tensorflow-nlp-tutorial: a TensorFlow 2 NLP tutorial from preprocessing to LLMs

What the tutorial covers

tensorflow-nlp-tutorial is a repository of natural language processing tutorials built on TensorFlow 2, organized as numbered notebook folders that progress from the basics to modern models. It accompanies a Korean-language book on deep learning for NLP, and the author also maintains a separate PyTorch version for readers who prefer that framework. The curriculum is broad: it starts with text preprocessing, bag-of-words and TF-IDF, vector similarity and classical machine learning, moves through multilayer perceptrons, RNNs, word embeddings and CNN and RNN text classification, then sequence labeling, subword tokenizers and sequence-to-sequence translation, and reaches Transformers, BERT and GPT-2 fine-tuning, BART and T5, the OpenAI API, basic and advanced RAG, LLM and VLM fine-tuning, and topic modeling. The audience is learners who want a hands-on, notebook-driven path through NLP on TensorFlow, particularly Korean-speaking readers following the companion book.

A notebook curriculum from basics to fine-tuning

The structure is the substance: each numbered folder is a stage, and working through them in order builds from foundations to current techniques. Early sections ground the reader in preprocessing and representation, TF-IDF, embeddings, similarity, before neural sequence models, so the classical ideas underpinning modern NLP are covered rather than skipped. The middle sections implement the architectures that define the field, RNNs and CNNs for text, sequence labeling for tasks like named-entity recognition, and Transformers for translation and chatbots. The later sections are notably current for a tutorial, covering BERT and GPT-2 fine-tuning for classification, NER and inference tasks, retrieval-augmented generation at basic and advanced levels, and fine-tuning of large language and vision-language models with techniques the topics list as LoRA, SFT and DPO. That breadth, from TF-IDF to DPO in one repository, is what distinguishes it from a narrow single-topic tutorial.

Working through the notebooks

There is nothing to install as a package, because the repository is a set of Jupyter notebooks. You clone it, open a stage's notebook in Jupyter, and run the cells, which use TensorFlow 2 and the Hugging Face transformers ecosystem for the model-based sections. Because it is learning material rather than a library, the path is to follow the numbered order, running and reading each notebook, and to set up a Python environment with TensorFlow and the libraries a given notebook imports, using a GPU for the heavier fine-tuning sections. The first real use is opening an early notebook, such as text preprocessing, to confirm your TensorFlow environment runs the cells, before progressing to the model sections that download pretrained weights and need more compute. Later notebooks that fine-tune BERT, GPT-2 or LLMs assume familiarity built in the earlier ones.

Where a tutorial repository has limits, including no license

The limitations start with a real one: the repository ships no license file. Without a license, default copyright applies, so despite the notebooks being public you have no granted right to reuse or redistribute them, which matters if you want to adapt the material for teaching or a product, and is worth resolving with the author. Beyond that, it is learning material tied to a companion book, and its primary documentation is Korean, so non-Korean readers rely on the code and the general structure more than the prose. As a fast-moving field's tutorial, individual notebooks can lag current library versions and may need small fixes to run, and the model-based sections require GPU compute and pretrained-model downloads. It teaches by example rather than being a maintained toolkit, so treat it as a course to work through and adapt, not a dependency.

The tutorial versus other NLP learning paths

The alternatives are other ways to learn NLP: the author's own PyTorch version of this tutorial, the Hugging Face course, or framework documentation. The PyTorch version covers similar ground for readers who prefer that framework, so the choice between them is mainly which framework you want to learn in. The Hugging Face course is polished, English-language and centered on the transformers library, strong on modern models but lighter on the classical foundations this tutorial includes. This repository's difference is its span, from TF-IDF and RNNs through to RAG and LLM fine-tuning, on TensorFlow, with a Korean book behind it. Choose the Hugging Face course for a maintained, English, transformer-focused path; choose the PyTorch version if you prefer PyTorch; and choose this repository if you want a TensorFlow-based, foundations-to-LLMs progression, especially alongside its companion book.

No license, and current status

The practical facts are that this is a TensorFlow 2 Jupyter-notebook tutorial spanning classical to modern NLP, accompanying a Korean book, with a parallel PyTorch tutorial by the same author, and it ships no license, which is the main thing to settle before reuse. The last push was on 2026-06-05, so it has been maintained recently, and the presence of current topics such as RAG and LLM and VLM fine-tuning shows the author extending it toward newer techniques rather than leaving it at the classical stages. Work through it if you want a hands-on TensorFlow NLP path and can follow code with Korean documentation, set up a TensorFlow environment and a GPU for the heavier notebooks, start from an early notebook to verify your setup, and clarify the licensing with the author if you intend to adapt the material beyond personal study.

Editorial conclusion

Work through tensorflow-nlp-tutorial if you want a hands-on, notebook-based NLP path on TensorFlow 2 that runs from TF-IDF and RNNs to Transformers, RAG and LLM fine-tuning, and you can follow code alongside Korean documentation. Do not treat it as a reusable library or adapt it beyond personal study without resolving the missing license, since default copyright applies. Clone it, set up a TensorFlow environment with a GPU for the heavier sections, and start from an early preprocessing notebook to confirm your setup before the model chapters.

Frequently asked questions

Official sources

1. Issues
2. README
3. ukairia777/tensorflow-nlp-tutorial on GitHub