transformers_tasks

Why we included this project

This repo is a code-first collection of NLP task implementations built on the Hugging Face transformers library. Instead of explaining algorithms in the abstract, it gives you runnable training scripts for text classification, generation, matching, named entity recognition, and relation extraction, with approaches like DSSM, Sentence-BERT, SimCSE, UIE, PET, and p-tuning wired up and ready to point at your own data through shell scripts and notebooks. It also covers the alignment side of the stack, including reward-model training and PPO for RLHF, ChatGLM fine-tuning, and zero-shot prompting, which helps when you move past single tasks into more advanced work. The task-based layout with clear entry points makes it a reasonable learning path for engineers new to transformers and a starting skeleton for experiments. The main caveat is that it is a work in progress, so treat each module as a baseline to verify and extend rather than a finished product.