nlp-tutorial
A set of PyTorch tutorials that implement classic NLP models in short code, from word embeddings to BERT.
- Stars
- 14.9k
- Forks
- 3.9k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 2.7 years ago
- Type
- Learning resource
No commits in over a year — check for forks or alternatives before adopting.
What it is
nlp-tutorial is a collection of Jupyter notebooks that walk through well-known natural language processing models, such as NNLM, Word2Vec, TextCNN, LSTM, Seq2Seq with attention, the Transformer, and BERT. Each entry links to the original research paper and a Colab notebook you can run in the browser. The README says most models are implemented in under 100 lines of code, which is meant to make the ideas easy to read.
- Run the Word2Vec notebook to train word embeddings and plot them as a graph
- Train a small TextCNN model for binary sentiment classification
- Follow the Seq2Seq with Attention notebook to build a simple translation model
- Step through the Transformer notebook to see how attention is implemented
Great for
- Students who want to see how classic NLP models work in code
- Researchers who want a short PyTorch reference implementation next to the paper
- Developers moving from basic machine learning into language models
- People who prefer learning by running notebooks
Not ideal for
- Anyone who needs a production-ready NLP library, since the README frames this as an example-focused tutorial
- People who need TensorFlow code, because the README says old TensorFlow v1 code is archived and only PyTorch 1.0+ is supported
- Readers wanting the latest large language model techniques, because the last push was in February 2024 and the curriculum covers older architectures
How to get it
- 1
Clone and build from source
You'll need: Python 3.5+ · PyTorch 1.0.0+
Clone the repository
git clone https://github.com/graykode/nlp-tutorial - 2
Run it
- Clone the repository or open a notebook directly in Google Colab from the README links
- Pick a lesson from the curriculum, such as 1-2.Word2Vec
- Open the notebook and run the cells in order
- Read the linked paper alongside the code to compare the two
- 3
Get the most out of it
- Start with the Basic Embedding Model section before moving to attention and Transformer lessons, since later lessons build on these ideas
- Use the Colab links to run lessons without installing anything locally
- Compare Transformer and Transformer(Greedy_decoder) notebooks to see two decoding approaches
- Read the original papers linked in each section for the full theory
Downloads
Strengths
- Each model is paired with its original paper, so the code can be checked against the source
- Notebooks run in Google Colab, so no local setup is required for many lessons
- Short implementations make the core ideas easier to follow
Trade-offs
- The README does not describe a formal course structure or explanatory text beyond the notebook titles
- Repository has no releases and the last push was 2024-02-21, so it may not reflect newer frameworks or models
- The tutorials are examples, not optimized or tested for real-world deployment
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| nlp-tutorial (this) | 14.9k | Learning resource | intermediate | 2.7 years ago | Students who want to see how classic NLP models work in code |
| stanford-tensorflow-tutorials | 10.4k | Learning resource | intermediate | 5.9 years ago | Students following the original CS 20 course materials |
| annotated_deep_learning_paper_implementations | 67.6k | Learning resource | intermediate | 4 days ago | Students learning how transformers, GANs, or optimizers work internally |
| External-Attention-pytorch | 12.2k | Library | beginner | 8 months ago | Students reading deep learning papers who want to see the core code of a module |
| nano-vllm | 16.3k | Library | intermediate | 6 months ago | Developers who want to understand how LLM inference engines work internally |
| Made-With-ML | 49.9k | Learning resource | intermediate | 8 months ago | Software engineers who want to learn how ML models get deployed and maintained |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.