# nlptown/nlp-notebooks

A collection of notebooks for Natural Language Processing from NLP Town

Repository: https://github.com/nlptown/nlp-notebooks
Canonical: https://ross.abutalabs.com/products/nlp-notebooks
Homepage: http://www.nlp.town
Language: Jupyter Notebook
License Family: other
Topics: natural-language-processing, text-mining, deep-learning, artificial-intelligence, nlp, word-embeddings
Last push: 2024-07-16T07:04:02+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3047, "days_push": 778, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1014, forks 386 (observed 2026-08-28T04:03:13.994754+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, tutorials, artificial-intelligence
- platform: python, cross-platform
- tags: jupyter-notebooks, word-embeddings, bert, text-classification, named-entity-recognition, topic-modeling, spacy, pytorch, sentence-similarity, natural-language-processing

## Member repositories
- nlptown/nlp-notebooks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:13.994754+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:11:34.720774+00:00, confidence not recorded.
  - readme: https://github.com/nlptown/nlp-notebooks (fetched 2026-08-28T04:03:13.994754+00:00, sha da1aed3345e5)
  - homepage: http://www.nlp.town (fetched 2026-08-29T13:10:56.831079+00:00, sha fcbd743be2e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.