Guide · Open source
How to use nlp-tutorial: clone, run notebooks and learn NLP models
Updated · written with AI from the project's own documentation and checked against our catalogue data
nlp-tutorial is an open-source collection of Jupyter notebooks by graykode that walks through classic natural language processing models in PyTorch, from NNLM and Word2Vec to the Transformer and BERT. It is aimed at students, researchers and developers who learn better by reading and running short code. By the end of this guide you will know how to get the repository, where each model lives, and how to run an example in your browser or locally.
nlp-tutorialgraykode/nlp-tutorial · ★ 14.9k · MIT Install options & downloads Quick start
- 1Check prerequisites. Confirm you have Python 3.5 or newer and PyTorch 1.0.0 or newer installed.
- 2Clone the repository. Run git clone https://github.com/graykode/nlp-tutorial in your terminal.
- 3Pick a model. Choose a model from the curriculum, such as NNLM for a first look at language models.
- 4Read the paper. Open the paper link listed for that model to see the original idea.
- 5Run the notebook. Open the Colab link in your browser, or open the matching notebook in the cloned folder locally.
- 6Move up the curriculum. Continue to sequence models, attention and then the Transformer and BERT.
What is nlp-tutorial and who is it for?
nlp-tutorial is a tutorial for people studying natural language processing (NLP) with PyTorch. The README says most of its models were implemented in fewer than 100 lines of code, not counting comments or blank lines, so the ideas are easy to read side by side with the original papers.
It is an example-focused resource, not a production library. The README also notes that old TensorFlow v1 code is archived and only PyTorch 1.0 or higher is supported.
- Students who want to see how classic NLP models work in code
- Researchers who want a short PyTorch reference next to the paper
- Developers moving from basic machine learning into language models
- People who prefer learning by running notebooks
Install nlp-tutorial by cloning the repository
The project uses a clone-and-build approach. Clone the repository with git, then open the folder for the model you want to study. The source lists Python 3.5+ and PyTorch 1.0.0+ as dependencies.
- Prerequisites: Python 3.5+ and PyTorch 1.0.0+
- Clone the repository from GitHub before running any notebook locally
git clone https://github.com/graykode/nlp-tutorialExplore the curriculum by model type
The curriculum is grouped into five parts: basic embedding models, CNN, RNN, attention mechanisms, and Transformer-based models. Each entry names the model, a one-line example task, and links to the original paper and a Colab notebook.
Start with NNLM or Word2Vec if you are new to embeddings. Move to TextRNN and TextLSTM for sequence models, then Seq2Seq with Attention, the Transformer and BERT for the attention-based architectures.
- 1. Basic Embedding Model: NNLM (predict next word), Word2Vec Skip-gram (embeddings and graph), FastText (sentence classification)
- 2. CNN: TextCNN (binary sentiment classification)
- 3. RNN: TextRNN (predict next step), TextLSTM (autocomplete), Bi-LSTM (predict next word in long sentence)
- 4. Attention Mechanism: Seq2Seq (change word), Seq2Seq with Attention (translate), Bi-LSTM with Attention (binary sentiment classification)
- 5. Transformer: The Transformer (translate), BERT (classify next sentence and predict masked tokens)
Run a notebook in Google Colab or locally
Each model that has a Colab link can be opened in the browser without local setup. Use the Colab link for the model you want, which opens the matching .ipynb file. Some entries list only a Colab link and no paper, so check the entry before you start.
To run locally, open the model folder from your clone, such as 1-1.NNLM, and open its notebook in Jupyter. Make sure your environment has Python 3.5+ and PyTorch 1.0.0+ first.
- Open the Colab link for the model to run it in the browser
- Clone the repo and open the matching folder for local runs
Read the paper alongside the code
Each entry links to the original research paper, with the publication year in the curriculum list. Reading the short code next to the paper is the main way the project is meant to be used.
Pick one model, read its paper's core idea, then read the matching notebook and trace how each part of the paper appears in the code.
What people use it for
- Learning how Word2Vec skip-gram embeddings are trained in a short PyTorch script
- Comparing a TextRNN or TextLSTM with the Seq2Seq model for simple sequence tasks
- Studying how the Transformer and BERT are implemented next to their papers
- Running a small sentiment classification example with TextCNN or Bi-LSTM with Attention
Frequently asked questions
Is nlp-tutorial free to use?
The project is released under the MIT licence, according to the repository information in the source. You can read, clone and adapt the code under those terms.
How do I install nlp-tutorial?
There is no package installer described in the source. Clone the repository with git clone https://github.com/graykode/nlp-tutorial, then open the notebooks in the model folders. You need Python 3.5+ and PyTorch 1.0.0+.
Does nlp-tutorial support TensorFlow?
No. The README says old TensorFlow v1 code is archived in the archive folder, and only PyTorch 1.0 or higher is supported for the current code.
Can I run nlp-tutorial without installing anything?
Yes, for models that have a Colab link. Each entry links to a Colab notebook that you can open and run in the browser.
Is nlp-tutorial good for production NLP work?
No. The README frames it as an example-focused tutorial, not a production-ready NLP library. For production use, a project like Hugging Face Transformers is a better fit.
How does nlp-tutorial compare with The Annotated Transformer or Hugging Face Transformers?
nlp-tutorial covers a wide range of classic models in short PyTorch notebooks, with each linked to its paper. The Annotated Transformer focuses on one model in depth, and Hugging Face Transformers is a library for using pretrained models in real applications.
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Our verdict
nlp-tutorial suits students, researchers and developers who want short PyTorch notebooks that match classic NLP papers. It is not the right choice if you need a production library, TensorFlow code, or coverage of the latest large language model techniques, since the curriculum centres on older architectures.
Install options & downloads