Popular GitHub Repositories for Multi Class Classification

基于pytorch_bert的中文多标签分类

multi-label，classifier，text classification，多标签文本分类，文本分类，BERT，ALBERT，multi-label-classification，seq2seq，attention，beam search

The code for the ACL 2023 paper "Linear Classifier: An Often-Forgotten Baseline for Text Classification".

Building a Multi-Label Classifier doesn't seem a difficult task using Keras, but when you are dealing with a highly imbalanced dataset with more than 30 different labels and with multiple losses it can become quite tricky.

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

Package provides java implementation of algorithms in the field of adaptive resonance theory (ART)

Supervised ML- Built a Multi-Class classification model to find the relation between features of a mobile phone(RAM, Internal Memory etc) and its selling price. Model will predict the price range indicating how high the price is.

This repository consists of various deep learning based text classification models. It's more of a plug and play type. Designed in a way that you can customise these projects with little or no efforts on custom datasets.

Computer Vision and Deep Learning

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based method of learning language representations. It is a bidirectional transformer pre-trained model developed using a combination of two tasks namely: masked language modeling objective and next sentence prediction on a large corpus.

Code repository for the paper Xu at SemEval2022 Task 4: pre-BERT Neural Network Methods vs post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection.

NLP assignments about creating a sexism classifier with different approaches.

The objective of this study is to classify customers based on their information about their demographics and financial information into these 3 categories and predict if the customer is going to have good or bad credit score.

Supplementary material for a paper at IAL 2022

Solution for SemEval-2025 Task 9: The Food Hazard Detection Challenge. Utilized machine learning, fine-tuned LLMs (GPT-2, LLaMA, Flan-T5-XL), and ensemble learning to classify food hazards and detect specific products and hazards from unstructured text.