It aims to encourage collaboration and interaction among developers through project participation. The projects cover various fields, including but not limited to science, numerical computation, and engineering. You are also encouraged to share your own projects in scisprint. Refer to project list below for more details.
modmesh seamlessly mixes C++ and Python through pybind11, allowing you to leverage the strengths of both programming languages for efficient PDE solving. We use Qt and Python to visualize the computation results to give you a better understanding of your PDE solution. modmesh also supports mesh visualization, currently in the Gmsh mesh file format. We have recently made efforts to improve the modmesh UI/UX.
The design allows it to run on Windows, Linux, and MacOS. Everyone can use or contribute to modmesh.
Sciwork Portal is a project for maintaining our official website - Sciwork.dev, which was built by Pelican with tailwindCSS, and deployed by Netfliy. We create the promotional pages for meetup and sprint events. Our team also maintains the sciwork conference page - conf.sciwork.dev.
We have always been actively trying to provide users a better web browsing experience, including information presentation and visual experience. Welcome to join us if you are interested in website maintence.
Our primary mission is to make Python's official documentation accessible to the Traditional Chinese audience by providing accurate and comprehensive translations. Whether you are a seasoned Python developer or a language expert, we welcome individuals who are enthusiastic about Python and passionate about making knowledge accessible to all.
Mandarin grammar parser based on syntactical context. By analyzing and simplifying the structures of correct usages into code instructions, provide a linguistics-based model to accomplish efficient Mandarin grammar checking tasks with minimal resources.
Topic: Text Classification Review: A Comparative Journey from Word Frequency to Large Language Models
Speaker: Teng-Lin Yu
The application scenarios of text classification technology in enterprises are numerous.
Whether itβs in customer service, risk management, or investment fields, text classification models can be established to help companies automate processes and thus save a significant amount of labor costs.
When we want to build a text classification model, we must first convert unstructured text into text features that can be used by the model. The key to the modelβs effectiveness lies in the way text features are processed. In this event, we will take the example of 30,000 user reviews from 19 banking apps on Google Play.
We will delve into different methods of processing text features, including term frequency matrices, TF-IDF, word2vec, large language models, and fine-tuning large language models, to compare their respective advantages and disadvantages as well as their impact on subsequent model effectiveness. Let us enhance our insights and skills in text classification tasks through this journey together.
Please register at kktix.
εη«ι½ζδΊ€ιε€§εΈ ε·₯η¨δΈι€¨ (Engineering Building 3, NYCU).