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Andreas Chandra

andreaschandra
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https://linkedin.com/in/chandraandreas

AI & ML interests

Sentiment Analysis, Question Answering, Text Summarization, Language Model

Recent Activity

replied to juhoinkinen's post 1 day ago
We (@osma, @MonaLehtinen & me, i.e. the Annif team at the National Library of Finland) recently took part in the LLMs4Subjects challenge at the SemEval-2025 workshop. The task was to use large language models (LLMs) to generate good quality subject indexing for bibliographic records, i.e. titles and abstracts. We are glad to report that our system performed well; it was ranked 🥇 1st in the category where the full vocabulary was used 🥈 2nd in the smaller vocabulary category 🏅 4th in the qualitative evaluations. 14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available. We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way. More information can be found in our system description preprint: https://huggingface.co/papers/2504.19675 See also the task description preprint: https://huggingface.co/papers/2504.07199 The Annif models trained for this task are available here: https://huggingface.co/NatLibFi/Annif-LLMs4Subjects-data
reacted to juhoinkinen's post with 🚀 1 day ago
We (@osma, @MonaLehtinen & me, i.e. the Annif team at the National Library of Finland) recently took part in the LLMs4Subjects challenge at the SemEval-2025 workshop. The task was to use large language models (LLMs) to generate good quality subject indexing for bibliographic records, i.e. titles and abstracts. We are glad to report that our system performed well; it was ranked 🥇 1st in the category where the full vocabulary was used 🥈 2nd in the smaller vocabulary category 🏅 4th in the qualitative evaluations. 14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available. We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way. More information can be found in our system description preprint: https://huggingface.co/papers/2504.19675 See also the task description preprint: https://huggingface.co/papers/2504.07199 The Annif models trained for this task are available here: https://huggingface.co/NatLibFi/Annif-LLMs4Subjects-data
reacted to DawnC's post with 👀 1 day ago
VisionScout — Now with Video Analysis! 🚀 I’m excited to announce a major update to VisionScout, my interactive vision tool that now supports VIDEO PROCESSING, in addition to powerful object detection and scene understanding! ⭐️ NEW: Video Analysis Is Here! 🎬 Upload any video file to detect and track objects using YOLOv8. ⏱️ Customize processing intervals to balance speed and thoroughness. 📊 Get comprehensive statistics and summaries showing object appearances across the entire video. What else can VisionScout do? 🖼️ Analyze any image and detect 80 object types with YOLOv8. 🔄 Switch between Nano, Medium, and XLarge models for speed or accuracy. 🎯 Filter by object classes (people, vehicles, animals, etc.) to focus on what matters. 📊 View detailed stats on detections, confidence levels, and distributions. 🧠 Understand scenes — interpreting environments and potential activities. ⚠️ Automatically identify possible safety concerns based on detected objects. What’s coming next? 🔎 Expanding YOLO’s object categories. ⚡ Faster real-time performance. 📱 Improved mobile responsiveness. My goal: To bridge the gap between raw detection and meaningful interpretation. I’m constantly exploring ways to help machines not just "see" but truly understand context — and to make these advanced tools accessible to everyone, regardless of technical background. Try it now! 🖼️👉 https://huggingface.co/spaces/DawnC/VisionScout If you enjoy VisionScout, a ❤️ Like for this project or feedback would mean a lot and keeps me motivated to keep building and improving! #ComputerVision #ObjectDetection #VideoAnalysis #YOLO #SceneUnderstanding #MachineLearning #TechForLife
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