# yandexdataschool/nlp_course

YSDA course in Natural Language Processing

Repository: https://github.com/yandexdataschool/nlp_course
Canonical: https://ross.abutalabs.com/products/nlp_course
Homepage: https://lena-voita.github.io/nlp_course.html
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-08-24T10:40:57+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 2916, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10656, forks 2760 (observed 2026-08-28T04:10:43.687745+00:00)

## What it is
The Yandex School of Data Analysis (YSDA) Natural Language Processing course, with lecture materials, interactive blog-style content, seminar notebooks, and homework assignments. It covers topics from word embeddings and language modeling to transformers, transfer learning, and large language models.

## Use cases
- learn natural language processing from scratch
- study how transformers and attention work
- find hands-on NLP homework notebooks
- self-study large language models and prompting
- recap seq2seq and machine translation concepts
- prepare for an NLP research career

## When to choose
- you want a structured, university-quality NLP curriculum with exercises
- you prefer interactive explanations with visualizations
- you want practical PyTorch/TensorFlow notebooks alongside theory

## When to avoid
- you need production NLP tooling or a library
- you want a quick reference rather than a full course
- you need certified or instructor-graded learning

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, machine-learning, deep-learning
- domain: education, tutorials, large-language-models
- platform: python, cross-platform
- tags: course, jupyter-notebooks, ysda, transformers, embeddings, seq2seq, prompting, self-study, natural-language-processing

## Member repositories
- yandexdataschool/nlp_course (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:43.687745+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 2026-08-29T17:18:12.826928+00:00, confidence not recorded.
  - readme: https://github.com/yandexdataschool/nlp_course (fetched 2026-08-28T04:10:43.687745+00:00, sha 896c1f5d62d3)
  - homepage: https://lena-voita.github.io/nlp_course.html (fetched 2026-08-29T08:17:19.597179+00:00, sha 8e12a9c574e5)
- Data as of 2026-08-30T08:39:29.467469+00:00.