A guide to the GPT-2 / T5 tutorial in NTHU Prof. Hung-Yu Kao's NLP course (Fall 2025). One task, LCSTS Chinese summarization, is solved twice. Decoder-only GPT-2 is written in native PyTorch: you join article and summary into one sequence, switch to left padding, and set padding labels to −100. Encoder-decoder mT5 uses Seq2SeqTrainer: no left padding needed, and DataCollatorForSeq2Seq handles the −100 for you. Both segment with jieba and score word-level ROUGE.
A guide to the Hugging Face BERT tutorial and HW3 in NTHU Prof. Hung-Yu Kao's NLP course (Fall 2025). The tutorial walks through binary IMDb sentiment classification: AutoTokenizer, the input_ids / token_type_ids / attention_mask fields, AutoModelForSequenceClassification, and Trainer. HW3 applies the same tools to SemEval 2014 Task 1: one bert-base-uncased with two heads, one regressing a 1–5 relatedness score and one classifying entailment into three classes. You add the two losses and write the training loop yourself, because Trainer is not allowed.
The ADL Fall 2025 course page schedules seven TA recitations across ten videos: Colab and PyTorch, ipdb debugging → the life of an NLP project → data formats for four NLP task types → LoRA and QLoRA → scaling laws, the LLaMA architecture, and MoE → quantization and evaluation → deployment with vLLM. The videos were recorded by the TA in 2023 and 2024 and reused in Fall 2025, so the Homework 1 and Homework 3 they mention are those years' assignments. The course page's five slide links all return 404; files with the same names open under the Fall 2024 path, and Deployment has a video only. This guide follows what the TA actually demonstrates and warns about in each session, with examples you can work by hand.
Hugging Face Hub is a collaboration layer for versioned models, datasets, and applications. Datasets handles data, Spaces runs demos, while Inference Providers and Endpoints provide managed inference.