This chapter offers a practical guide to NLP competitions, focusing on PLM-based workflows, data preprocessing, and training strategies for real-world text tasks. It begins by introducing NLP competition fundamentals. NLP enables human-computer communication using natural language and has broad applications, from text generation to traditional tasks. Most NLP competitions still concentrate on classic classification and regression problems, as demonstrated by Kaggle’s 2018–2022 industrial competitions. Key steps for NLP competitions are then outlined. Exploratory data analysis for text uses tools like Matplotlib and WordCloud. Data preprocessing covers spelling correction, text cleaning, and encoding standardization. Data augmentation techniques, including synonym replacement, back-translation, and meta pseudo-labeling, expand labeled data. For modeling, pre-trained language models (PLMs) like BERT are dominant, with Hugging Face’s library crucial. Details of BERT’s architecture and variants such as RoBERTa, DeBERTa, and ALBERT are provided. Model selection tips consider text length, task type, and domain data. Input design for different text tasks uses special tokens, and model heads adapt to output needs. Finally, ensemble learning methods like weighted averaging and stacking boost performance. Training techniques, including dynamic validation and adversarial training, enhance convergence and robustness. Special token usage and in-task masked language modeling are important for complex tasks. Inference optimizations like dynamic padding improve efficiency.