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

Instructions, exercises and example data sets for Annif hands-on tutorial

Multilabel classification task rock news articles based on Python

Multi-label classification using LLMs, with additional enhancements using quantization and LoRA (Low-Rank Adaptation). Get better performance on GPU.

This project addresses the Human Value Detection Challenge, where the objective is to classify, given a textual argument and a human value category, classify whether or not the argument draws on that category.

This study aims to investigate the effectiveness of three Transformers (BERT, RoBERTa, XLNet) in handling data sparsity and cold start problems in the recommender system. We present a Transformer-based hybrid recommender system that predicts missing ratings and ex- tracts semantic embeddings from user reviews to mitigate the issues.

Machine Learning in R

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

This ML model is especially designed for Ed-Tech organizations who are confused to categorized their courses according to proper guidance. To solve this major problem, it's here to help you.

A library for multi-class and multi-label text classification

This repository offers a robust solution for multilabel image classification. Utilizing advanced neural networks like VGG16, VGG19, ResNet50, InceptionV3, DenseNet121, and MobileNetV2, the project achieves precise classification across 107 diverse categories.

A library for multi-class and multi-label text classification

A novel, deep-learning software solution for the automatic roofing material classification of the Dutch building stock using state-of-the art deep learning and data fusion techniques.

Multilabel Tagalog Hate Speech Classifier using BERT

Implementation of the MCTS algorithm for Classifier Chains.

The Steel Plates Faults dataset project utilizes machine learning to enhance quality control in steel manufacturing, aiming to develop models for efficient fault detection and classification. This initiative promises to improve productivity and reduce costs, ensuring the delivery of high-quality steel products to meet industry demands.

Source code for EvalNE, a Python library for evaluating Network Embedding methods.

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

This code generate partitions for a multilabel dataset using the Jaccard Index similarity measure. We use HCLUST with 6 linkage metrics to generate several partitions. You may build the partition with the highest coefficient. This code also provide an analysis about the partitioning.

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