HarderThenHarder/transformers_tasks resource

Health v2 · maintenance only

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2420 stars · 398 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook-based NLP algorithm implementations built on the Hugging Face transformers library, covering text classification, text matching, information extraction, prompt tasks, text generation, and RLHF/SFT. It serves as a learning resource and starter codebase where users swap in their own datasets to train task-specific models.

Use cases

• learn how to fine-tune BERT for text classification
• train a sentence similarity model for search recall
• implement RLHF with a reward model and PPO on GPT2
• do named entity recognition with UIE
• try prompt-based few-shot learning like PET and p-tuning
• fine-tune T5 for Chinese question answering
• learn SFT and LLM alignment from example code

When to choose

• you want readable, task-by-task example code for transformers-based NLP
• you are learning RLHF, SFT, or prompt tuning with small runnable examples
• you need reference implementations of text matching architectures like DSSM and Sentence-BERT

When to avoid

• you need a production-ready NLP library with APIs and packaging
• you require a maintained project with a license and active releases
• you need scalable training infrastructure rather than educational scripts

Facets

learning-resource · maturity maintenance

nlp machine-learning llm-training rag prompt-engineering machine-learning large-language-models tutorials python windows transformers huggingface text-classification text-matching information-extraction rlhf sft jupyter-notebooks chinese-nlp example-code natural-language-processing linux macos gpu

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