Text Classification: Effectively categorizing text into predefined classes. The T5 model's flexibility and power are paving the way for exciting new possibilities in NLP research and applications. Whether you're a researcher, developer, or enthusiast, this blog will provide valuable insights into why T5 is a game-changer. #NLP #MachineLearning #ArtificialIntelligence #T5Model #GoogleResearch #TextGeneration #BlogPost #GenAI #llm

Short Post Series: Post 2 : What is Retrieval Augmented Generation (RAG)? RAG is a relatively new approach in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI). It addresses a key limitation of LLMs – their reliance on the information they were trained on, which can be outdated or incomplete. Essentially, RAG allows LLMs to access and utilize external information sources, making their responses more comprehensive, accurate, and up-to-date. #AI #LLM #RAG #NLP #SimpleAI

ARCHITECTURE of TRANSFORMER 🤖 A transformer model, introduced in 2017, revolutionized Natural Language Processing (NLP) by enabling robust handling of sequential data. It consists of two main components: Encoders and Decoders. ENCODERS: ⏩ Each word is embedded into a vector. ⏩ Vectors are concurrently fed into Self-Attention, facilitating contextual understanding by incorporating relevant words. ⏩ Self-Attention mathematically involves embedding each word, splitting into 8 heads, and calculating attention scores using Q/K/V matrices. ⏩ Following addition and normalization, attention scores are computed via softmax, resulting in a Z vector which undergoes further transformations via weight multiplication. ⏩ Z vectors are then forwarded to a Feed Forward Neural Network (FFNN), wherein traditional hidden and output layer operations occur. DECODERS: Similar to Encoders, words are embedded and undergo Self-Attention. However, before proceeding to FFNN, Encoder-Decoder attention occurs, emphasizing the significance and context of each word. Know More : https://lnkd.in/gg8Vrw2S Code Implementation : https://lnkd.in/eQMW4GUU Depth Understanding : https://lnkd.in/eJDn8rZa #MachineLearning #DataSCience #Transformers #AI #NLP

Hello!! This task is about Summarization."Text summarization" refers to the technique of shortening long pieces of text. It is the process of breaking down lengthy and complex text into smaller chunks summarizing the content. The idea is to find the most essential and most relevant information and present it in a human-readable and comprehensive way, without changing the meaning of the text, to a shorter version, reducing the size of the initial text while at the same time preserving key informational elements. Automatic text summarization is a common problem in machine learning and natural language processing (NLP). #AIMERS #AIMERSOCIETY #HuggingFace #HuggingFaceModels #Summarization #NLP #NaturalLanguageProcessing #MachineLearning #GoogleColab #TextSummarization #APSHCE #GIET #GGU AIMER Society - Artificial Intelligence Medical and Engineering Researchers Society

'Magnus': Optimizing Large Language Model Serving Efficiency in LMaaS Transformer-based generative Large Language Models (LLMs) have proven their prowess across a spectrum of Natural Language Processing (NLP) tasks. Despite their versatility, the cost of training and deploying these models often deters developers. Leading AI firms such as OpenAI, Google, and Baidu address this with Language Model-as-a-Service (LMaaS), granting access to LLMs via APIs. https://is.gd/WVAtKK #AI #AItechnology #artificialintelligence #llm #LMaaS #machinelearning

I'm thrilled to share our latest study which addresses the challenge of accurately extracting location information from the unstructured and variable text found on social media platforms, particularly during emergencies. The study aims to develop and validate the Text-to-Text Transfer Transformer (T5) model for this task, achieving an impressive 95% accuracy, compared to SpaCy's 45%. The superior performance of the T5 model highlights its potential to significantly enhance real-time disaster response and public safety efforts by processing complex social media data. Kudos to the team for pushing the boundaries of what's possible in Natural Language Processing! 👏 #NLP #MachineLearning #SocialMedia link: https://lnkd.in/eh3bDKP3