Experiments with (German) text classification using state-of-the-art Deep Learning approaches.
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Updated
Feb 3, 2022 - Jupyter Notebook
Experiments with (German) text classification using state-of-the-art Deep Learning approaches.
Solution to sample NLP challenge at Kaggle
2022 HAI 1팀 프로젝트 방언 번역기 개발을 위한 레포지토리입니다.
An unsupervised automated bookkeeping data pipeline that cleans bank SMS text notifications, eliminates "Garbage In, Garbage Out" errors, clusters rows using K-Means, and streams analytics data dashboards to a live Streamlit interface. Created by Srinivasta.
This is a production-ready, end-to-end system developed to detect and classify racist tweets using advanced Natural Language Processing (NLP) techniques. Built on top of BERTweet (vinai/bertweet-base) and fine-tuned with a robust, k-fold cross-validation training pipeline, powered by streamlit UI!
GePart: German Party Classification Model. Student project during the 5th and 6th semester.
Built a scikit-learn ML pipeline to automate IT ticket routing. Cleans raw data errors and accurately classifies text to direct requests to the correct department, resulting in FastAPI deployment.
An end-to-end credibility analysis platform that uses an ensemble of classic Machine Learning (Linear SVM) and Deep Learning (RoBERTa Transformer) to detect fake news in real-time. Features a React dashboard, automatic URL article scraping, model comparison views, and contextual reasoning insights.
NLP Transformer pipeline + LLM QA with RAG - Homeworks for BigData Team NLP course on LLMs
This project focuses on detecting disaster-related tweets using machine learning. The dataset, sourced from Kaggle, contains 7613 tweets labeled as disaster-related or not. Various Natural Language Processing (NLP) techniques and machine learning models were applied to classify these tweets accurately.
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