agnews

[NAACL 2021] This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach'.

[ACL'23 Findings] This is the code repo for our ACL'23 Findings paper "ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval".

[AAAI 2023] This is the code for our paper `Neighborhood-Regularized Self-Training for Learning with Few Labels'.

A project demonstrating the use of Large Language Models (LLMs) for text classification using the RoBERTa model.

Recurrent Capsule Network for Text Classification

Fine-tuning doesn't require a server cluster! This project uses QLoRA to optimize BERT for news classification. By combining 4-bit quantization and LoRA, I slashed VRAM usage by 75% and trained only 2% of parameters. The result? A pro-level classifier in a tiny 20MB adapter file—high performance, minimal hardware.

AG News Text Classification: Classical NLP vs sentence and word embeddings with datasets of size 50, 200, 500, 2000, and 10000

Comparative analysis of classical NLP, sentence embeddings and word embeddings across AG News, DBpedia and IMDb 50K

News Topic Classifier: Fine-tuned BERT model for lightning-fast news categorization

FedSLIP is a federated learning framework for zero-shot personalized parameter efficient fine-tuning using dual-track LoRA, local sparse identity masks, FedProx stabilization, and low communication cost.