NLP_pytorch_project

Why we included this project

This repository collects PyTorch implementations of most classic NLP tasks, from word embeddings and text classification to machine translation, named entity recognition, reading comprehension, and text generation. Each task folder holds several competing approaches side by side, so you can compare multiple BERT-style variants for classification, different CRF-based NER setups, and MRC models ranging from BiDAF to QANet. The author has also split the work into focused follow-up repositories for text classification, semantic similarity, and text generation, which helps when you already know which model you want to study. It reads like a well-organized study notebook rather than a production library, so treat it as a learning and reference resource for comparing model architectures and training recipes.