Let's outline the steps to build such applications and explain why semantic search combined with GPT QnA is a powerful approach.ġ️⃣ Broader knowledge coverage: Leverage up-to-date information from various documents.Ģ️⃣ Context-specific answers: Generate precise answers based on relevant passages.ģ️⃣ Adaptability: Easily update information sources or tune to different domains.Ĥ️⃣ Better handling of ambiguous queries: Identify the most relevant passages for accurate answers. 🤖 Combining LangChain, Pinecone, and LLMs like GPT-4. We want to extend our warmest congratulations to Shubham for his incredible efforts and thank all participants for their hard work and dedication.įutureSmart AI AI Demos #OCRChallenge #MachineLearning #DataExtraction #GraphAnalysis #AI #WinnerAnnouncement #EffortMatters #SharingKnowledgeĮxtracting Data from Charts and Graphs: The OCR Challenge Solution To share his insights and help others learn from his experience, Shubham has generously provided a detailed blog post, walking through his winning solution, the different approaches he used, and the accompanying code. We're excited to announce that shubham mehla's exceptional submission earned him the title of OCR Challenge Winner! 🌟 Shubham's solution showcases the potential of advanced machine learning techniques in making graph analysis more accessible and efficient.
The objective was not to achieve a perfect solution but to reward outstanding effort and creativity in tackling a complex problem. We recently held the OCR Challenge, aimed at developing innovative solutions for extracting data from charts and graphs in images or PDFs. 🚀 Exciting Announcement: OCR Challenge Winner! 🏆
0 kommentar(er)
