nlptown/nlp-notebooks resource

Health v2 · maintenance only

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1014 stars · 386 forks observed · 2026-08-28

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

A collection of Jupyter notebooks covering practical Natural Language Processing topics, from word embeddings and topic modeling to BERT-based text classification and named entity recognition. It is published by NLP Town as an educational resource accompanying their NLP consultancy and workshops.

Use cases

• learn word embeddings for NLP
• train a BERT text classifier in PyTorch
• build a named entity recognition model with spaCy or CRFs
• do topic modeling with LDA on a text corpus
• compute sentence similarity with embeddings
• learn multilingual and cross-lingual transfer learning
• try zero-shot text classification
• fine-tune transformers for sequence labelling

When to choose

• you want hands-on, runnable notebooks to learn practical NLP techniques
• you need worked examples spanning classical ML (scikit-learn, LDA) and deep learning (BERT, BiLSTM) approaches
• you are preparing NLP training material or self-studying text mining

When to avoid

• you need a production-ready NLP library rather than tutorial notebooks
• you expect maintained code with a license, tests, or releases
• you need up-to-date coverage of modern LLM tooling, as content skews to pre-LLM-era techniques

Facets

learning-resource · maturity maintenance

nlp machine-learning deep-learning machine-learning tutorials artificial-intelligence python cross-platform jupyter-notebooks word-embeddings bert text-classification named-entity-recognition topic-modeling spacy pytorch sentence-similarity natural-language-processing

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markdown · JSON · MCP: product_card(name="nlptown/nlp-notebooks")