Popular GitHub Repositories for Multilabel

Simple Image Heads and Layers for deep learning based computer vision

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)

The code of CIKM'19 paper《Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach》

Annif is a multi-algorithm automated subject indexing tool for libraries, archives and museums.

A bidirectional LSTM with attention for multiclass/multilabel text classification.

A PyTorch-based toolkit for natural language processing

Unsupervised multilabel image segmentation (color/gray/multichannel) based on the Potts model (aka piecewise constant Mumford-Shah model)

Multi-Label Image Classification of Chest X-Rays In Pytorch

[CVPR 2023] Official code repository for "How you feelin'? Learning Emotions and Mental States in Movie Scenes". https://arxiv.org/abs/2304.05634

This is a reimplementation of AG-CNN. ("Thorax Disease Classification with Attention Guided Convolutional Neural Network","Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification")

Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis

? A model for early detection of multiple faults in induction motors based on the use of PCA and multilabel decision-trees

Word2Vec encodings based search engine for Stackoverflow questions

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules

This GitHub repository provides an implementation of the paper "MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network" . MAGNET is a state-of-the-art approach for multi-label text classification, leveraging the power of graph neural networks (GNNs) and attention mechanisms.

This repo contains implementation of advanced ML techniques. Includes model ensembles, cost-sensitive learning and dealing with class imbalance.

The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BertForSequenceClassification model. Also tried RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model.

A pytorch implementation of mpvae.

Multilabel Convolutional Neural Network model to analyze and extract sentiments in texts in Brazilian Portuguese language.

A research tool for exploring and annotating large datasets with Active Learning