Multilabel Text Sequence Classification
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Updated
Mar 15, 2023
Multilabel Text Sequence Classification
This is the repository to my bachelor's thesis. The aim was to classify antisemitic comments. A custom dataset was created with the 4chan API and labeled. Different DistilBERT models for sequence-classification were trained and compared. A case study was then conducted.
Saccharomyces cerevisiae information, gene calling and ORF identification assistant
This is a repository with the assignments of IE678 Deep Learning course at University of Mannheim.
music genre classification using 2D CNN, 1D CNN - LSTM and Librosa
Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type) using a support vector-machine model.
In this project we are going to create a network to predict a category for the sequence.
Developed system for the "Climate Activism Stance and Hate Event Detection" Shared Task at CASE 2024
NLP Project - Sentence Classification - Toxicity- Approx 20,000 comments - ranging from 2 to 30 words. Balanced Data Set. 1. Traditional, pre-2010 NLP and ML techniques used. 2. Dense Word Vectors - w2v & Glove, sentence vector created from averaged word vectors, ANN. 3. Glove combined with bi-LSTMs and 2D Convs.
Applied Deep Learning 深度學習之應用 by Vivian Chen 陳縕儂 at NTU CSIE
Encode and decode hand movements using C# and Kinect. Based on K-means and Hidden Markov Models.
Data pipelines for both TensorFlow and PyTorch!
Submission for SemEval 2023 Task 10 EDOS
Predict intent from user queries
Streamlit-based web application designed to assist researchers in validating automated coding results through manual review and comparison. This tool provides an intuitive interface for reviewing coded data, adjusting labels, and analyzing the accuracy of automated coding processes.
A visual debugging tool for fine-tuned BERT models for (multilabel) sequence classification tasks
A useful repository for calculating classification baselines using Bert
Second place solution for McKinsey Analytics Hackathon
This web based application takes a user input and shows the sentiment and polarity scores of the user's input.
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