PinnedUnderstanding Semantic Search— (Part 1: Introduction to Machine Reading Comprehension)Have you ever wondered how Google can give a granular answer to our question?Transfer Learning4 min readTransfer Learning4 min read
PinnedUnderstanding Semantic Search — (Part 2: Machine Reading Comprehension at Scale)In the previous article, I introduced the Machine Reading Comprehension model using SQuAD Dataset, Language Models, and Transfer Learning. In this article, I will discuss how to scale the Machine Reading Comprehension model for long documents. The biggest limitation of the Machine Reading Comprehension model is the input size of…Tf Idf6 min readTf Idf6 min read
PinnedUnderstanding Semantic Search — (Part 3: Introduction to Knowledge Graphs for Question Answering)Have you ever wondered how Google shows the “About” panel when you search for a person or a place? They use Knowledge Graphs to do it!Question Answering5 min readQuestion Answering5 min read
Published inGoPenAI·May 15Invoice or Bill Custom Parsing using Kor (LangChain Extension), Generative Language Models & Prompt Engineering (Part 1)Unstructured / Semi-Structured Data Custom Formatting — Background: In this digital age, businesses are immersed in a sea of data. Every interaction, transaction, and online engagement generates vast amounts of data. This data comes in various forms like text, images, audio, and video. …Langchain10 min readLangchain10 min read
Apr 24Understanding Semantic Search — (Part 9: Introduction to Generative Question Answering, Prompt Engineering, LangChain Library, and more!)TABLE OF CONTENTS: Introduction to Generative Question Answering Prompt Engineering LangChain Library Which question answering is better? (Extractive vs. Generative) Conclusion Introduction to Generative Question Answering: In part 0 of the series, I introduced different types of question answers, including extractive and generative. Extractive Question Answering (EQA) systems find the answer to a question within…Langchain6 min readLangchain6 min read
Mar 15Understanding Semantic Search — (Part 5: Ranking Metrics for Evaluating Question Answering Systems and Recommendation Systems)This article explained the retriever and reader architecture used for extractive question-answering modeling. In short, for a given user query, the retriever model predicts the best passages, and the reader model takes the output from retriever models to predict relevant answers. The last article explained different answer quality metrics for…Metrics7 min readMetrics7 min read
Mar 2Understanding Semantic Search — (Part 8: Unlock the Power of Extractive Question Answering Modelling — Learn How to Tune Language Models for Semantic Search)Why, when, and how to train language models for extractive question-answering modeling? — TABLE OF CONTENTS: Introduction (summary of retriever and reader architecture) Different ways to train NLP models Annotations and Best Practices Training Retriever Models Training Reader Models Conclusion Introduction: In part 0 of the series, I introduced different forms of semantic search applications, including extractive question-answering (EQA) modeling. …Semantic Search9 min readSemantic Search9 min read
Published inLMAO Learning Group·Feb 12Introducing LMAO: Language Modeling Advancements Online Learning GroupWhat is this learning group about? — As humans, we rely heavily on language to communicate with each other, but language can be complex to understand and difficult to master. Machines are no exception. Hence, the idea of language modeling came into existence and helped machines better process and understand the nuances of language. …NLP3 min readNLP3 min read
Published inLMAO Learning Group·Feb 10Are Large Language Models (LLMs) Thinking Machines? — Post chatGPT buzz!Summarizing my thoughts after working on LLMs for a while and reading DISSOCIATING LANGUAGE AND THOUGHT IN LARGE LANGUAGE MODELS: A COGNITIVE PERSPECTIVE paper — Introduction Formal vs. Functional Linguistic Competence How do today's LLMs perform on Formal Linguistic Competence? How do today's LLMs perform on Functional Linguistic Competence? Conclusion Introduction: Large Language Models (LLMs) have become powerful. Multiple applications are built on these models across different domains. For example, Viable utilizes GPT-3 LLM to analyze…ChatGPT8 min readChatGPT8 min read
Published inCognistx’s SQUARE/DIP Blog·Jan 30An Intelligent and Scalable Search Tool For Healthcare IndustryApplications of Semantic Search in the Healthcare Domain Semantic search technology uses natural language processing and understanding (NLP & NLU) to understand the meaning behind search queries and provide the most relevant results. It allows healthcare providers to find the information they need quickly and accurately. …Search4 min readSearch4 min read
